Last updated on 2024-11-08 07:50:26 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.3.0 | 10.27 | 323.08 | 333.35 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.3.0 | 6.47 | 254.59 | 261.06 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.3.0 | 502.64 | OK | |||
| r-devel-linux-x86_64-fedora-gcc | 1.3.0 | 304.11 | ERROR | |||
| r-devel-windows-x86_64 | 1.3.0 | 10.00 | 244.00 | 254.00 | OK | |
| r-patched-linux-x86_64 | 1.3.0 | 12.45 | 347.69 | 360.14 | OK | |
| r-release-linux-x86_64 | 1.3.0 | 9.97 | 353.18 | 363.15 | OK | |
| r-release-macos-arm64 | 1.3.0 | 56.00 | ERROR | |||
| r-release-macos-x86_64 | 1.3.0 | 89.00 | ERROR | |||
| r-release-windows-x86_64 | 1.3.0 | 12.00 | 260.00 | 272.00 | OK | |
| r-oldrel-macos-arm64 | 1.3.0 | 144.00 | OK | |||
| r-oldrel-macos-x86_64 | 1.3.0 | 448.00 | OK | |||
| r-oldrel-windows-x86_64 | 1.3.0 | 14.00 | 310.00 | 324.00 | OK |
Version: 1.3.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [173s/244s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(rbmi)
>
> test_check("rbmi")
mmrm() registered as emmeans extension
[ FAIL 4 | WARN 0 | SKIP 13 | PASS 1243 ]
══ Skipped tests (13) ══════════════════════════════════════════════════════════
• On CRAN (1): 'test-mmrm.R:337:5'
• is_full_test() is not TRUE (12): 'test-draws.R:115:5',
'test-fullusage.R:43:5', 'test-fullusage.R:117:5', 'test-fullusage.R:209:5',
'test-fullusage.R:300:5', 'test-fullusage.R:431:5', 'test-fullusage.R:520:5',
'test-fullusage.R:673:5', 'test-mcmc.R:327:5', 'test-mcmc.R:502:5',
'test-parallel.R:8:5', 'test-reproducibility.R:9:5'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-draws.R:144:9'): bayes ─────────────────────────────────────────
Error in `fit_mcmc(designmat = model_df_scaled[, -1, drop = FALSE], outcome = model_df_scaled[,
1, drop = TRUE], group = data2[[vars$group]], visit = data2[[vars$visit]],
subjid = data2[[vars$subjid]], method = method, quiet = quiet)`: unable to load shared object '/tmp/RtmppwuNuP/working_dir/RtmpZh6Js7/file805651f947753.so':
libunwind.so.1: cannot open shared object file: No such file or directory
Backtrace:
▆
1. ├─base::suppressWarnings(...) at test-draws.R:143:5
2. │ └─base::withCallingHandlers(...)
3. ├─rbmi::draws(d$dat, d$dat_ice, d$vars, meth, quiet = TRUE) at test-draws.R:144:9
4. └─rbmi:::draws.bayes(d$dat, d$dat_ice, d$vars, meth, quiet = TRUE)
5. └─rbmi:::fit_mcmc(...)
── Error ('test-mcmc.R:108:13'): Verbose suppression works ─────────────────────
Error in `fit_mcmc(designmat = model_df_scaled[, -1, drop = FALSE], outcome = model_df_scaled[,
1, drop = TRUE], group = data2[[vars$group]], visit = data2[[vars$visit]],
subjid = data2[[vars$subjid]], method = method, quiet = quiet)`: unable to load shared object '/tmp/RtmppwuNuP/working_dir/RtmpZh6Js7/file80565462dd527.so':
libunwind.so.1: cannot open shared object file: No such file or directory
Backtrace:
▆
1. ├─base::suppressWarnings(...) at test-mcmc.R:106:5
2. │ └─base::withCallingHandlers(...)
3. ├─utils::capture.output(...) at test-mcmc.R:107:9
4. │ └─base::withVisible(...elt(i))
5. ├─rbmi::draws(...) at test-mcmc.R:108:13
6. └─rbmi:::draws.bayes(...)
7. └─rbmi:::fit_mcmc(...)
── Error ('test-print.R:102:9'): print - bayesian ──────────────────────────────
Error in `fit_mcmc(designmat = model_df_scaled[, -1, drop = FALSE], outcome = model_df_scaled[,
1, drop = TRUE], group = data2[[vars$group]], visit = data2[[vars$visit]],
subjid = data2[[vars$subjid]], method = method, quiet = quiet)`: unable to load shared object '/tmp/RtmppwuNuP/working_dir/RtmpZh6Js7/file8056572bab751.so':
libunwind.so.1: cannot open shared object file: No such file or directory
Backtrace:
▆
1. ├─base::suppressWarnings(...) at test-print.R:101:5
2. │ └─base::withCallingHandlers(...)
3. ├─rbmi::draws(...) at test-print.R:102:9
4. └─rbmi:::draws.bayes(...)
5. └─rbmi:::fit_mcmc(...)
── Error ('test-reproducibility.R:121:9'): bayes - set.seed produces identical results ──
Error in `fit_mcmc(designmat = model_df_scaled[, -1, drop = FALSE], outcome = model_df_scaled[,
1, drop = TRUE], group = data2[[vars$group]], visit = data2[[vars$visit]],
subjid = data2[[vars$subjid]], method = method, quiet = quiet)`: unable to load shared object '/tmp/RtmppwuNuP/working_dir/RtmpZh6Js7/file805652b803221.so':
libunwind.so.1: cannot open shared object file: No such file or directory
Backtrace:
▆
1. ├─base::suppressWarnings(...) at test-reproducibility.R:120:5
2. │ └─base::withCallingHandlers(...)
3. ├─rbmi::draws(dat, dat_ice, vars, meth, quiet = TRUE) at test-reproducibility.R:121:9
4. └─rbmi:::draws.bayes(dat, dat_ice, vars, meth, quiet = TRUE)
5. └─rbmi:::fit_mcmc(...)
[ FAIL 4 | WARN 0 | SKIP 13 | PASS 1243 ]
Error: Test failures
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc
Version: 1.3.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [6s/10s]
Running the tests in ‘tests/testthat.R’ failed.
Last 13 lines of output:
43: tryCatch(withCallingHandlers({ eval(code, test_env) if (!handled && !is.null(test)) { skip_empty() }}, expectation = handle_expectation, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error), error = handle_fatal, skip = function(e) { })
44: test_code(test = NULL, code = exprs, env = env, default_reporter = StopReporter$new())
45: source_file(path, env = env(env), desc = desc, error_call = error_call)
46: FUN(X[[i]], ...)
47: lapply(test_paths, test_one_file, env = env, desc = desc, error_call = error_call)
48: doTryCatch(return(expr), name, parentenv, handler)
49: tryCatchOne(expr, names, parentenv, handlers[[1L]])
50: tryCatchList(expr, classes, parentenv, handlers)
51: tryCatch(code, testthat_abort_reporter = function(cnd) { cat(conditionMessage(cnd), "\n") NULL})
52: with_reporter(reporters$multi, lapply(test_paths, test_one_file, env = env, desc = desc, error_call = error_call))
53: test_files_serial(test_dir = test_dir, test_package = test_package, test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, desc = desc, load_package = load_package, error_call = error_call)
54: test_files(test_dir = path, test_paths = test_paths, test_package = package, reporter = reporter, load_helpers = load_helpers, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, load_package = load_package, parallel = parallel)
55: test_dir("testthat", package = package, reporter = reporter, ..., load_package = "installed")
56: test_check("rbmi")
An irrecoverable exception occurred. R is aborting now ...
Flavor: r-release-macos-arm64
Version: 1.3.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [10s/14s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(rbmi)
>
> test_check("rbmi")
*** caught segfault ***
address 0x0, cause 'memory not mapped'
Traceback:
1: Module(module, mustStart = TRUE)
2: .getModulePointer(x)
3: new("Module", .xData = <environment>)$stan_fit4modelaa9c6a27673f_rbmi_mmrm
4: new("Module", .xData = <environment>)$stan_fit4modelaa9c6a27673f_rbmi_mmrm
5: eval(call("$", mod, paste("stan_fit4", model_cppname, sep = "")))
6: eval(call("$", mod, paste("stan_fit4", model_cppname, sep = "")))
7: object@mk_cppmodule(object)
8: .local(object, ...)
9: (new("nonstandardGenericFunction", .Data = function (object, ...) { standardGeneric("sampling")}, generic = "sampling", package = "rstan", group = list(), valueClass = character(0), signature = "object", default = NULL, skeleton = (function (object, ...) stop(gettextf("invalid call in method dispatch to '%s' (no default method)", "sampling"), domain = NA))(object, ...)))(object = new("stanmodel", model_name = "rbmi_mmrm", model_code = "functions {\n int integer_division(int a, int b) {\n // perform a/b ensuring return value is also an int\n int i = 0;\n while(b*(i+1) <= a) {\n i = i + 1;\n }\n return(i);\n }\n array[] vector to_vector_of_arrays(vector vec, int length_array) {\n // treansform a vector into a vector of arrays. Example: vec = [1,2,3,4,5,6] and\n // length_array = 2, then output = [1,2; 3,4; 5,6]\n array[integer_division(num_elements(vec),length_array)] vector[length_array] res;\n int j = 1;\n int i = 1;\n while(j <= num_elements(vec)) {\n res[i,] = vec[j:(j+length_array-1)];\n i = i+1;\n j = j + length_array;\n }\n return(res);\n }\n}\ndata {\n int<lower=1> N; // number of observations\n int<lower=1> P; // number of covariates (number of columns of design matrix)\n int<lower=1> G; // number of Sigma Groups\n int<lower=1> n_visit; // number of visits\n int<lower=1> n_pat; // number of pat groups (# missingness patterns * groups)\n array[n_pat] int<lower=1> pat_G; // Index for which Sigma the pat group should use\n array[n_pat] int<lower=1> pat_n_pt; // number of patients in each pat group\n array[n_pat] int<lower=1> pat_n_visit; // number of non-missing visits in each pat group\n array[n_pat, n_visit] int<lower=1> pat_sigma_index; // rows/cols from sigma to subset on for the pat group\n vector[N] y; // outcome variable\n matrix[N,P] Q; // design matrix (After QR decomp)\n matrix[P,P] R; // R matrix (from QR decomp)\n array[G] matrix[n_visit, n_visit] Sigma_init; // covariance matrix estimated from MMRM\n}\ntransformed data {\n matrix[P, P] R_inverse = inverse(R);\n}\nparameters {\n vector[P] theta; // coefficients of linear model on covariates\n array[G] cov_matrix[n_visit] Sigma; // covariance matrix(s)\n}\nmodel {\n int data_start_row = 1;\n vector[N] mu = Q * theta;\n for(g in 1:G){\n Sigma[g] ~ inv_wishart(n_visit+2, Sigma_init[g]);\n }\n for(i in 1:n_pat) {\n // Index + size variables for current pat group\n int nvis = pat_n_visit[i]; // number of visits\n int npt = pat_n_pt[i]; // number of patients\n int g = pat_G[i]; // Sigma index\n // Get required/reduced Sigma for current pat group\n array[nvis] int sig_index = pat_sigma_index[i, 1:nvis];\n matrix[nvis,nvis] sig = Sigma[g][sig_index, sig_index];\n // Derive data indcies for current pat group\n int data_stop_row = data_start_row + ((nvis * npt) -1);\n // Extract required data for the current pat group\n array[npt] vector[nvis] y_obs = to_vector_of_arrays(y[data_start_row:data_stop_row], nvis);\n array[npt] vector[nvis] mu_obs = to_vector_of_arrays(mu[data_start_row:data_stop_row], nvis);\n y_obs ~ multi_normal(mu_obs, sig);\n // Update data index for next pat group\n data_start_row = data_stop_row + 1;\n }\n}\ngenerated quantities {\n vector[P] beta = R_inverse * theta;\n}", model_cpp = list(model_cppname = "modelaa9c6a27673f_rbmi_mmrm", model_cppcode = "#ifndef MODELS_HPP\n#define MODELS_HPP\n#define STAN__SERVICES__COMMAND_HPP\n#include <rstan/rstaninc.hpp>\n#ifndef USE_STANC3\n#define USE_STANC3\n#endif\n// Code generated by stanc v2.32.2\n#include <stan/model/model_header.hpp>\nnamespace modelaa9c6a27673f_rbmi_mmrm_namespace {\nusing stan::model::model_base_crtp;\nusing namespace stan::math;\nstan::math::profile_map profiles__;\nstatic constexpr std::array<const char*, 80> locations_array__ =\n {\" (found before start of program)\",\n \" (in 'rbmi_mmrm', line 43, column 4 to column 20)\",\n \" (in 'rbmi_mmrm', line 44, column 4 to column 39)\",\n \" (in 'rbmi_mmrm', line 71, column 3 to column 38)\",\n \" (in 'rbmi_mmrm', line 47, column 4 to column 27)\",\n \" (in 'rbmi_mmrm', line 48, column 11 to column 12)\",\n \" (in 'rbmi_mmrm', line 48, column 4 to column 29)\",\n \" (in 'rbmi_mmrm', line 50, column 8 to column 57)\",\n \" (in 'rbmi_mmrm', line 49, column 17 to line 51, column 5)\",\n \" (in 'rbmi_mmrm', line 49, column 4 to line 51, column 5)\",\n \" (in 'rbmi_mmrm', line 54, column 8 to column 34)\",\n \" (in 'rbmi_mmrm', line 55, column 8 to column 30)\",\n \" (in 'rbmi_mmrm', line 56, column 8 to column 25)\",\n \" (in 'rbmi_mmrm', line 58, column 14 to column 18)\",\n \" (in 'rbmi_mmrm', line 58, column 8 to column 63)\",\n \" (in 'rbmi_mmrm', line 59, column 15 to column 19)\",\n \" (in 'rbmi_mmrm', line 59, column 20 to column 24)\",\n \" (in 'rbmi_mmrm', line 59, column 8 to column 63)\",\n \" (in 'rbmi_mmrm', line 61, column 8 to column 63)\",\n \" (in 'rbmi_mmrm', line 63, column 14 to column 17)\",\n \" (in 'rbmi_mmrm', line 63, column 26 to column 30)\",\n \" (in 'rbmi_mmrm', line 63, column 8 to column 99)\",\n \" (in 'rbmi_mmrm', line 64, column 14 to column 17)\",\n \" (in 'rbmi_mmrm', line 64, column 26 to column 30)\",\n \" (in 'rbmi_mmrm', line 64, column 8 to column 101)\",\n \" (in 'rbmi_mmrm', line 65, column 8 to column 42)\",\n \" (in 'rbmi_mmrm', line 67, column 8 to column 43)\",\n \" (in 'rbmi_mmrm', line 52, column 22 to line 68, column 5)\",\n \" (in 'rbmi_mmrm', line 52, column 4 to line 68, column 5)\",\n \" (in 'rbmi_mmrm', line 25, column 4 to column 19)\",\n \" (in 'rbmi_mmrm', line 26, column 4 to column 19)\",\n \" (in 'rbmi_mmrm', line 27, column 4 to column 19)\",\n \" (in 'rbmi_mmrm', line 28, column 4 to column 25)\",\n \" (in 'rbmi_mmrm', line 29, column 4 to column 23)\",\n \" (in 'rbmi_mmrm', line 30, column 10 to column 15)\",\n \" (in 'rbmi_mmrm', line 30, column 4 to column 36)\",\n \" (in 'rbmi_mmrm', line 31, column 10 to column 15)\",\n \" (in 'rbmi_mmrm', line 31, column 4 to column 39)\",\n \" (in 'rbmi_mmrm', line 32, column 10 to column 15)\",\n \" (in 'rbmi_mmrm', line 32, column 4 to column 42)\",\n \" (in 'rbmi_mmrm', line 33, column 10 to column 15)\",\n \" (in 'rbmi_mmrm', line 33, column 17 to column 24)\",\n \" (in 'rbmi_mmrm', line 33, column 4 to column 55)\",\n \" (in 'rbmi_mmrm', line 34, column 11 to column 12)\",\n \" (in 'rbmi_mmrm', line 34, column 4 to column 16)\",\n \" (in 'rbmi_mmrm', line 35, column 11 to column 12)\",\n \" (in 'rbmi_mmrm', line 35, column 13 to column 14)\",\n \" (in 'rbmi_mmrm', line 35, column 4 to column 18)\",\n \" (in 'rbmi_mmrm', line 36, column 11 to column 12)\",\n \" (in 'rbmi_mmrm', line 36, column 13 to column 14)\",\n \" (in 'rbmi_mmrm', line 36, column 4 to column 18)\",\n \" (in 'rbmi_mmrm', line 37, column 10 to column 11)\",\n \" (in 'rbmi_mmrm', line 37, column 20 to column 27)\",\n \" (in 'rbmi_mmrm', line 37, column 29 to column 36)\",\n \" (in 'rbmi_mmrm', line 37, column 4 to column 49)\",\n \" (in 'rbmi_mmrm', line 40, column 10 to column 11)\",\n \" (in 'rbmi_mmrm', line 40, column 13 to column 14)\",\n \" (in 'rbmi_mmrm', line 40, column 3 to column 39)\",\n \" (in 'rbmi_mmrm', line 43, column 11 to column 12)\",\n \" (in 'rbmi_mmrm', line 44, column 10 to column 11)\",\n \" (in 'rbmi_mmrm', line 44, column 24 to column 31)\",\n \" (in 'rbmi_mmrm', line 71, column 10 to column 11)\",\n \" (in 'rbmi_mmrm', line 4, column 8 to column 18)\",\n \" (in 'rbmi_mmrm', line 6, column 12 to column 22)\",\n \" (in 'rbmi_mmrm', line 5, column 28 to line 7, column 9)\",\n \" (in 'rbmi_mmrm', line 5, column 8 to line 7, column 9)\",\n \" (in 'rbmi_mmrm', line 8, column 8 to column 18)\",\n \" (in 'rbmi_mmrm', line 2, column 39 to line 9, column 5)\",\n \" (in 'rbmi_mmrm', line 13, column 14 to column 62)\",\n \" (in 'rbmi_mmrm', line 13, column 71 to column 83)\",\n \" (in 'rbmi_mmrm', line 13, column 8 to column 89)\",\n \" (in 'rbmi_mmrm', line 14, column 8 to column 18)\",\n \" (in 'rbmi_mmrm', line 15, column 8 to column 18)\",\n \" (in 'rbmi_mmrm', line 17, column 12 to column 48)\",\n \" (in 'rbmi_mmrm', line 18, column 12 to column 20)\",\n \" (in 'rbmi_mmrm', line 19, column 12 to column 33)\",\n \" (in 'rbmi_mmrm', line 16, column 38 to line 20, column 9)\",\n \" (in 'rbmi_mmrm', line 16, column 8 to line 20, column 9)\",\n \" (in 'rbmi_mmrm', line 21, column 8 to column 20)\",\n \" (in 'rbmi_mmrm', line 10, column 69 to line 22, column 5)\"};\nint integer_division(const int& a, const int& b, std::ostream* pstream__);\ntemplate <typename T0__,\n stan::require_all_t<stan::is_col_vector<T0__>,\n stan::is_vt_not_complex<T0__>>* = nullptr>\nstd::vector<\n Eigen::Matrix<stan::promote_args_t<stan::base_type_t<T0__>>,-1,1>>\nto_vector_of_arrays(const T0__& vec_arg__, const int& length_array,\n std::ostream* pstream__);\nint integer_division(const int& a, const int& b, std::ostream* pstream__) {\n using local_scalar_t__ = double;\n int current_statement__ = 0;\n static constexpr bool propto__ = true;\n // suppress unused var warning\n (void) propto__;\n local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());\n // suppress unused var warning\n (void) DUMMY_VAR__;\n try {\n int i = std::numeric_limits<int>::min();\n current_statement__ = 62;\n i = 0;\n current_statement__ = 65;\n while (stan::math::logical_lte((b * (i + 1)), a)) {\n current_statement__ = 63;\n i = (i + 1);\n }\n current_statement__ = 66;\n return i;\n } catch (const std::exception& e) {\n stan::lang::rethrow_located(e, locations_array__[current_statement__]);\n }\n}\ntemplate <typename T0__,\n stan::require_all_t<stan::is_col_vector<T0__>,\n stan::is_vt_not_complex<T0__>>*>\nstd::vector<\n Eigen::Matrix<stan::promote_args_t<stan::base_type_t<T0__>>,-1,1>>\nto_vector_of_arrays(const T0__& vec_arg__, const int& length_array,\n std::ostream* pstream__) {\n using local_scalar_t__ = stan::promote_args_t<stan::base_type_t<T0__>>;\n int current_statement__ = 0;\n const auto& vec = stan::math::to_ref(vec_arg__);\n static constexpr bool propto__ = true;\n // suppress unused var warning\n (void) propto__;\n local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());\n // suppress unused var warning\n (void) DUMMY_VAR__;\n try {\n current_statement__ = 68;\n stan::math::validate_non_negative_index(\"res\",\n \"integer_division(num_elements(vec), length_array)\",\n integer_division(stan::math::num_elements(vec), length_array, pstream__));\n current_statement__ = 69;\n stan::math::validate_non_negative_index(\"res\", \"length_array\",\n length_array);\n std::vector<Eigen::Matrix<local_scalar_t__,-1,1>> res =\n std::vector<Eigen::Matrix<local_scalar_t__,-1,1>>(integer_division(\n stan::math::num_elements(\n vec),\n length_array,\n pstream__),\n Eigen::Matrix<local_scalar_t__,-1,1>::Constant(length_array,\n DUMMY_VAR__));\n int j = std::numeric_limits<int>::min();\n current_statement__ = 71;\n j = 1;\n int i = std::numeric_limits<int>::min();\n current_statement__ = 72;\n i = 1;\n current_statement__ = 77;\n while (stan::math::logical_lte(j, stan::math::num_elements(vec))) {\n current_statement__ = 73;\n stan::model::assign(res,\n stan::model::rvalue(vec, \"vec\",\n stan::model::index_min_max(j, ((j + length_array) - 1))),\n \"assigning variable res\", stan::model::index_uni(i),\n stan::model::index_omni());\n current_statement__ = 74;\n i = (i + 1);\n current_statement__ = 75;\n j = (j + length_array);\n }\n current_statement__ = 78;\n return res;\n } catch (const std::exception& e) {\n stan::lang::rethrow_located(e, locations_array__[current_statement__]);\n }\n}\nclass modelaa9c6a27673f_rbmi_mmrm final : public model_base_crtp<modelaa9c6a27673f_rbmi_mmrm> {\nprivate:\n int N;\n int P;\n int G;\n int n_visit;\n int n_pat;\n std::vector<int> pat_G;\n std::vector<int> pat_n_pt;\n std::vector<int> pat_n_visit;\n std::vector<std::vector<int>> pat_sigma_index;\n Eigen::Matrix<double,-1,1> y_data__;\n Eigen::Matrix<double,-1,-1> Q_data__;\n Eigen::Matrix<double,-1,-1> R_data__;\n std::vector<Eigen::Matrix<double,-1,-1>> Sigma_init;\n Eigen::Matrix<double,-1,-1> R_inverse_data__;\n Eigen::Map<Eigen::Matrix<double,-1,1>> y{nullptr, 0};\n Eigen::Map<Eigen::Matrix<double,-1,-1>> Q{nullptr, 0, 0};\n Eigen::Map<Eigen::Matrix<double,-1,-1>> R{nullptr, 0, 0};\n Eigen::Map<Eigen::Matrix<double,-1,-1>> R_inverse{nullptr, 0, 0};\npublic:\n ~modelaa9c6a27673f_rbmi_mmrm() {}\n modelaa9c6a27673f_rbmi_mmrm(stan::io::var_context& context__, unsigned int\n random_seed__ = 0, std::ostream*\n pstream__ = nullptr) : model_base_crtp(0) {\n int current_statement__ = 0;\n using local_scalar_t__ = double;\n boost::ecuyer1988 base_rng__ =\n stan::services::util::create_rng(random_seed__, 0);\n // suppress unused var warning\n (void) base_rng__;\n static constexpr const char* function__ =\n \"modelaa9c6a27673f_rbmi_mmrm_namespace::modelaa9c6a27673f_rbmi_mmrm\";\n // suppress unused var warning\n (void) function__;\n local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());\n // suppress unused var warning\n (void) DUMMY_VAR__;\n try {\n int pos__ = std::numeric_limits<int>::min();\n pos__ = 1;\n current_statement__ = 29;\n context__.validate_dims(\"data initialization\", \"N\", \"int\",\n std::vector<size_t>{});\n N = std::numeric_limits<int>::min();\n current_statement__ = 29;\n N = context__.vals_i(\"N\")[(1 - 1)];\n current_statement__ = 29;\n stan::math::check_greater_or_equal(function__, \"N\", N, 1);\n current_statement__ = 30;\n context__.validate_dims(\"data initialization\", \"P\", \"int\",\n std::vector<size_t>{});\n P = std::numeric_limits<int>::min();\n current_statement__ = 30;\n P = context__.vals_i(\"P\")[(1 - 1)];\n current_statement__ = 30;\n stan::math::check_greater_or_equal(function__, \"P\", P, 1);\n current_statement__ = 31;\n context__.validate_dims(\"data initialization\", \"G\", \"int\",\n std::vector<size_t>{});\n G = std::numeric_limits<int>::min();\n current_statement__ = 31;\n G = context__.vals_i(\"G\")[(1 - 1)];\n current_statement__ = 31;\n stan::math::check_greater_or_equal(function__, \"G\", G, 1);\n current_statement__ = 32;\n context__.validate_dims(\"data initialization\", \"n_visit\", \"int\",\n std::vector<size_t>{});\n n_visit = std::numeric_limits<int>::min();\n current_statement__ = 32;\n n_visit = context__.vals_i(\"n_visit\")[(1 - 1)];\n current_statement__ = 32;\n stan::math::check_greater_or_equal(function__, \"n_visit\", n_visit, 1);\n current_statement__ = 33;\n context__.validate_dims(\"data initialization\", \"n_pat\", \"int\",\n std::vector<size_t>{});\n n_pat = std::numeric_limits<int>::min();\n current_statement__ = 33;\n n_pat = context__.vals_i(\"n_pat\")[(1 - 1)];\n current_statement__ = 33;\n stan::math::check_greater_or_equal(function__, \"n_pat\", n_pat, 1);\n current_statement__ = 34;\n stan::math::validate_non_negative_index(\"pat_G\", \"n_pat\", n_pat);\n current_statement__ = 35;\n context__.validate_dims(\"data initialization\", \"pat_G\", \"int\",\n std::vector<size_t>{static_cast<size_t>(n_pat)});\n pat_G = std::vector<int>(n_pat, std::numeric_limits<int>::min());\n current_statement__ = 35;\n pat_G = context__.vals_i(\"pat_G\");\n current_statement__ = 35;\n stan::math::check_greater_or_equal(function__, \"pat_G\", pat_G, 1);\n current_statement__ = 36;\n stan::math::validate_non_negative_index(\"pat_n_pt\", \"n_pat\", n_pat);\n current_statement__ = 37;\n context__.validate_dims(\"data initialization\", \"pat_n_pt\", \"int\",\n std::vector<size_t>{static_cast<size_t>(n_pat)});\n pat_n_pt = std::vector<int>(n_pat, std::numeric_limits<int>::min());\n current_statement__ = 37;\n pat_n_pt = context__.vals_i(\"pat_n_pt\");\n current_statement__ = 37;\n stan::math::check_greater_or_equal(function__, \"pat_n_pt\", pat_n_pt, 1);\n current_statement__ = 38;\n stan::math::validate_non_negative_index(\"pat_n_visit\", \"n_pat\", n_pat);\n current_statement__ = 39;\n context__.validate_dims(\"data initialization\", \"pat_n_visit\", \"int\",\n std::vector<size_t>{static_cast<size_t>(n_pat)});\n pat_n_visit = std::vector<int>(n_pat, std::numeric_limits<int>::min());\n current_statement__ = 39;\n pat_n_visit = context__.vals_i(\"pat_n_visit\");\n current_statement__ = 39;\n stan::math::check_greater_or_equal(function__, \"pat_n_visit\",\n pat_n_visit, 1);\n current_statement__ = 40;\n stan::math::validate_non_negative_index(\"pat_sigma_index\", \"n_pat\",\n n_pat);\n current_statement__ = 41;\n stan::math::validate_non_negative_index(\"pat_sigma_index\", \"n_visit\",\n n_visit);\n current_statement__ = 42;\n context__.validate_dims(\"data initialization\", \"pat_sigma_index\",\n \"int\",\n std::vector<size_t>{static_cast<size_t>(n_pat),\n static_cast<size_t>(n_visit)});\n pat_sigma_index = std::vector<std::vector<int>>(n_pat,\n std::vector<int>(n_visit,\n std::numeric_limits<int>::min()));\n {\n std::vector<int> pat_sigma_index_flat__;\n current_statement__ = 42;\n pat_sigma_index_flat__ = context__.vals_i(\"pat_sigma_index\");\n current_statement__ = 42;\n pos__ = 1;\n current_statement__ = 42;\n for (int sym1__ = 1; sym1__ <= n_visit; ++sym1__) {\n current_statement__ = 42;\n for (int sym2__ = 1; sym2__ <= n_pat; ++sym2__) {\n current_statement__ = 42;\n stan::model::assign(pat_sigma_index,\n pat_sigma_index_flat__[(pos__ - 1)],\n \"assigning variable pat_sigma_index\",\n stan::model::index_uni(sym2__), stan::model::index_uni(sym1__));\n current_statement__ = 42;\n pos__ = (pos__ + 1);\n }\n }\n }\n current_statement__ = 42;\n stan::math::check_greater_or_equal(function__, \"pat_sigma_index\",\n pat_sigma_index, 1);\n current_statement__ = 43;\n stan::math::validate_non_negative_index(\"y\", \"N\", N);\n current_statement__ = 44;\n context__.validate_dims(\"data initialization\", \"y\", \"double\",\n std::vector<size_t>{static_cast<size_t>(N)});\n y_data__ = Eigen::Matrix<double,-1,1>::Constant(N,\n std::numeric_limits<double>::quiet_NaN());\n new (&y) Eigen::Map<Eigen::Matrix<double,-1,1>>(y_data__.data(), N);\n {\n std::vector<local_scalar_t__> y_flat__;\n current_statement__ = 44;\n y_flat__ = context__.vals_r(\"y\");\n current_statement__ = 44;\n pos__ = 1;\n current_statement__ = 44;\n for (int sym1__ = 1; sym1__ <= N; ++sym1__) {\n current_statement__ = 44;\n stan::model::assign(y, y_flat__[(pos__ - 1)],\n \"assigning variable y\", stan::model::index_uni(sym1__));\n current_statement__ = 44;\n pos__ = (pos__ + 1);\n }\n }\n current_statement__ = 45;\n stan::math::validate_non_negative_index(\"Q\", \"N\", N);\n current_statement__ = 46;\n stan::math::validate_non_negative_index(\"Q\", \"P\", P);\n current_statement__ = 47;\n context__.validate_dims(\"data initialization\", \"Q\", \"double\",\n std::vector<size_t>{static_cast<size_t>(N), static_cast<size_t>(P)});\n Q_data__ = Eigen::Matrix<double,-1,-1>::Constant(N, P,\n std::numeric_limits<double>::quiet_NaN());\n new (&Q) Eigen::Map<Eigen::Matrix<double,-1,-1>>(Q_data__.data(), N, P);\n {\n std::vector<local_scalar_t__> Q_flat__;\n current_statement__ = 47;\n Q_flat__ = context__.vals_r(\"Q\");\n current_statement__ = 47;\n pos__ = 1;\n current_statement__ = 47;\n for (int sym1__ = 1; sym1__ <= P; ++sym1__) {\n current_statement__ = 47;\n for (int sym2__ = 1; sym2__ <= N; ++sym2__) {\n current_statement__ = 47;\n stan::model::assign(Q, Q_flat__[(pos__ - 1)],\n \"assigning variable Q\", stan::model::index_uni(sym2__),\n stan::model::index_uni(sym1__));\n current_statement__ = 47;\n pos__ = (pos__ + 1);\n }\n }\n }\n current_statement__ = 48;\n stan::math::validate_non_negative_index(\"R\", \"P\", P);\n current_statement__ = 49;\n stan::math::validate_non_negative_index(\"R\", \"P\", P);\n current_statement__ = 50;\n context__.validate_dims(\"data initialization\", \"R\", \"double\",\n std::vector<size_t>{static_cast<size_t>(P), static_cast<size_t>(P)});\n R_data__ = Eigen::Matrix<double,-1,-1>::Constant(P, P,\n std::numeric_limits<double>::quiet_NaN());\n new (&R) Eigen::Map<Eigen::Matrix<double,-1,-1>>(R_data__.data(), P, P);\n {\n std::vector<local_scalar_t__> R_flat__;\n current_statement__ = 50;\n R_flat__ = context__.vals_r(\"R\");\n current_statement__ = 50;\n pos__ = 1;\n current_statement__ = 50;\n for (int sym1__ = 1; sym1__ <= P; ++sym1__) {\n current_statement__ = 50;\n for (int sym2__ = 1; sym2__ <= P; ++sym2__) {\n current_statement__ = 50;\n stan::model::assign(R, R_flat__[(pos__ - 1)],\n \"assigning variable R\", stan::model::index_uni(sym2__),\n stan::model::index_uni(sym1__));\n current_statement__ = 50;\n pos__ = (pos__ + 1);\n }\n }\n }\n current_statement__ = 51;\n stan::math::validate_non_negative_index(\"Sigma_init\", \"G\", G);\n current_statement__ = 52;\n stan::math::validate_non_negative_index(\"Sigma_init\", \"n_visit\",\n n_visit);\n current_statement__ = 53;\n stan::math::validate_non_negative_index(\"Sigma_init\", \"n_visit\",\n n_visit);\n current_statement__ = 54;\n context__.validate_dims(\"data initialization\", \"Sigma_init\", \"double\",\n std::vector<size_t>{static_cast<size_t>(G),\n static_cast<size_t>(n_visit), static_cast<size_t>(n_visit)});\n Sigma_init = std::vector<Eigen::Matrix<double,-1,-1>>(G,\n Eigen::Matrix<double,-1,-1>::Constant(n_visit, n_visit,\n std::numeric_limits<double>::quiet_NaN()));\n {\n std::vector<local_scalar_t__> Sigma_init_flat__;\n current_statement__ = 54;\n Sigma_init_flat__ = context__.vals_r(\"Sigma_init\");\n current_statement__ = 54;\n pos__ = 1;\n current_statement__ = 54;\n for (int sym1__ = 1; sym1__ <= n_visit; ++sym1__) {\n current_statement__ = 54;\n for (int sym2__ = 1; sym2__ <= n_visit; ++sym2__) {\n current_statement__ = 54;\n for (int sym3__ = 1; sym3__ <= G; ++sym3__) {\n current_statement__ = 54;\n stan::model::assign(Sigma_init, Sigma_init_flat__[(pos__ - 1)],\n \"assigning variable Sigma_init\",\n stan::model::index_uni(sym3__),\n stan::model::index_uni(sym2__),\n stan::model::index_uni(sym1__));\n current_statement__ = 54;\n pos__ = (pos__ + 1);\n }\n }\n }\n }\n current_statement__ = 55;\n stan::math::validate_non_negative_index(\"R_inverse\", \"P\", P);\n current_statement__ = 56;\n stan::math::validate_non_negative_index(\"R_inverse\", \"P\", P);\n current_statement__ = 57;\n R_inverse_data__ = Eigen::Matrix<double,-1,-1>::Constant(P, P,\n std::numeric_limits<double>::quiet_NaN());\n new (&R_inverse)\n Eigen::Map<Eigen::Matrix<double,-1,-1>>(R_inverse_data__.data(), P,\n P);\n current_statement__ = 57;\n stan::model::assign(R_inverse, stan::math::inverse(R),\n \"assigning variable R_inverse\");\n current_statement__ = 58;\n stan::math::validate_non_negative_index(\"theta\", \"P\", P);\n current_statement__ = 59;\n stan::math::validate_non_negative_index(\"Sigma\", \"G\", G);\n current_statement__ = 60;\n stan::math::validate_non_negative_index(\"Sigma\", \"n_visit\", n_visit);\n current_statement__ = 60;\n stan::math::validate_non_negative_index(\"Sigma\", \"n_visit\", n_visit);\n current_statement__ = 61;\n stan::math::validate_non_negative_index(\"beta\", \"P\", P);\n } catch (const std::exception& e) {\n stan::lang::rethrow_located(e, locations_array__[current_statement__]);\n }\n num_params_r__ = P + (G * (n_visit + ((n_visit * (n_visit - 1)) / 2)));\n }\n inline std::string model_name() const final {\n return \"modelaa9c6a27673f_rbmi_mmrm\";\n }\n inline std::vector<std::string> model_compile_info() const noexcept {\n return std::vector<std::string>{\"stanc_version = stanc3 v2.32.2\",\n \"stancflags = --\"};\n }\n template <bool propto__, bool jacobian__, typename VecR, typename VecI,\n stan::require_vector_like_t<VecR>* = nullptr,\n stan::require_vector_like_vt<std::is_integral, VecI>* = nullptr>\n inline stan::scalar_type_t<VecR>\n log_prob_impl(VecR& params_r__, VecI& params_i__, std::ostream*\n pstream__ = nullptr) const {\n using T__ = stan::scalar_type_t<VecR>;\n using local_scalar_t__ = T__;\n T__ lp__(0.0);\n stan::math::accumulator<T__> lp_accum__;\n stan::io::deserializer<local_scalar_t__> in__(params_r__, params_i__);\n int current_statement__ = 0;\n local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());\n // suppress unused var warning\n (void) DUMMY_VAR__;\n static constexpr const char* function__ =\n \"modelaa9c6a27673f_rbmi_mmrm_namespace::log_prob\";\n // suppress unused var warning\n (void) function__;\n try {\n Eigen::Matrix<local_scalar_t__,-1,1> theta =\n Eigen::Matrix<local_scalar_t__,-1,1>::Constant(P, DUMMY_VAR__);\n current_statement__ = 1;\n theta = in__.template read<Eigen::Matrix<local_scalar_t__,-1,1>>(P);\n std::vector<Eigen::Matrix<local_scalar_t__,-1,-1>> Sigma =\n std::vector<Eigen::Matrix<local_scalar_t__,-1,-1>>(G,\n Eigen::Matrix<local_scalar_t__,-1,-1>::Constant(n_visit, n_visit,\n DUMMY_VAR__));\n current_statement__ = 2;\n Sigma = in__.template read_constrain_cov_matrix<\n std::vector<Eigen::Matrix<local_scalar_t__,-1,-1>>,\n jacobian__>(lp__, G, n_visit);\n {\n int data_start_row = std::numeric_limits<int>::min();\n current_statement__ = 4;\n data_start_row = 1;\n current_statement__ = 5;\n stan::math::validate_non_negative_index(\"mu\", \"N\", N);\n Eigen::Matrix<local_scalar_t__,-1,1> mu =\n Eigen::Matrix<local_scalar_t__,-1,1>::Constant(N, DUMMY_VAR__);\n current_statement__ = 6;\n stan::model::assign(mu, stan::math::multiply(Q, theta),\n \"assigning variable mu\");\n current_statement__ = 9;\n for (int g = 1; g <= G; ++g) {\n current_statement__ = 7;\n lp_accum__.add(stan::math::inv_wishart_lpdf<propto__>(\n stan::model::rvalue(Sigma, \"Sigma\",\n stan::model::index_uni(g)), (n_visit + 2),\n stan::model::rvalue(Sigma_init, \"Sigma_init\",\n stan::model::index_uni(g))));\n }\n current_statement__ = 28;\n for (int i = 1; i <= n_pat; ++i) {\n int nvis = std::numeric_limits<int>::min();\n current_statement__ = 10;\n nvis = stan::model::rvalue(pat_n_visit, \"pat_n_visit\",\n stan::model::index_uni(i));\n int npt = std::numeric_limits<int>::min();\n current_statement__ = 11;\n npt = stan::model::rvalue(pat_n_pt, \"pat_n_pt\",\n stan::model::index_uni(i));\n int g = std::numeric_limits<int>::min();\n current_statement__ = 12;\n g = stan::model::rvalue(pat_G, \"pat_G\", stan::model::index_uni(i));\n current_statement__ = 13;\n stan::math::validate_non_negative_index(\"sig_index\", \"nvis\", nvis);\n std::vector<int> sig_index =\n std::vector<int>(nvis, std::numeric_limits<int>::min());\n current_statement__ = 14;\n stan::model::assign(sig_index,\n stan::model::rvalue(pat_sigma_index, \"pat_sigma_index\",\n stan::model::index_uni(i), stan::model::index_min_max(1, nvis)),\n \"assigning variable sig_index\");\n current_statement__ = 15;\n stan::math::validate_non_negative_index(\"sig\", \"nvis\", nvis);\n current_statement__ = 16;\n stan::math::validate_non_negative_index(\"sig\", \"nvis\", nvis);\n Eigen::Matrix<local_scalar_t__,-1,-1> sig =\n Eigen::Matrix<local_scalar_t__,-1,-1>::Constant(nvis, nvis,\n DUMMY_VAR__);\n current_statement__ = 17;\n stan::model::assign(sig,\n stan::model::rvalue(\n stan::model::rvalue(Sigma, \"Sigma\", stan::model::index_uni(g)),\n \"Sigma[g]\", stan::model::index_multi(sig_index),\n stan::model::index_multi(sig_index)), \"assigning variable sig\");\n int data_stop_row = std::numeric_limits<int>::min();\n current_statement__ = 18;\n data_stop_row = (data_start_row + ((nvis * npt) - 1));\n current_statement__ = 19;\n stan::math::validate_non_negative_index(\"y_obs\", \"npt\", npt);\n current_statement__ = 20;\n stan::math::validate_non_negative_index(\"y_obs\", \"nvis\", nvis);\n std::vector<Eigen::Matrix<local_scalar_t__,-1,1>> y_obs =\n std::vector<Eigen::Matrix<local_scalar_t__,-1,1>>(npt,\n Eigen::Matrix<local_scalar_t__,-1,1>::Constant(nvis,\n DUMMY_VAR__));\n current_statement__ = 21;\n stan::model::assign(y_obs,\n to_vector_of_arrays(\n stan::model::rvalue(y, \"y\",\n stan::model::index_min_max(data_start_row, data_stop_row)),\n nvis, pstream__), \"assigning variable y_obs\");\n current_statement__ = 22;\n stan::math::validate_non_negative_index(\"mu_obs\", \"npt\", npt);\n current_statement__ = 23;\n stan::math::validate_non_negative_index(\"mu_obs\", \"nvis\", nvis);\n std::vector<Eigen::Matrix<local_scalar_t__,-1,1>> mu_obs =\n std::vector<Eigen::Matrix<local_scalar_t__,-1,1>>(npt,\n Eigen::Matrix<local_scalar_t__,-1,1>::Constant(nvis,\n DUMMY_VAR__));\n current_statement__ = 24;\n stan::model::assign(mu_obs,\n to_vector_of_arrays(\n stan::model::rvalue(mu, \"mu\",\n stan::model::index_min_max(data_start_row, data_stop_row)),\n nvis, pstream__), \"assigning variable mu_obs\");\n current_statement__ = 25;\n lp_accum__.add(stan::math::multi_normal_lpdf<propto__>(y_obs,\n mu_obs, sig));\n current_statement__ = 26;\n data_start_row = (data_stop_row + 1);\n }\n }\n } catch (const std::exception& e) {\n stan::lang::rethrow_located(e, locations_array__[current_statement__]);\n }\n lp_accum__.add(lp__);\n return lp_accum__.sum();\n }\n template <typename RNG, typename VecR, typename VecI, typename VecVar,\n stan::require_vector_like_vt<std::is_floating_point,\n VecR>* = nullptr, stan::require_vector_like_vt<std::is_integral,\n VecI>* = nullptr, stan::require_vector_vt<std::is_floating_point,\n VecVar>* = nullptr>\n inline void\n write_array_impl(RNG& base_rng__, VecR& params_r__, VecI& params_i__,\n VecVar& vars__, const bool\n emit_transformed_parameters__ = true, const bool\n emit_generated_quantities__ = true, std::ostream*\n pstream__ = nullptr) const {\n using local_scalar_t__ = double;\n stan::io::deserializer<local_scalar_t__> in__(params_r__, params_i__);\n stan::io::serializer<local_scalar_t__> out__(vars__);\n static constexpr bool propto__ = true;\n // suppress unused var warning\n (void) propto__;\n double lp__ = 0.0;\n // suppress unused var warning\n (void) lp__;\n int current_statement__ = 0;\n stan::math::accumulator<double> lp_accum__;\n local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());\n // suppress unused var warning\n (void) DUMMY_VAR__;\n constexpr bool jacobian__ = false;\n static constexpr const char* function__ =\n \"modelaa9c6a27673f_rbmi_mmrm_namespace::write_array\";\n // suppress unused var warning\n (void) function__;\n try {\n Eigen::Matrix<double,-1,1> theta =\n Eigen::Matrix<double,-1,1>::Constant(P,\n std::numeric_limits<double>::quiet_NaN());\n current_statement__ = 1;\n theta = in__.template read<Eigen::Matrix<local_scalar_t__,-1,1>>(P);\n std::vector<Eigen::Matrix<double,-1,-1>> Sigma =\n std::vector<Eigen::Matrix<double,-1,-1>>(G,\n Eigen::Matrix<double,-1,-1>::Constant(n_visit, n_visit,\n std::numeric_limits<double>::quiet_NaN()));\n current_statement__ = 2;\n Sigma = in__.template read_constrain_cov_matrix<\n std::vector<Eigen::Matrix<local_scalar_t__,-1,-1>>,\n jacobian__>(lp__, G, n_visit);\n out__.write(theta);\n for (int sym1__ = 1; sym1__ <= n_visit; ++sym1__) {\n for (int sym2__ = 1; sym2__ <= n_visit; ++sym2__) {\n for (int sym3__ = 1; sym3__ <= G; ++sym3__) {\n out__.write(stan::model::rvalue(Sigma, \"Sigma\",\n stan::model::index_uni(sym3__),\n stan::model::index_uni(sym2__),\n stan::model::index_uni(sym1__)));\n }\n }\n }\n if (stan::math::logical_negation(\n (stan::math::primitive_value(emit_transformed_parameters__) ||\n stan::math::primitive_value(emit_generated_quantities__)))) {\n return ;\n }\n if (stan::math::logical_negation(emit_generated_quantities__)) {\n return ;\n }\n Eigen::Matrix<double,-1,1> beta =\n Eigen::Matrix<double,-1,1>::Constant(P,\n std::numeric_limits<double>::quiet_NaN());\n current_statement__ = 3;\n stan::model::assign(beta, stan::math::multiply(R_inverse, theta),\n \"assigning variable beta\");\n out__.write(beta);\n } catch (const std::exception& e) {\n stan::lang::rethrow_located(e, locations_array__[current_statement__]);\n }\n }\n template <typename VecVar, typename VecI,\n stan::require_vector_t<VecVar>* = nullptr,\n stan::require_vector_like_vt<std::is_integral, VecI>* = nullptr>\n inline void\n unconstrain_array_impl(const VecVar& params_r__, const VecI& params_i__,\n VecVar& vars__, std::ostream* pstream__ = nullptr) const {\n using local_scalar_t__ = double;\n stan::io::deserializer<local_scalar_t__> in__(params_r__, params_i__);\n stan::io::serializer<local_scalar_t__> out__(vars__);\n int current_statement__ = 0;\n local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());\n // suppress unused var warning\n (void) DUMMY_VAR__;\n try {\n int pos__ = std::numeric_limits<int>::min();\n pos__ = 1;\n Eigen::Matrix<local_scalar_t__,-1,1> theta =\n Eigen::Matrix<local_scalar_t__,-1,1>::Constant(P, DUMMY_VAR__);\n current_statement__ = 1;\n stan::model::assign(theta,\n in__.read<Eigen::Matrix<local_scalar_t__,-1,1>>(P),\n \"assigning variable theta\");\n out__.write(theta);\n std::vector<Eigen::Matrix<local_scalar_t__,-1,-1>> Sigma =\n std::vector<Eigen::Matrix<local_scalar_t__,-1,-1>>(G,\n Eigen::Matrix<local_scalar_t__,-1,-1>::Constant(n_visit, n_visit,\n DUMMY_VAR__));\n current_statement__ = 2;\n for (int sym1__ = 1; sym1__ <= n_visit; ++sym1__) {\n current_statement__ = 2;\n for (int sym2__ = 1; sym2__ <= n_visit; ++sym2__) {\n current_statement__ = 2;\n for (int sym3__ = 1; sym3__ <= G; ++sym3__) {\n current_statement__ = 2;\n stan::model::assign(Sigma, in__.read<local_scalar_t__>(),\n \"assigning variable Sigma\", stan::model::index_uni(sym3__),\n stan::model::index_uni(sym2__), stan::model::index_uni(sym1__));\n }\n }\n }\n out__.write_free_cov_matrix(Sigma);\n } catch (const std::exception& e) {\n stan::lang::rethrow_located(e, locations_array__[current_statement__]);\n }\n }\n template <typename VecVar, stan::require_vector_t<VecVar>* = nullptr>\n inline void\n transform_inits_impl(const stan::io::var_context& context__, VecVar&\n vars__, std::ostream* pstream__ = nullptr) const {\n using local_scalar_t__ = double;\n stan::io::serializer<local_scalar_t__> out__(vars__);\n int current_statement__ = 0;\n local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());\n // suppress unused var warning\n (void) DUMMY_VAR__;\n try {\n current_statement__ = 1;\n context__.validate_dims(\"parameter initialization\", \"theta\", \"double\",\n std::vector<size_t>{static_cast<size_t>(P)});\n current_statement__ = 2;\n context__.validate_dims(\"parameter initialization\", \"Sigma\", \"double\",\n std::vector<size_t>{static_cast<size_t>(G),\n static_cast<size_t>(n_visit), static_cast<size_t>(n_visit)});\n int pos__ = std::numeric_limits<int>::min();\n pos__ = 1;\n Eigen::Matrix<local_scalar_t__,-1,1> theta =\n Eigen::Matrix<local_scalar_t__,-1,1>::Constant(P, DUMMY_VAR__);\n {\n std::vector<local_scalar_t__> theta_flat__;\n current_statement__ = 1;\n theta_flat__ = context__.vals_r(\"theta\");\n current_statement__ = 1;\n pos__ = 1;\n current_statement__ = 1;\n for (int sym1__ = 1; sym1__ <= P; ++sym1__) {\n current_statement__ = 1;\n stan::model::assign(theta, theta_flat__[(pos__ - 1)],\n \"assigning variable theta\", stan::model::index_uni(sym1__));\n current_statement__ = 1;\n pos__ = (pos__ + 1);\n }\n }\n out__.write(theta);\n std::vector<Eigen::Matrix<local_scalar_t__,-1,-1>> Sigma =\n std::vector<Eigen::Matrix<local_scalar_t__,-1,-1>>(G,\n Eigen::Matrix<local_scalar_t__,-1,-1>::Constant(n_visit, n_visit,\n DUMMY_VAR__));\n {\n std::vector<local_scalar_t__> Sigma_flat__;\n current_statement__ = 2;\n Sigma_flat__ = context__.vals_r(\"Sigma\");\n current_statement__ = 2;\n pos__ = 1;\n current_statement__ = 2;\n for (int sym1__ = 1; sym1__ <= n_visit; ++sym1__) {\n current_statement__ = 2;\n for (int sym2__ = 1; sym2__ <= n_visit; ++sym2__) {\n current_statement__ = 2;\n for (int sym3__ = 1; sym3__ <= G; ++sym3__) {\n current_statement__ = 2;\n stan::model::assign(Sigma, Sigma_flat__[(pos__ - 1)],\n \"assigning variable Sigma\", stan::model::index_uni(sym3__),\n stan::model::index_uni(sym2__),\n stan::model::index_uni(sym1__));\n current_statement__ = 2;\n pos__ = (pos__ + 1);\n }\n }\n }\n }\n out__.write_free_cov_matrix(Sigma);\n } catch (const std::exception& e) {\n stan::lang::rethrow_located(e, locations_array__[current_statement__]);\n }\n }\n inline void\n get_param_names(std::vector<std::string>& names__, const bool\n emit_transformed_parameters__ = true, const bool\n emit_generated_quantities__ = true) const {\n names__ = std::vector<std::string>{\"theta\", \"Sigma\"};\n if (emit_transformed_parameters__) {}\n if (emit_generated_quantities__) {\n std::vector<std::string> temp{\"beta\"};\n names__.reserve(names__.size() + temp.size());\n names__.insert(names__.end(), temp.begin(), temp.end());\n }\n }\n inline void\n get_dims(std::vector<std::vector<size_t>>& dimss__, const bool\n emit_transformed_parameters__ = true, const bool\n emit_generated_quantities__ = true) const {\n dimss__ = std::vector<std::vector<size_t>>{std::vector<size_t>{static_cast<\n size_t>(P)},\n std::vector<size_t>{static_cast<size_t>(G),\n static_cast<size_t>(n_visit), static_cast<size_t>(n_visit)}};\n if (emit_transformed_parameters__) {}\n if (emit_generated_quantities__) {\n std::vector<std::vector<size_t>>\n temp{std::vector<size_t>{static_cast<size_t>(P)}};\n dimss__.reserve(dimss__.size() + temp.size());\n dimss__.insert(dimss__.end(), temp.begin(), temp.end());\n }\n }\n inline void\n constrained_param_names(std::vector<std::string>& param_names__, bool\n emit_transformed_parameters__ = true, bool\n emit_generated_quantities__ = true) const final {\n for (int sym1__ = 1; sym1__ <= P; ++sym1__) {\n param_names__.emplace_back(std::string() + \"theta\" + '.' +\n std::to_string(sym1__));\n }\n for (int sym1__ = 1; sym1__ <= n_visit; ++sym1__) {\n for (int sym2__ = 1; sym2__ <= n_visit; ++sym2__) {\n for (int sym3__ = 1; sym3__ <= G; ++sym3__) {\n param_names__.emplace_back(std::string() + \"Sigma\" + '.' +\n std::to_string(sym3__) + '.' + std::to_string(sym2__) + '.' +\n std::to_string(sym1__));\n }\n }\n }\n if (emit_transformed_parameters__) {}\n if (emit_generated_quantities__) {\n for (int sym1__ = 1; sym1__ <= P; ++sym1__) {\n param_names__.emplace_back(std::string() + \"beta\" + '.' +\n std::to_string(sym1__));\n }\n }\n }\n inline void\n unconstrained_param_names(std::vector<std::string>& param_names__, bool\n emit_transformed_parameters__ = true, bool\n emit_generated_quantities__ = true) const final {\n for (int sym1__ = 1; sym1__ <= P; ++sym1__) {\n param_names__.emplace_back(std::string() + \"theta\" + '.' +\n std::to_string(sym1__));\n }\n for (int sym1__ = 1; sym1__ <= (n_visit + ((n_visit * (n_visit - 1)) /\n 2)); ++sym1__) {\n for (int sym2__ = 1; sym2__ <= G; ++sym2__) {\n param_names__.emplace_back(std::string() + \"Sigma\" + '.' +\n std::to_string(sym2__) + '.' + std::to_string(sym1__));\n }\n }\n if (emit_transformed_parameters__) {}\n if (emit_generated_quantities__) {\n for (int sym1__ = 1; sym1__ <= P; ++sym1__) {\n param_names__.emplace_back(std::string() + \"beta\" + '.' +\n std::to_string(sym1__));\n }\n }\n }\n inline std::string get_constrained_sizedtypes() const {\n return std::string(\"[{\\\"name\\\":\\\"theta\\\",\\\"type\\\":{\\\"name\\\":\\\"vector\\\",\\\"length\\\":\" + std::to_string(P) + \"},\\\"block\\\":\\\"parameters\\\"},{\\\"name\\\":\\\"Sigma\\\",\\\"type\\\":{\\\"name\\\":\\\"array\\\",\\\"length\\\":\" + std::to_string(G) + \",\\\"element_type\\\":{\\\"name\\\":\\\"matrix\\\",\\\"rows\\\":\" + std::to_string(n_visit) + \",\\\"cols\\\":\" + std::to_string(n_visit) + \"}},\\\"block\\\":\\\"parameters\\\"},{\\\"name\\\":\\\"beta\\\",\\\"type\\\":{\\\"name\\\":\\\"vector\\\",\\\"length\\\":\" + std::to_string(P) + \"},\\\"block\\\":\\\"generated_quantities\\\"}]\");\n }\n inline std::string get_unconstrained_sizedtypes() const {\n return std::string(\"[{\\\"name\\\":\\\"theta\\\",\\\"type\\\":{\\\"name\\\":\\\"vector\\\",\\\"length\\\":\" + std::to_string(P) + \"},\\\"block\\\":\\\"parameters\\\"},{\\\"name\\\":\\\"Sigma\\\",\\\"type\\\":{\\\"name\\\":\\\"array\\\",\\\"length\\\":\" + std::to_string(G) + \",\\\"element_type\\\":{\\\"name\\\":\\\"vector\\\",\\\"length\\\":\" + std::to_string((n_visit + ((n_visit * (n_visit - 1)) /2))) + \"}},\\\"block\\\":\\\"parameters\\\"},{\\\"name\\\":\\\"beta\\\",\\\"type\\\":{\\\"name\\\":\\\"vector\\\",\\\"length\\\":\" + std::to_string(P) + \"},\\\"block\\\":\\\"generated_quantities\\\"}]\");\n }\n // Begin method overload boilerplate\n template <typename RNG> inline void\n write_array(RNG& base_rng, Eigen::Matrix<double,-1,1>& params_r,\n Eigen::Matrix<double,-1,1>& vars, const bool\n emit_transformed_parameters = true, const bool\n emit_generated_quantities = true, std::ostream*\n pstream = nullptr) const {\n const size_t num_params__ = (P + ((G * n_visit) * n_visit));\n const size_t num_transformed = emit_transformed_parameters * (0);\n const size_t num_gen_quantities = emit_generated_quantities * (P);\n const size_t num_to_write = num_params__ + num_transformed +\n num_gen_quantities;\n std::vector<int> params_i;\n vars = Eigen::Matrix<double,-1,1>::Constant(num_to_write,\n std::numeric_limits<double>::quiet_NaN());\n write_array_impl(base_rng, params_r, params_i, vars,\n emit_transformed_parameters, emit_generated_quantities, pstream);\n }\n template <typename RNG> inline void\n write_array(RNG& base_rng, std::vector<double>& params_r, std::vector<int>&\n params_i, std::vector<double>& vars, bool\n emit_transformed_parameters = true, bool\n emit_generated_quantities = true, std::ostream*\n pstream = nullptr) const {\n const size_t num_params__ = (P + ((G * n_visit) * n_visit));\n const size_t num_transformed = emit_transformed_parameters * (0);\n const size_t num_gen_quantities = emit_generated_quantities * (P);\n const size_t num_to_write = num_params__ + num_transformed +\n num_gen_quantities;\n vars = std::vector<double>(num_to_write,\n std::numeric_limits<double>::quiet_NaN());\n write_array_impl(base_rng, params_r, params_i, vars,\n emit_transformed_parameters, emit_generated_quantities, pstream);\n }\n template <bool propto__, bool jacobian__, typename T_> inline T_\n log_prob(Eigen::Matrix<T_,-1,1>& params_r, std::ostream* pstream = nullptr) const {\n Eigen::Matrix<int,-1,1> params_i;\n return log_prob_impl<propto__, jacobian__>(params_r, params_i, pstream);\n }\n template <bool propto__, bool jacobian__, typename T_> inline T_\n log_prob(std::vector<T_>& params_r, std::vector<int>& params_i,\n std::ostream* pstream = nullptr) const {\n return log_prob_impl<propto__, jacobian__>(params_r, params_i, pstream);\n }\n inline void\n transform_inits(const stan::io::var_context& context,\n Eigen::Matrix<double,-1,1>& params_r, std::ostream*\n pstream = nullptr) const final {\n std::vector<double> params_r_vec(params_r.size());\n std::vector<int> params_i;\n transform_inits(context, params_i, params_r_vec, pstream);\n params_r = Eigen::Map<Eigen::Matrix<double,-1,1>>(params_r_vec.data(),\n params_r_vec.size());\n }\n inline void\n transform_inits(const stan::io::var_context& context, std::vector<int>&\n params_i, std::vector<double>& vars, std::ostream*\n pstream__ = nullptr) const {\n vars.resize(num_params_r__);\n transform_inits_impl(context, vars, pstream__);\n }\n inline void\n unconstrain_array(const std::vector<double>& params_constrained,\n std::vector<double>& params_unconstrained, std::ostream*\n pstream = nullptr) const {\n const std::vector<int> params_i;\n params_unconstrained = std::vector<double>(num_params_r__,\n std::numeric_limits<double>::quiet_NaN());\n unconstrain_array_impl(params_constrained, params_i,\n params_unconstrained, pstream);\n }\n inline void\n unconstrain_array(const Eigen::Matrix<double,-1,1>& params_constrained,\n Eigen::Matrix<double,-1,1>& params_unconstrained,\n std::ostream* pstream = nullptr) const {\n const std::vector<int> params_i;\n params_unconstrained = Eigen::Matrix<double,-1,1>::Constant(num_params_r__,\n std::numeric_limits<double>::quiet_NaN());\n unconstrain_array_impl(params_constrained, params_i,\n params_unconstrained, pstream);\n }\n};\n}\nusing stan_model = modelaa9c6a27673f_rbmi_mmrm_namespace::modelaa9c6a27673f_rbmi_mmrm;\n#ifndef USING_R\n// Boilerplate\nstan::model::model_base&\nnew_model(stan::io::var_context& data_context, unsigned int seed,\n std::ostream* msg_stream) {\n stan_model* m = new stan_model(data_context, seed, msg_stream);\n return *m;\n}\nstan::math::profile_map& get_stan_profile_data() {\n return modelaa9c6a27673f_rbmi_mmrm_namespace::profiles__;\n}\n#endif\n#endif"), mk_cppmodule = function (object) { prep_call_sampler(object) model_cppname <- object@model_cpp$model_cppname mod <- get("module", envir = object@dso@.CXXDSOMISC, inherits = FALSE) eval(call("$", mod, paste("stan_fit4", model_cppname, sep = ""))) }, dso = new("cxxdso", sig = list(fileaa9c5c0bb1fd = character(0)), dso_saved = TRUE, dso_filename = "fileaa9c5c0bb1fd", modulename = "stan_fit4modelaa9c6a27673f_rbmi_mmrm_mod", system = "x86_64, darwin20", cxxflags = "CXXFLAGS = -falign-functions=64 -Wall -g -O2 $(LTO)", .CXXDSOMISC = <environment>)), data = list(N = 340L, P = 8L, G = 1L, n_visit = 3L, n_pat = 2L, pat_G = c(1, 1), pat_n_pt = c(60, 80), pat_n_visit = c(3, 2), pat_sigma_index = list( c(1, 2, 3), c(1, 2, 999)), y = c(0.574036883477536, 0.139105398171835, 0.176977232131814, -1.16935503183362, -1.21591438062592, -1.08964625296442, -1.58121597188229, -1.03382417471284, -0.813329718686652, -0.659208380311965, -0.596830231476417, -0.428690642917848, 1.19340919052579, 1.19324384350827, 1.14076998750238, 0.508301722783451, 0.285101803715272, -0.080275904172036, 0.420951505580432, 0.729295857616047, 0.606493590677628, 1.36133467580718, 0.785097805390841, 0.850728879541265, 1.59518647362531, 1.99123113996823, 1.62317993728234, -1.01500989395391, -1.29590087043236, -1.09167466441661, -1.92326503149754, -1.6438232242331, -2.09494484800456, -0.916339955699448, -1.21279752527853, -0.824654473194631, 0.160587572122124, 0.533787595446638, 0.509785626882552, 0.625441265387049, 0.869595429439695, 0.606754094352448, 0.416887452319262, 0.0527870943089152, -0.290746253299322, -0.0405562348050075, 0.138363916063878, 0.199662379261492, -0.0292589587575427, 0.184625776371717, 0.596974269230419, 0.460701441061857, 0.606107749594927, 0.675147663778399, 1.66002871020651, 1.84142634216536, 1.99191103632866, 1.10904044059033, 0.414249192109747, 0.672496352831966, 0.789592735008045, 0.677931435319177, 0.543093801624015, -0.899674446856585, -0.98731077385762, -1.31298117495064, -0.790921720329644, -0.407036721173524, -0.221080281874233, 1.43428989449746, 1.46018790177788, 1.29835543116011, 1.93804574039781, 1.67293427649682, 1.58849553103043, -1.01450332611738, -0.842813717586719, -0.629379340054354, -0.267415091390975, -0.657913697591214, -0.595234267484234, 0.120679904254403, 0.178945058091685, 0.258466389498271, -0.449519107207705, -0.418311943730978, -0.607987725151117, 0.475470586549514, 0.465961733342575, 0.628255437173784, -0.269181976386959, -0.489283917847538, -0.480553495290893, 0.0126097945506521, 0.188893941324742, -0.00785768753235569, -1.64359171150621, -1.51956953580399, -1.83648421907106, -0.334174407608112, 0.173961399031304, 0.512090615451255, 0.815812442864812, 1.00796572845926, 1.08799807363422, -0.499836503819652, -0.194917201294718, -0.347531934556113, 0.0967719422298859, 0.0448422069391228, -0.0380229372755732, 0.89027308837742, 0.923859648959303, 0.693773508152388, 0.0200206385163762, 0.139715592410514, 0.355610280019225, -0.554244284647314, -0.337491541079209, -0.0476717323926215, 1.74097877156221, 1.74584951452271, 1.87453426708686, 1.53665210908614, 1.27351842521603, 0.782842422447314, 0.780226954526566, 0.533531436107968, 0.471968788026106, -1.50987360873711, -1.4213975714265, -1.32130245857978, -1.53643170343886, -1.96117485895016, -1.63868589576792, -0.0924369697453538, -0.236384532458898, -0.416943554508815, 0.524748546809872, 1.01066900867255, 1.03925121395702, -1.47575422517021, -1.84351894914451, -1.96406505049471, -0.958450708965594, -1.00073327233577, -1.15403786283949, 1.30985852929418, 0.677518704253333, 0.633123295459129, -1.00859795137567, -0.486351090482834, -0.629987247106474, -0.703803757755735, -0.423015811624416, -0.68260822611802, 1.29970709660456, 0.950166424412677, 0.700867866424596, 0.885973022497435, 0.69557127871005, 0.683167942482159, -0.448047722773281, -0.345122636504496, -0.44919578758697, -0.350389882825743, 0.218778679169879, 0.460591876155823, -1.13643984701411, -1.37594847426891, -1.38924924598483, 1.05497189150375, 1.44213159591197, 1.44244341883156, 0.989297127175544, 1.28938706033795, 0.974021459136989, 0.0997949939225535, 0.0977838256866348, 0.152771705034039, 0.0563099671794369, -0.109302500409893, -0.770343945290974, -0.840203655711169, -1.06621935574191, -0.818506844251997, 0.483309359293258, -0.077312553568267, -1.82787954030716, -1.51850241691396, 0.385765772873327, 0.318930612816827, 0.0529827480329966, -0.256048504100078, 0.755705549841678, 0.841931688341897, 0.712085742232759, 0.644772892574503, -1.14155231656161, -1.4428233579315, -1.05021841671635, -1.39451198199354, -0.267077536825949, -0.707269786974737, -1.45623361118695, -1.14058269087304, -0.768761960571674, -0.570612606157713, -0.764238909934079, -1.27539783051645, -0.845195950602966, -1.35937750037253, -0.403679174325962, -0.181315302498918, -0.138311056457262, 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0.00222305092368008, 0.00332949514226805, -0.246165884693855, 0, -0.0972018727205666, -0.0932785531887564, -0.0803448339241552, 0.191330027057476, -0.00444610184736015, -0.00665899028453591, 0.0732625048297967, -0.175220044568803), Sigma_init = list( c(0.135928026518976, 0.0348523498539469, 0.00728419213566572, 0.0348523498539469, 0.0345033646543252, 0.00223741352686764, 0.00728419213566572, 0.00223741352686764, 0.0129045826930864 ))), pars = c("beta", "Sigma"), chains = 1, warmup = 200, thin = 2, iter = 204, init = list(list(theta = c(-5.9502531301265e-05, -0.745820423193819, -0.00612121696522099, 0.00358076868728952, 0.552831864322152, -0.281246666814181, -0.00509794025644147, -0.00632342582509301), sigma = list(Placebo = c(0.135928026518976, 0.0348523498539469, 0.00728419213566572, 0.0348523498539469, 0.0345033646543252, 0.00223741352686764, 0.00728419213566572, 0.00223741352686764, 0.0129045826930864), TRT = c(0.135928026518976, 0.0348523498539469, 0.00728419213566572, 0.0348523498539469, 0.0345033646543252, 0.00223741352686764, 0.00728419213566572, 0.00223741352686764, 0.0129045826930864)))), refresh = 0, seed = 2053082391L)
10: (new("nonstandardGenericFunction", .Data = function (object, ...) { standardGeneric("sampling")}, generic = "sampling", package = "rstan", group = list(), valueClass = character(0), signature = "object", default = NULL, skeleton = (function (object, ...) stop(gettextf("invalid call in method dispatch to '%s' (no default method)", "sampling"), domain = NA))(object, ...)))(object = new("stanmodel", model_name = "rbmi_mmrm", model_code = "functions {\n int integer_division(int a, int b) {\n // perform a/b ensuring return value is also an int\n int i = 0;\n while(b*(i+1) <= a) {\n i = i + 1;\n }\n return(i);\n }\n array[] vector to_vector_of_arrays(vector vec, int length_array) {\n // treansform a vector into a vector of arrays. Example: vec = [1,2,3,4,5,6] and\n // length_array = 2, then output = [1,2; 3,4; 5,6]\n array[integer_division(num_elements(vec),length_array)] vector[length_array] res;\n int j = 1;\n int i = 1;\n while(j <= num_elements(vec)) {\n res[i,] = vec[j:(j+length_array-1)];\n i = i+1;\n j = j + length_array;\n }\n return(res);\n }\n}\ndata {\n int<lower=1> N; // number of observations\n int<lower=1> P; // number of covariates (number of columns of design matrix)\n int<lower=1> G; // number of Sigma Groups\n int<lower=1> n_visit; // number of visits\n int<lower=1> n_pat; // number of pat groups (# missingness patterns * groups)\n array[n_pat] int<lower=1> pat_G; // Index for which Sigma the pat group should use\n array[n_pat] int<lower=1> pat_n_pt; // number of patients in each pat group\n array[n_pat] int<lower=1> pat_n_visit; // number of non-missing visits in each pat group\n array[n_pat, n_visit] int<lower=1> pat_sigma_index; // rows/cols from sigma to subset on for the pat group\n vector[N] y; // outcome variable\n matrix[N,P] Q; // design matrix (After QR decomp)\n matrix[P,P] R; // R matrix (from QR decomp)\n array[G] matrix[n_visit, n_visit] Sigma_init; // covariance matrix estimated from MMRM\n}\ntransformed data {\n matrix[P, P] R_inverse = inverse(R);\n}\nparameters {\n vector[P] theta; // coefficients of linear model on covariates\n array[G] cov_matrix[n_visit] Sigma; // covariance matrix(s)\n}\nmodel {\n int data_start_row = 1;\n vector[N] mu = Q * theta;\n for(g in 1:G){\n Sigma[g] ~ inv_wishart(n_visit+2, Sigma_init[g]);\n }\n for(i in 1:n_pat) {\n // Index + size variables for current pat group\n int nvis = pat_n_visit[i]; // number of visits\n int npt = pat_n_pt[i]; // number of patients\n int g = pat_G[i]; // Sigma index\n // Get required/reduced Sigma for current pat group\n array[nvis] int sig_index = pat_sigma_index[i, 1:nvis];\n matrix[nvis,nvis] sig = Sigma[g][sig_index, sig_index];\n // Derive data indcies for current pat group\n int data_stop_row = data_start_row + ((nvis * npt) -1);\n // Extract required data for the current pat group\n array[npt] vector[nvis] y_obs = to_vector_of_arrays(y[data_start_row:data_stop_row], nvis);\n array[npt] vector[nvis] mu_obs = to_vector_of_arrays(mu[data_start_row:data_stop_row], nvis);\n y_obs ~ multi_normal(mu_obs, sig);\n // Update data index for next pat group\n data_start_row = data_stop_row + 1;\n }\n}\ngenerated quantities {\n vector[P] beta = R_inverse * theta;\n}", model_cpp = list(model_cppname = "modelaa9c6a27673f_rbmi_mmrm", model_cppcode = "#ifndef MODELS_HPP\n#define MODELS_HPP\n#define STAN__SERVICES__COMMAND_HPP\n#include <rstan/rstaninc.hpp>\n#ifndef USE_STANC3\n#define USE_STANC3\n#endif\n// Code generated by stanc v2.32.2\n#include <stan/model/model_header.hpp>\nnamespace modelaa9c6a27673f_rbmi_mmrm_namespace {\nusing stan::model::model_base_crtp;\nusing namespace stan::math;\nstan::math::profile_map profiles__;\nstatic constexpr std::array<const char*, 80> locations_array__ =\n {\" (found before start of program)\",\n \" (in 'rbmi_mmrm', line 43, column 4 to column 20)\",\n \" (in 'rbmi_mmrm', line 44, column 4 to column 39)\",\n \" (in 'rbmi_mmrm', line 71, column 3 to column 38)\",\n \" (in 'rbmi_mmrm', line 47, column 4 to column 27)\",\n \" (in 'rbmi_mmrm', line 48, column 11 to column 12)\",\n \" (in 'rbmi_mmrm', line 48, column 4 to column 29)\",\n \" (in 'rbmi_mmrm', line 50, column 8 to column 57)\",\n \" (in 'rbmi_mmrm', line 49, column 17 to line 51, column 5)\",\n \" (in 'rbmi_mmrm', line 49, column 4 to line 51, column 5)\",\n \" (in 'rbmi_mmrm', line 54, column 8 to column 34)\",\n \" (in 'rbmi_mmrm', line 55, column 8 to column 30)\",\n \" (in 'rbmi_mmrm', line 56, column 8 to column 25)\",\n \" (in 'rbmi_mmrm', line 58, column 14 to column 18)\",\n \" (in 'rbmi_mmrm', line 58, column 8 to column 63)\",\n \" (in 'rbmi_mmrm', line 59, column 15 to column 19)\",\n \" (in 'rbmi_mmrm', line 59, column 20 to column 24)\",\n \" (in 'rbmi_mmrm', line 59, column 8 to column 63)\",\n \" (in 'rbmi_mmrm', line 61, column 8 to column 63)\",\n \" (in 'rbmi_mmrm', line 63, column 14 to column 17)\",\n \" (in 'rbmi_mmrm', line 63, column 26 to column 30)\",\n \" (in 'rbmi_mmrm', line 63, column 8 to column 99)\",\n \" (in 'rbmi_mmrm', line 64, column 14 to column 17)\",\n \" (in 'rbmi_mmrm', line 64, column 26 to column 30)\",\n \" (in 'rbmi_mmrm', line 64, column 8 to column 101)\",\n \" (in 'rbmi_mmrm', line 65, column 8 to column 42)\",\n \" (in 'rbmi_mmrm', line 67, column 8 to column 43)\",\n \" (in 'rbmi_mmrm', line 52, column 22 to line 68, column 5)\",\n \" (in 'rbmi_mmrm', line 52, column 4 to line 68, column 5)\",\n \" (in 'rbmi_mmrm', line 25, column 4 to column 19)\",\n \" (in 'rbmi_mmrm', line 26, column 4 to column 19)\",\n \" (in 'rbmi_mmrm', line 27, column 4 to column 19)\",\n \" (in 'rbmi_mmrm', line 28, column 4 to column 25)\",\n \" (in 'rbmi_mmrm', line 29, column 4 to column 23)\",\n \" (in 'rbmi_mmrm', line 30, column 10 to column 15)\",\n \" (in 'rbmi_mmrm', line 30, column 4 to column 36)\",\n \" (in 'rbmi_mmrm', line 31, column 10 to column 15)\",\n \" (in 'rbmi_mmrm', line 31, column 4 to column 39)\",\n \" (in 'rbmi_mmrm', line 32, column 10 to column 15)\",\n \" (in 'rbmi_mmrm', line 32, column 4 to column 42)\",\n \" (in 'rbmi_mmrm', line 33, column 10 to column 15)\",\n \" (in 'rbmi_mmrm', line 33, column 17 to column 24)\",\n \" (in 'rbmi_mmrm', line 33, column 4 to column 55)\",\n \" (in 'rbmi_mmrm', line 34, column 11 to column 12)\",\n \" (in 'rbmi_mmrm', line 34, column 4 to column 16)\",\n \" (in 'rbmi_mmrm', line 35, column 11 to column 12)\",\n \" (in 'rbmi_mmrm', line 35, column 13 to column 14)\",\n \" (in 'rbmi_mmrm', line 35, column 4 to column 18)\",\n \" (in 'rbmi_mmrm', line 36, column 11 to column 12)\",\n \" (in 'rbmi_mmrm', line 36, column 13 to column 14)\",\n \" (in 'rbmi_mmrm', line 36, column 4 to column 18)\",\n \" (in 'rbmi_mmrm', line 37, column 10 to column 11)\",\n \" (in 'rbmi_mmrm', line 37, column 20 to column 27)\",\n \" (in 'rbmi_mmrm', line 37, column 29 to column 36)\",\n \" (in 'rbmi_mmrm', line 37, column 4 to column 49)\",\n \" (in 'rbmi_mmrm', line 40, column 10 to column 11)\",\n \" (in 'rbmi_mmrm', line 40, column 13 to column 14)\",\n \" (in 'rbmi_mmrm', line 40, column 3 to column 39)\",\n \" (in 'rbmi_mmrm', line 43, column 11 to column 12)\",\n \" (in 'rbmi_mmrm', line 44, column 10 to column 11)\",\n \" (in 'rbmi_mmrm', line 44, column 24 to column 31)\",\n \" (in 'rbmi_mmrm', line 71, column 10 to column 11)\",\n \" (in 'rbmi_mmrm', line 4, column 8 to column 18)\",\n \" (in 'rbmi_mmrm', line 6, column 12 to column 22)\",\n \" (in 'rbmi_mmrm', line 5, column 28 to line 7, column 9)\",\n \" (in 'rbmi_mmrm', line 5, column 8 to line 7, column 9)\",\n \" (in 'rbmi_mmrm', line 8, column 8 to column 18)\",\n \" (in 'rbmi_mmrm', line 2, column 39 to line 9, column 5)\",\n \" (in 'rbmi_mmrm', line 13, column 14 to column 62)\",\n \" (in 'rbmi_mmrm', line 13, column 71 to column 83)\",\n \" (in 'rbmi_mmrm', line 13, column 8 to column 89)\",\n \" (in 'rbmi_mmrm', line 14, column 8 to column 18)\",\n \" (in 'rbmi_mmrm', line 15, column 8 to column 18)\",\n \" (in 'rbmi_mmrm', line 17, column 12 to column 48)\",\n \" (in 'rbmi_mmrm', line 18, column 12 to column 20)\",\n \" (in 'rbmi_mmrm', line 19, column 12 to column 33)\",\n \" (in 'rbmi_mmrm', line 16, column 38 to line 20, column 9)\",\n \" (in 'rbmi_mmrm', line 16, column 8 to line 20, column 9)\",\n \" (in 'rbmi_mmrm', line 21, column 8 to column 20)\",\n \" (in 'rbmi_mmrm', line 10, column 69 to line 22, column 5)\"};\nint integer_division(const int& a, const int& b, std::ostream* pstream__);\ntemplate <typename T0__,\n stan::require_all_t<stan::is_col_vector<T0__>,\n stan::is_vt_not_complex<T0__>>* = nullptr>\nstd::vector<\n Eigen::Matrix<stan::promote_args_t<stan::base_type_t<T0__>>,-1,1>>\nto_vector_of_arrays(const T0__& vec_arg__, const int& length_array,\n std::ostream* pstream__);\nint integer_division(const int& a, const int& b, std::ostream* pstream__) {\n using local_scalar_t__ = double;\n int current_statement__ = 0;\n static constexpr bool propto__ = true;\n // suppress unused var warning\n (void) propto__;\n local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());\n // suppress unused var warning\n (void) DUMMY_VAR__;\n try {\n int i = std::numeric_limits<int>::min();\n current_statement__ = 62;\n i = 0;\n current_statement__ = 65;\n while (stan::math::logical_lte((b * (i + 1)), a)) {\n current_statement__ = 63;\n i = (i + 1);\n }\n current_statement__ = 66;\n return i;\n } catch (const std::exception& e) {\n stan::lang::rethrow_located(e, locations_array__[current_statement__]);\n }\n}\ntemplate <typename T0__,\n stan::require_all_t<stan::is_col_vector<T0__>,\n stan::is_vt_not_complex<T0__>>*>\nstd::vector<\n Eigen::Matrix<stan::promote_args_t<stan::base_type_t<T0__>>,-1,1>>\nto_vector_of_arrays(const T0__& vec_arg__, const int& length_array,\n std::ostream* pstream__) {\n using local_scalar_t__ = stan::promote_args_t<stan::base_type_t<T0__>>;\n int current_statement__ = 0;\n const auto& vec = stan::math::to_ref(vec_arg__);\n static constexpr bool propto__ = true;\n // suppress unused var warning\n (void) propto__;\n local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());\n // suppress unused var warning\n (void) DUMMY_VAR__;\n try {\n current_statement__ = 68;\n stan::math::validate_non_negative_index(\"res\",\n \"integer_division(num_elements(vec), length_array)\",\n integer_division(stan::math::num_elements(vec), length_array, pstream__));\n current_statement__ = 69;\n stan::math::validate_non_negative_index(\"res\", \"length_array\",\n length_array);\n std::vector<Eigen::Matrix<local_scalar_t__,-1,1>> res =\n std::vector<Eigen::Matrix<local_scalar_t__,-1,1>>(integer_division(\n stan::math::num_elements(\n vec),\n length_array,\n pstream__),\n Eigen::Matrix<local_scalar_t__,-1,1>::Constant(length_array,\n DUMMY_VAR__));\n int j = std::numeric_limits<int>::min();\n current_statement__ = 71;\n j = 1;\n int i = std::numeric_limits<int>::min();\n current_statement__ = 72;\n i = 1;\n current_statement__ = 77;\n while (stan::math::logical_lte(j, stan::math::num_elements(vec))) {\n current_statement__ = 73;\n stan::model::assign(res,\n stan::model::rvalue(vec, \"vec\",\n stan::model::index_min_max(j, ((j + length_array) - 1))),\n \"assigning variable res\", stan::model::index_uni(i),\n stan::model::index_omni());\n current_statement__ = 74;\n i = (i + 1);\n current_statement__ = 75;\n j = (j + length_array);\n }\n current_statement__ = 78;\n return res;\n } catch (const std::exception& e) {\n stan::lang::rethrow_located(e, locations_array__[current_statement__]);\n }\n}\nclass modelaa9c6a27673f_rbmi_mmrm final : public model_base_crtp<modelaa9c6a27673f_rbmi_mmrm> {\nprivate:\n int N;\n int P;\n int G;\n int n_visit;\n int n_pat;\n std::vector<int> pat_G;\n std::vector<int> pat_n_pt;\n std::vector<int> pat_n_visit;\n std::vector<std::vector<int>> pat_sigma_index;\n Eigen::Matrix<double,-1,1> y_data__;\n Eigen::Matrix<double,-1,-1> Q_data__;\n Eigen::Matrix<double,-1,-1> R_data__;\n std::vector<Eigen::Matrix<double,-1,-1>> Sigma_init;\n Eigen::Matrix<double,-1,-1> R_inverse_data__;\n Eigen::Map<Eigen::Matrix<double,-1,1>> y{nullptr, 0};\n Eigen::Map<Eigen::Matrix<double,-1,-1>> Q{nullptr, 0, 0};\n Eigen::Map<Eigen::Matrix<double,-1,-1>> R{nullptr, 0, 0};\n Eigen::Map<Eigen::Matrix<double,-1,-1>> R_inverse{nullptr, 0, 0};\npublic:\n ~modelaa9c6a27673f_rbmi_mmrm() {}\n modelaa9c6a27673f_rbmi_mmrm(stan::io::var_context& context__, unsigned int\n random_seed__ = 0, std::ostream*\n pstream__ = nullptr) : model_base_crtp(0) {\n int current_statement__ = 0;\n using local_scalar_t__ = double;\n boost::ecuyer1988 base_rng__ =\n stan::services::util::create_rng(random_seed__, 0);\n // suppress unused var warning\n (void) base_rng__;\n static constexpr const char* function__ =\n \"modelaa9c6a27673f_rbmi_mmrm_namespace::modelaa9c6a27673f_rbmi_mmrm\";\n // suppress unused var warning\n (void) function__;\n local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());\n // suppress unused var warning\n (void) DUMMY_VAR__;\n try {\n int pos__ = std::numeric_limits<int>::min();\n pos__ = 1;\n current_statement__ = 29;\n context__.validate_dims(\"data initialization\", \"N\", \"int\",\n std::vector<size_t>{});\n N = std::numeric_limits<int>::min();\n current_statement__ = 29;\n N = context__.vals_i(\"N\")[(1 - 1)];\n current_statement__ = 29;\n stan::math::check_greater_or_equal(function__, \"N\", N, 1);\n current_statement__ = 30;\n context__.validate_dims(\"data initialization\", \"P\", \"int\",\n std::vector<size_t>{});\n P = std::numeric_limits<int>::min();\n current_statement__ = 30;\n P = context__.vals_i(\"P\")[(1 - 1)];\n current_statement__ = 30;\n stan::math::check_greater_or_equal(function__, \"P\", P, 1);\n current_statement__ = 31;\n context__.validate_dims(\"data initialization\", \"G\", \"int\",\n std::vector<size_t>{});\n G = std::numeric_limits<int>::min();\n current_statement__ = 31;\n G = context__.vals_i(\"G\")[(1 - 1)];\n current_statement__ = 31;\n stan::math::check_greater_or_equal(function__, \"G\", G, 1);\n current_statement__ = 32;\n context__.validate_dims(\"data initialization\", \"n_visit\", \"int\",\n std::vector<size_t>{});\n n_visit = std::numeric_limits<int>::min();\n current_statement__ = 32;\n n_visit = context__.vals_i(\"n_visit\")[(1 - 1)];\n current_statement__ = 32;\n stan::math::check_greater_or_equal(function__, \"n_visit\", n_visit, 1);\n current_statement__ = 33;\n context__.validate_dims(\"data initialization\", \"n_pat\", \"int\",\n std::vector<size_t>{});\n n_pat = std::numeric_limits<int>::min();\n current_statement__ = 33;\n n_pat = context__.vals_i(\"n_pat\")[(1 - 1)];\n current_statement__ = 33;\n stan::math::check_greater_or_equal(function__, \"n_pat\", n_pat, 1);\n current_statement__ = 34;\n stan::math::validate_non_negative_index(\"pat_G\", \"n_pat\", n_pat);\n current_statement__ = 35;\n context__.validate_dims(\"data initialization\", \"pat_G\", \"int\",\n std::vector<size_t>{static_cast<size_t>(n_pat)});\n pat_G = std::vector<int>(n_pat, std::numeric_limits<int>::min());\n current_statement__ = 35;\n pat_G = context__.vals_i(\"pat_G\");\n current_statement__ = 35;\n stan::math::check_greater_or_equal(function__, \"pat_G\", pat_G, 1);\n current_statement__ = 36;\n stan::math::validate_non_negative_index(\"pat_n_pt\", \"n_pat\", n_pat);\n current_statement__ = 37;\n context__.validate_dims(\"data initialization\", \"pat_n_pt\", \"int\",\n std::vector<size_t>{static_cast<size_t>(n_pat)});\n pat_n_pt = std::vector<int>(n_pat, std::numeric_limits<int>::min());\n current_statement__ = 37;\n pat_n_pt = context__.vals_i(\"pat_n_pt\");\n current_statement__ = 37;\n stan::math::check_greater_or_equal(function__, \"pat_n_pt\", pat_n_pt, 1);\n current_statement__ = 38;\n stan::math::validate_non_negative_index(\"pat_n_visit\", \"n_pat\", n_pat);\n current_statement__ = 39;\n context__.validate_dims(\"data initialization\", \"pat_n_visit\", \"int\",\n std::vector<size_t>{static_cast<size_t>(n_pat)});\n pat_n_visit = std::vector<int>(n_pat, std::numeric_limits<int>::min());\n current_statement__ = 39;\n pat_n_visit = context__.vals_i(\"pat_n_visit\");\n current_statement__ = 39;\n stan::math::check_greater_or_equal(function__, \"pat_n_visit\",\n pat_n_visit, 1);\n current_statement__ = 40;\n stan::math::validate_non_negative_index(\"pat_sigma_index\", \"n_pat\",\n n_pat);\n current_statement__ = 41;\n stan::math::validate_non_negative_index(\"pat_sigma_index\", \"n_visit\",\n n_visit);\n current_statement__ = 42;\n context__.validate_dims(\"data initialization\", \"pat_sigma_index\",\n \"int\",\n std::vector<size_t>{static_cast<size_t>(n_pat),\n static_cast<size_t>(n_visit)});\n pat_sigma_index = std::vector<std::vector<int>>(n_pat,\n std::vector<int>(n_visit,\n std::numeric_limits<int>::min()));\n {\n std::vector<int> pat_sigma_index_flat__;\n current_statement__ = 42;\n pat_sigma_index_flat__ = context__.vals_i(\"pat_sigma_index\");\n current_statement__ = 42;\n pos__ = 1;\n current_statement__ = 42;\n for (int sym1__ = 1; sym1__ <= n_visit; ++sym1__) {\n current_statement__ = 42;\n for (int sym2__ = 1; sym2__ <= n_pat; ++sym2__) {\n current_statement__ = 42;\n stan::model::assign(pat_sigma_index,\n pat_sigma_index_flat__[(pos__ - 1)],\n \"assigning variable pat_sigma_index\",\n stan::model::index_uni(sym2__), stan::model::index_uni(sym1__));\n current_statement__ = 42;\n pos__ = (pos__ + 1);\n }\n }\n }\n current_statement__ = 42;\n stan::math::check_greater_or_equal(function__, \"pat_sigma_index\",\n pat_sigma_index, 1);\n current_statement__ = 43;\n stan::math::validate_non_negative_index(\"y\", \"N\", N);\n current_statement__ = 44;\n context__.validate_dims(\"data initialization\", \"y\", \"double\",\n std::vector<size_t>{static_cast<size_t>(N)});\n y_data__ = Eigen::Matrix<double,-1,1>::Constant(N,\n std::numeric_limits<double>::quiet_NaN());\n new (&y) Eigen::Map<Eigen::Matrix<double,-1,1>>(y_data__.data(), N);\n {\n std::vector<local_scalar_t__> y_flat__;\n current_statement__ = 44;\n y_flat__ = context__.vals_r(\"y\");\n current_statement__ = 44;\n pos__ = 1;\n current_statement__ = 44;\n for (int sym1__ = 1; sym1__ <= N; ++sym1__) {\n current_statement__ = 44;\n stan::model::assign(y, y_flat__[(pos__ - 1)],\n \"assigning variable y\", stan::model::index_uni(sym1__));\n current_statement__ = 44;\n pos__ = (pos__ + 1);\n }\n }\n current_statement__ = 45;\n stan::math::validate_non_negative_index(\"Q\", \"N\", N);\n current_statement__ = 46;\n stan::math::validate_non_negative_index(\"Q\", \"P\", P);\n current_statement__ = 47;\n context__.validate_dims(\"data initialization\", \"Q\", \"double\",\n std::vector<size_t>{static_cast<size_t>(N), static_cast<size_t>(P)});\n Q_data__ = Eigen::Matrix<double,-1,-1>::Constant(N, P,\n std::numeric_limits<double>::quiet_NaN());\n new (&Q) Eigen::Map<Eigen::Matrix<double,-1,-1>>(Q_data__.data(), N, P);\n {\n std::vector<local_scalar_t__> Q_flat__;\n current_statement__ = 47;\n Q_flat__ = context__.vals_r(\"Q\");\n current_statement__ = 47;\n pos__ = 1;\n current_statement__ = 47;\n for (int sym1__ = 1; sym1__ <= P; ++sym1__) {\n current_statement__ = 47;\n for (int sym2__ = 1; sym2__ <= N; ++sym2__) {\n current_statement__ = 47;\n stan::model::assign(Q, Q_flat__[(pos__ - 1)],\n \"assigning variable Q\", stan::model::index_uni(sym2__),\n stan::model::index_uni(sym1__));\n current_statement__ = 47;\n pos__ = (pos__ + 1);\n }\n }\n }\n current_statement__ = 48;\n stan::math::validate_non_negative_index(\"R\", \"P\", P);\n current_statement__ = 49;\n stan::math::validate_non_negative_index(\"R\", \"P\", P);\n current_statement__ = 50;\n context__.validate_dims(\"data initialization\", \"R\", \"double\",\n std::vector<size_t>{static_cast<size_t>(P), static_cast<size_t>(P)});\n R_data__ = Eigen::Matrix<double,-1,-1>::Constant(P, P,\n std::numeric_limits<double>::quiet_NaN());\n new (&R) Eigen::Map<Eigen::Matrix<double,-1,-1>>(R_data__.data(), P, P);\n {\n std::vector<local_scalar_t__> R_flat__;\n current_statement__ = 50;\n R_flat__ = context__.vals_r(\"R\");\n current_statement__ = 50;\n pos__ = 1;\n current_statement__ = 50;\n for (int sym1__ = 1; sym1__ <= P; ++sym1__) {\n current_statement__ = 50;\n for (int sym2__ = 1; sym2__ <= P; ++sym2__) {\n current_statement__ = 50;\n stan::model::assign(R, R_flat__[(pos__ - 1)],\n \"assigning variable R\", stan::model::index_uni(sym2__),\n stan::model::index_uni(sym1__));\n current_statement__ = 50;\n pos__ = (pos__ + 1);\n }\n }\n }\n current_statement__ = 51;\n stan::math::validate_non_negative_index(\"Sigma_init\", \"G\", G);\n current_statement__ = 52;\n stan::math::validate_non_negative_index(\"Sigma_init\", \"n_visit\",\n n_visit);\n current_statement__ = 53;\n stan::math::validate_non_negative_index(\"Sigma_init\", \"n_visit\",\n n_visit);\n current_statement__ = 54;\n context__.validate_dims(\"data initialization\", \"Sigma_init\", \"double\",\n std::vector<size_t>{static_cast<size_t>(G),\n static_cast<size_t>(n_visit), static_cast<size_t>(n_visit)});\n Sigma_init = std::vector<Eigen::Matrix<double,-1,-1>>(G,\n Eigen::Matrix<double,-1,-1>::Constant(n_visit, n_visit,\n std::numeric_limits<double>::quiet_NaN()));\n {\n std::vector<local_scalar_t__> Sigma_init_flat__;\n current_statement__ = 54;\n Sigma_init_flat__ = context__.vals_r(\"Sigma_init\");\n current_statement__ = 54;\n pos__ = 1;\n current_statement__ = 54;\n for (int sym1__ = 1; sym1__ <= n_visit; ++sym1__) {\n current_statement__ = 54;\n for (int sym2__ = 1; sym2__ <= n_visit; ++sym2__) {\n current_statement__ = 54;\n for (int sym3__ = 1; sym3__ <= G; ++sym3__) {\n current_statement__ = 54;\n stan::model::assign(Sigma_init, Sigma_init_flat__[(pos__ - 1)],\n \"assigning variable Sigma_init\",\n stan::model::index_uni(sym3__),\n stan::model::index_uni(sym2__),\n stan::model::index_uni(sym1__));\n current_statement__ = 54;\n pos__ = (pos__ + 1);\n }\n }\n }\n }\n current_statement__ = 55;\n stan::math::validate_non_negative_index(\"R_inverse\", \"P\", P);\n current_statement__ = 56;\n stan::math::validate_non_negative_index(\"R_inverse\", \"P\", P);\n current_statement__ = 57;\n R_inverse_data__ = Eigen::Matrix<double,-1,-1>::Constant(P, P,\n std::numeric_limits<double>::quiet_NaN());\n new (&R_inverse)\n Eigen::Map<Eigen::Matrix<double,-1,-1>>(R_inverse_data__.data(), P,\n P);\n current_statement__ = 57;\n stan::model::assign(R_inverse, stan::math::inverse(R),\n \"assigning variable R_inverse\");\n current_statement__ = 58;\n stan::math::validate_non_negative_index(\"theta\", \"P\", P);\n current_statement__ = 59;\n stan::math::validate_non_negative_index(\"Sigma\", \"G\", G);\n current_statement__ = 60;\n stan::math::validate_non_negative_index(\"Sigma\", \"n_visit\", n_visit);\n current_statement__ = 60;\n stan::math::validate_non_negative_index(\"Sigma\", \"n_visit\", n_visit);\n current_statement__ = 61;\n stan::math::validate_non_negative_index(\"beta\", \"P\", P);\n } catch (const std::exception& e) {\n stan::lang::rethrow_located(e, locations_array__[current_statement__]);\n }\n num_params_r__ = P + (G * (n_visit + ((n_visit * (n_visit - 1)) / 2)));\n }\n inline std::string model_name() const final {\n return \"modelaa9c6a27673f_rbmi_mmrm\";\n }\n inline std::vector<std::string> model_compile_info() const noexcept {\n return std::vector<std::string>{\"stanc_version = stanc3 v2.32.2\",\n \"stancflags = --\"};\n }\n template <bool propto__, bool jacobian__, typename VecR, typename VecI,\n stan::require_vector_like_t<VecR>* = nullptr,\n stan::require_vector_like_vt<std::is_integral, VecI>* = nullptr>\n inline stan::scalar_type_t<VecR>\n log_prob_impl(VecR& params_r__, VecI& params_i__, std::ostream*\n pstream__ = nullptr) const {\n using T__ = stan::scalar_type_t<VecR>;\n using local_scalar_t__ = T__;\n T__ lp__(0.0);\n stan::math::accumulator<T__> lp_accum__;\n stan::io::deserializer<local_scalar_t__> in__(params_r__, params_i__);\n int current_statement__ = 0;\n local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());\n // suppress unused var warning\n (void) DUMMY_VAR__;\n static constexpr const char* function__ =\n \"modelaa9c6a27673f_rbmi_mmrm_namespace::log_prob\";\n // suppress unused var warning\n (void) function__;\n try {\n Eigen::Matrix<local_scalar_t__,-1,1> theta =\n Eigen::Matrix<local_scalar_t__,-1,1>::Constant(P, DUMMY_VAR__);\n current_statement__ = 1;\n theta = in__.template read<Eigen::Matrix<local_scalar_t__,-1,1>>(P);\n std::vector<Eigen::Matrix<local_scalar_t__,-1,-1>> Sigma =\n std::vector<Eigen::Matrix<local_scalar_t__,-1,-1>>(G,\n Eigen::Matrix<local_scalar_t__,-1,-1>::Constant(n_visit, n_visit,\n DUMMY_VAR__));\n current_statement__ = 2;\n Sigma = in__.template read_constrain_cov_matrix<\n std::vector<Eigen::Matrix<local_scalar_t__,-1,-1>>,\n jacobian__>(lp__, G, n_visit);\n {\n int data_start_row = std::numeric_limits<int>::min();\n current_statement__ = 4;\n data_start_row = 1;\n current_statement__ = 5;\n stan::math::validate_non_negative_index(\"mu\", \"N\", N);\n Eigen::Matrix<local_scalar_t__,-1,1> mu =\n Eigen::Matrix<local_scalar_t__,-1,1>::Constant(N, DUMMY_VAR__);\n current_statement__ = 6;\n stan::model::assign(mu, stan::math::multiply(Q, theta),\n \"assigning variable mu\");\n current_statement__ = 9;\n for (int g = 1; g <= G; ++g) {\n current_statement__ = 7;\n lp_accum__.add(stan::math::inv_wishart_lpdf<propto__>(\n stan::model::rvalue(Sigma, \"Sigma\",\n stan::model::index_uni(g)), (n_visit + 2),\n stan::model::rvalue(Sigma_init, \"Sigma_init\",\n stan::model::index_uni(g))));\n }\n current_statement__ = 28;\n for (int i = 1; i <= n_pat; ++i) {\n int nvis = std::numeric_limits<int>::min();\n current_statement__ = 10;\n nvis = stan::model::rvalue(pat_n_visit, \"pat_n_visit\",\n stan::model::index_uni(i));\n int npt = std::numeric_limits<int>::min();\n current_statement__ = 11;\n npt = stan::model::rvalue(pat_n_pt, \"pat_n_pt\",\n stan::model::index_uni(i));\n int g = std::numeric_limits<int>::min();\n current_statement__ = 12;\n g = stan::model::rvalue(pat_G, \"pat_G\", stan::model::index_uni(i));\n current_statement__ = 13;\n stan::math::validate_non_negative_index(\"sig_index\", \"nvis\", nvis);\n std::vector<int> sig_index =\n std::vector<int>(nvis, std::numeric_limits<int>::min());\n current_statement__ = 14;\n stan::model::assign(sig_index,\n stan::model::rvalue(pat_sigma_index, \"pat_sigma_index\",\n stan::model::index_uni(i), stan::model::index_min_max(1, nvis)),\n \"assigning variable sig_index\");\n current_statement__ = 15;\n stan::math::validate_non_negative_index(\"sig\", \"nvis\", nvis);\n current_statement__ = 16;\n stan::math::validate_non_negative_index(\"sig\", \"nvis\", nvis);\n Eigen::Matrix<local_scalar_t__,-1,-1> sig =\n Eigen::Matrix<local_scalar_t__,-1,-1>::Constant(nvis, nvis,\n DUMMY_VAR__);\n current_statement__ = 17;\n stan::model::assign(sig,\n stan::model::rvalue(\n stan::model::rvalue(Sigma, \"Sigma\", stan::model::index_uni(g)),\n \"Sigma[g]\", stan::model::index_multi(sig_index),\n stan::model::index_multi(sig_index)), \"assigning variable sig\");\n int data_stop_row = std::numeric_limits<int>::min();\n current_statement__ = 18;\n data_stop_row = (data_start_row + ((nvis * npt) - 1));\n current_statement__ = 19;\n stan::math::validate_non_negative_index(\"y_obs\", \"npt\", npt);\n current_statement__ = 20;\n stan::math::validate_non_negative_index(\"y_obs\", \"nvis\", nvis);\n std::vector<Eigen::Matrix<local_scalar_t__,-1,1>> y_obs =\n std::vector<Eigen::Matrix<local_scalar_t__,-1,1>>(npt,\n Eigen::Matrix<local_scalar_t__,-1,1>::Constant(nvis,\n DUMMY_VAR__));\n current_statement__ = 21;\n stan::model::assign(y_obs,\n to_vector_of_arrays(\n stan::model::rvalue(y, \"y\",\n stan::model::index_min_max(data_start_row, data_stop_row)),\n nvis, pstream__), \"assigning variable y_obs\");\n current_statement__ = 22;\n stan::math::validate_non_negative_index(\"mu_obs\", \"npt\", npt);\n current_statement__ = 23;\n stan::math::validate_non_negative_index(\"mu_obs\", \"nvis\", nvis);\n std::vector<Eigen::Matrix<local_scalar_t__,-1,1>> mu_obs =\n std::vector<Eigen::Matrix<local_scalar_t__,-1,1>>(npt,\n Eigen::Matrix<local_scalar_t__,-1,1>::Constant(nvis,\n DUMMY_VAR__));\n current_statement__ = 24;\n stan::model::assign(mu_obs,\n to_vector_of_arrays(\n stan::model::rvalue(mu, \"mu\",\n stan::model::index_min_max(data_start_row, data_stop_row)),\n nvis, pstream__), \"assigning variable mu_obs\");\n current_statement__ = 25;\n lp_accum__.add(stan::math::multi_normal_lpdf<propto__>(y_obs,\n mu_obs, sig));\n current_statement__ = 26;\n data_start_row = (data_stop_row + 1);\n }\n }\n } catch (const std::exception& e) {\n stan::lang::rethrow_located(e, locations_array__[current_statement__]);\n }\n lp_accum__.add(lp__);\n return lp_accum__.sum();\n }\n template <typename RNG, typename VecR, typename VecI, typename VecVar,\n stan::require_vector_like_vt<std::is_floating_point,\n VecR>* = nullptr, stan::require_vector_like_vt<std::is_integral,\n VecI>* = nullptr, stan::require_vector_vt<std::is_floating_point,\n VecVar>* = nullptr>\n inline void\n write_array_impl(RNG& base_rng__, VecR& params_r__, VecI& params_i__,\n VecVar& vars__, const bool\n emit_transformed_parameters__ = true, const bool\n emit_generated_quantities__ = true, std::ostream*\n pstream__ = nullptr) const {\n using local_scalar_t__ = double;\n stan::io::deserializer<local_scalar_t__> in__(params_r__, params_i__);\n stan::io::serializer<local_scalar_t__> out__(vars__);\n static constexpr bool propto__ = true;\n // suppress unused var warning\n (void) propto__;\n double lp__ = 0.0;\n // suppress unused var warning\n (void) lp__;\n int current_statement__ = 0;\n stan::math::accumulator<double> lp_accum__;\n local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());\n // suppress unused var warning\n (void) DUMMY_VAR__;\n constexpr bool jacobian__ = false;\n static constexpr const char* function__ =\n \"modelaa9c6a27673f_rbmi_mmrm_namespace::write_array\";\n // suppress unused var warning\n (void) function__;\n try {\n Eigen::Matrix<double,-1,1> theta =\n Eigen::Matrix<double,-1,1>::Constant(P,\n std::numeric_limits<double>::quiet_NaN());\n current_statement__ = 1;\n theta = in__.template read<Eigen::Matrix<local_scalar_t__,-1,1>>(P);\n std::vector<Eigen::Matrix<double,-1,-1>> Sigma =\n std::vector<Eigen::Matrix<double,-1,-1>>(G,\n Eigen::Matrix<double,-1,-1>::Constant(n_visit, n_visit,\n std::numeric_limits<double>::quiet_NaN()));\n current_statement__ = 2;\n Sigma = in__.template read_constrain_cov_matrix<\n std::vector<Eigen::Matrix<local_scalar_t__,-1,-1>>,\n jacobian__>(lp__, G, n_visit);\n out__.write(theta);\n for (int sym1__ = 1; sym1__ <= n_visit; ++sym1__) {\n for (int sym2__ = 1; sym2__ <= n_visit; ++sym2__) {\n for (int sym3__ = 1; sym3__ <= G; ++sym3__) {\n out__.write(stan::model::rvalue(Sigma, \"Sigma\",\n stan::model::index_uni(sym3__),\n stan::model::index_uni(sym2__),\n stan::model::index_uni(sym1__)));\n }\n }\n }\n if (stan::math::logical_negation(\n (stan::math::primitive_value(emit_transformed_parameters__) ||\n stan::math::primitive_value(emit_generated_quantities__)))) {\n return ;\n }\n if (stan::math::logical_negation(emit_generated_quantities__)) {\n return ;\n }\n Eigen::Matrix<double,-1,1> beta =\n Eigen::Matrix<double,-1,1>::Constant(P,\n std::numeric_limits<double>::quiet_NaN());\n current_statement__ = 3;\n stan::model::assign(beta, stan::math::multiply(R_inverse, theta),\n \"assigning variable beta\");\n out__.write(beta);\n } catch (const std::exception& e) {\n stan::lang::rethrow_located(e, locations_array__[current_statement__]);\n }\n }\n template <typename VecVar, typename VecI,\n stan::require_vector_t<VecVar>* = nullptr,\n stan::require_vector_like_vt<std::is_integral, VecI>* = nullptr>\n inline void\n unconstrain_array_impl(const VecVar& params_r__, const VecI& params_i__,\n VecVar& vars__, std::ostream* pstream__ = nullptr) const {\n using local_scalar_t__ = double;\n stan::io::deserializer<local_scalar_t__> in__(params_r__, params_i__);\n stan::io::serializer<local_scalar_t__> out__(vars__);\n int current_statement__ = 0;\n local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());\n // suppress unused var warning\n (void) DUMMY_VAR__;\n try {\n int pos__ = std::numeric_limits<int>::min();\n pos__ = 1;\n Eigen::Matrix<local_scalar_t__,-1,1> theta =\n Eigen::Matrix<local_scalar_t__,-1,1>::Constant(P, DUMMY_VAR__);\n current_statement__ = 1;\n stan::model::assign(theta,\n in__.read<Eigen::Matrix<local_scalar_t__,-1,1>>(P),\n \"assigning variable theta\");\n out__.write(theta);\n std::vector<Eigen::Matrix<local_scalar_t__,-1,-1>> Sigma =\n std::vector<Eigen::Matrix<local_scalar_t__,-1,-1>>(G,\n Eigen::Matrix<local_scalar_t__,-1,-1>::Constant(n_visit, n_visit,\n DUMMY_VAR__));\n current_statement__ = 2;\n for (int sym1__ = 1; sym1__ <= n_visit; ++sym1__) {\n current_statement__ = 2;\n for (int sym2__ = 1; sym2__ <= n_visit; ++sym2__) {\n current_statement__ = 2;\n for (int sym3__ = 1; sym3__ <= G; ++sym3__) {\n current_statement__ = 2;\n stan::model::assign(Sigma, in__.read<local_scalar_t__>(),\n \"assigning variable Sigma\", stan::model::index_uni(sym3__),\n stan::model::index_uni(sym2__), stan::model::index_uni(sym1__));\n }\n }\n }\n out__.write_free_cov_matrix(Sigma);\n } catch (const std::exception& e) {\n stan::lang::rethrow_located(e, locations_array__[current_statement__]);\n }\n }\n template <typename VecVar, stan::require_vector_t<VecVar>* = nullptr>\n inline void\n transform_inits_impl(const stan::io::var_context& context__, VecVar&\n vars__, std::ostream* pstream__ = nullptr) const {\n using local_scalar_t__ = double;\n stan::io::serializer<local_scalar_t__> out__(vars__);\n int current_statement__ = 0;\n local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());\n // suppress unused var warning\n (void) DUMMY_VAR__;\n try {\n current_statement__ = 1;\n context__.validate_dims(\"parameter initialization\", \"theta\", \"double\",\n std::vector<size_t>{static_cast<size_t>(P)});\n current_statement__ = 2;\n context__.validate_dims(\"parameter initialization\", \"Sigma\", \"double\",\n std::vector<size_t>{static_cast<size_t>(G),\n static_cast<size_t>(n_visit), static_cast<size_t>(n_visit)});\n int pos__ = std::numeric_limits<int>::min();\n pos__ = 1;\n Eigen::Matrix<local_scalar_t__,-1,1> theta =\n Eigen::Matrix<local_scalar_t__,-1,1>::Constant(P, DUMMY_VAR__);\n {\n std::vector<local_scalar_t__> theta_flat__;\n current_statement__ = 1;\n theta_flat__ = context__.vals_r(\"theta\");\n current_statement__ = 1;\n pos__ = 1;\n current_statement__ = 1;\n for (int sym1__ = 1; sym1__ <= P; ++sym1__) {\n current_statement__ = 1;\n stan::model::assign(theta, theta_flat__[(pos__ - 1)],\n \"assigning variable theta\", stan::model::index_uni(sym1__));\n current_statement__ = 1;\n pos__ = (pos__ + 1);\n }\n }\n out__.write(theta);\n std::vector<Eigen::Matrix<local_scalar_t__,-1,-1>> Sigma =\n std::vector<Eigen::Matrix<local_scalar_t__,-1,-1>>(G,\n Eigen::Matrix<local_scalar_t__,-1,-1>::Constant(n_visit, n_visit,\n DUMMY_VAR__));\n {\n std::vector<local_scalar_t__> Sigma_flat__;\n current_statement__ = 2;\n Sigma_flat__ = context__.vals_r(\"Sigma\");\n current_statement__ = 2;\n pos__ = 1;\n current_statement__ = 2;\n for (int sym1__ = 1; sym1__ <= n_visit; ++sym1__) {\n current_statement__ = 2;\n for (int sym2__ = 1; sym2__ <= n_visit; ++sym2__) {\n current_statement__ = 2;\n for (int sym3__ = 1; sym3__ <= G; ++sym3__) {\n current_statement__ = 2;\n stan::model::assign(Sigma, Sigma_flat__[(pos__ - 1)],\n \"assigning variable Sigma\", stan::model::index_uni(sym3__),\n stan::model::index_uni(sym2__),\n stan::model::index_uni(sym1__));\n current_statement__ = 2;\n pos__ = (pos__ + 1);\n }\n }\n }\n }\n out__.write_free_cov_matrix(Sigma);\n } catch (const std::exception& e) {\n stan::lang::rethrow_located(e, locations_array__[current_statement__]);\n }\n }\n inline void\n get_param_names(std::vector<std::string>& names__, const bool\n emit_transformed_parameters__ = true, const bool\n emit_generated_quantities__ = true) const {\n names__ = std::vector<std::string>{\"theta\", \"Sigma\"};\n if (emit_transformed_parameters__) {}\n if (emit_generated_quantities__) {\n std::vector<std::string> temp{\"beta\"};\n names__.reserve(names__.size() + temp.size());\n names__.insert(names__.end(), temp.begin(), temp.end());\n }\n }\n inline void\n get_dims(std::vector<std::vector<size_t>>& dimss__, const bool\n emit_transformed_parameters__ = true, const bool\n emit_generated_quantities__ = true) const {\n dimss__ = std::vector<std::vector<size_t>>{std::vector<size_t>{static_cast<\n size_t>(P)},\n std::vector<size_t>{static_cast<size_t>(G),\n static_cast<size_t>(n_visit), static_cast<size_t>(n_visit)}};\n if (emit_transformed_parameters__) {}\n if (emit_generated_quantities__) {\n std::vector<std::vector<size_t>>\n temp{std::vector<size_t>{static_cast<size_t>(P)}};\n dimss__.reserve(dimss__.size() + temp.size());\n dimss__.insert(dimss__.end(), temp.begin(), temp.end());\n }\n }\n inline void\n constrained_param_names(std::vector<std::string>& param_names__, bool\n emit_transformed_parameters__ = true, bool\n emit_generated_quantities__ = true) const final {\n for (int sym1__ = 1; sym1__ <= P; ++sym1__) {\n param_names__.emplace_back(std::string() + \"theta\" + '.' +\n std::to_string(sym1__));\n }\n for (int sym1__ = 1; sym1__ <= n_visit; ++sym1__) {\n for (int sym2__ = 1; sym2__ <= n_visit; ++sym2__) {\n for (int sym3__ = 1; sym3__ <= G; ++sym3__) {\n param_names__.emplace_back(std::string() + \"Sigma\" + '.' +\n std::to_string(sym3__) + '.' + std::to_string(sym2__) + '.' +\n std::to_string(sym1__));\n }\n }\n }\n if (emit_transformed_parameters__) {}\n if (emit_generated_quantities__) {\n for (int sym1__ = 1; sym1__ <= P; ++sym1__) {\n param_names__.emplace_back(std::string() + \"beta\" + '.' +\n std::to_string(sym1__));\n }\n }\n }\n inline void\n unconstrained_param_names(std::vector<std::string>& param_names__, bool\n emit_transformed_parameters__ = true, bool\n emit_generated_quantities__ = true) const final {\n for (int sym1__ = 1; sym1__ <= P; ++sym1__) {\n param_names__.emplace_back(std::string() + \"theta\" + '.' +\n std::to_string(sym1__));\n }\n for (int sym1__ = 1; sym1__ <= (n_visit + ((n_visit * (n_visit - 1)) /\n 2)); ++sym1__) {\n for (int sym2__ = 1; sym2__ <= G; ++sym2__) {\n param_names__.emplace_back(std::string() + \"Sigma\" + '.' +\n std::to_string(sym2__) + '.' + std::to_string(sym1__));\n }\n }\n if (emit_transformed_parameters__) {}\n if (emit_generated_quantities__) {\n for (int sym1__ = 1; sym1__ <= P; ++sym1__) {\n param_names__.emplace_back(std::string() + \"beta\" + '.' +\n std::to_string(sym1__));\n }\n }\n }\n inline std::string get_constrained_sizedtypes() const {\n return std::string(\"[{\\\"name\\\":\\\"theta\\\",\\\"type\\\":{\\\"name\\\":\\\"vector\\\",\\\"length\\\":\" + std::to_string(P) + \"},\\\"block\\\":\\\"parameters\\\"},{\\\"name\\\":\\\"Sigma\\\",\\\"type\\\":{\\\"name\\\":\\\"array\\\",\\\"length\\\":\" + std::to_string(G) + \",\\\"element_type\\\":{\\\"name\\\":\\\"matrix\\\",\\\"rows\\\":\" + std::to_string(n_visit) + \",\\\"cols\\\":\" + std::to_string(n_visit) + \"}},\\\"block\\\":\\\"parameters\\\"},{\\\"name\\\":\\\"beta\\\",\\\"type\\\":{\\\"name\\\":\\\"vector\\\",\\\"length\\\":\" + std::to_string(P) + \"},\\\"block\\\":\\\"generated_quantities\\\"}]\");\n }\n inline std::string get_unconstrained_sizedtypes() const {\n return std::string(\"[{\\\"name\\\":\\\"theta\\\",\\\"type\\\":{\\\"name\\\":\\\"vector\\\",\\\"length\\\":\" + std::to_string(P) + \"},\\\"block\\\":\\\"parameters\\\"},{\\\"name\\\":\\\"Sigma\\\",\\\"type\\\":{\\\"name\\\":\\\"array\\\",\\\"length\\\":\" + std::to_string(G) + \",\\\"element_type\\\":{\\\"name\\\":\\\"vector\\\",\\\"length\\\":\" + std::to_string((n_visit + ((n_visit * (n_visit - 1)) /2))) + \"}},\\\"block\\\":\\\"parameters\\\"},{\\\"name\\\":\\\"beta\\\",\\\"type\\\":{\\\"name\\\":\\\"vector\\\",\\\"length\\\":\" + std::to_string(P) + \"},\\\"block\\\":\\\"generated_quantities\\\"}]\");\n }\n // Begin method overload boilerplate\n template <typename RNG> inline void\n write_array(RNG& base_rng, Eigen::Matrix<double,-1,1>& params_r,\n Eigen::Matrix<double,-1,1>& vars, const bool\n emit_transformed_parameters = true, const bool\n emit_generated_quantities = true, std::ostream*\n pstream = nullptr) const {\n const size_t num_params__ = (P + ((G * n_visit) * n_visit));\n const size_t num_transformed = emit_transformed_parameters * (0);\n const size_t num_gen_quantities = emit_generated_quantities * (P);\n const size_t num_to_write = num_params__ + num_transformed +\n num_gen_quantities;\n std::vector<int> params_i;\n vars = Eigen::Matrix<double,-1,1>::Constant(num_to_write,\n std::numeric_limits<double>::quiet_NaN());\n write_array_impl(base_rng, params_r, params_i, vars,\n emit_transformed_parameters, emit_generated_quantities, pstream);\n }\n template <typename RNG> inline void\n write_array(RNG& base_rng, std::vector<double>& params_r, std::vector<int>&\n params_i, std::vector<double>& vars, bool\n emit_transformed_parameters = true, bool\n emit_generated_quantities = true, std::ostream*\n pstream = nullptr) const {\n const size_t num_params__ = (P + ((G * n_visit) * n_visit));\n const size_t num_transformed = emit_transformed_parameters * (0);\n const size_t num_gen_quantities = emit_generated_quantities * (P);\n const size_t num_to_write = num_params__ + num_transformed +\n num_gen_quantities;\n vars = std::vector<double>(num_to_write,\n std::numeric_limits<double>::quiet_NaN());\n write_array_impl(base_rng, params_r, params_i, vars,\n emit_transformed_parameters, emit_generated_quantities, pstream);\n }\n template <bool propto__, bool jacobian__, typename T_> inline T_\n log_prob(Eigen::Matrix<T_,-1,1>& params_r, std::ostream* pstream = nullptr) const {\n Eigen::Matrix<int,-1,1> params_i;\n return log_prob_impl<propto__, jacobian__>(params_r, params_i, pstream);\n }\n template <bool propto__, bool jacobian__, typename T_> inline T_\n log_prob(std::vector<T_>& params_r, std::vector<int>& params_i,\n std::ostream* pstream = nullptr) const {\n return log_prob_impl<propto__, jacobian__>(params_r, params_i, pstream);\n }\n inline void\n transform_inits(const stan::io::var_context& context,\n Eigen::Matrix<double,-1,1>& params_r, std::ostream*\n pstream = nullptr) const final {\n std::vector<double> params_r_vec(params_r.size());\n std::vector<int> params_i;\n transform_inits(context, params_i, params_r_vec, pstream);\n params_r = Eigen::Map<Eigen::Matrix<double,-1,1>>(params_r_vec.data(),\n params_r_vec.size());\n }\n inline void\n transform_inits(const stan::io::var_context& context, std::vector<int>&\n params_i, std::vector<double>& vars, std::ostream*\n pstream__ = nullptr) const {\n vars.resize(num_params_r__);\n transform_inits_impl(context, vars, pstream__);\n }\n inline void\n unconstrain_array(const std::vector<double>& params_constrained,\n std::vector<double>& params_unconstrained, std::ostream*\n pstream = nullptr) const {\n const std::vector<int> params_i;\n params_unconstrained = std::vector<double>(num_params_r__,\n std::numeric_limits<double>::quiet_NaN());\n unconstrain_array_impl(params_constrained, params_i,\n params_unconstrained, pstream);\n }\n inline void\n unconstrain_array(const Eigen::Matrix<double,-1,1>& params_constrained,\n Eigen::Matrix<double,-1,1>& params_unconstrained,\n std::ostream* pstream = nullptr) const {\n const std::vector<int> params_i;\n params_unconstrained = Eigen::Matrix<double,-1,1>::Constant(num_params_r__,\n std::numeric_limits<double>::quiet_NaN());\n unconstrain_array_impl(params_constrained, params_i,\n params_unconstrained, pstream);\n }\n};\n}\nusing stan_model = modelaa9c6a27673f_rbmi_mmrm_namespace::modelaa9c6a27673f_rbmi_mmrm;\n#ifndef USING_R\n// Boilerplate\nstan::model::model_base&\nnew_model(stan::io::var_context& data_context, unsigned int seed,\n std::ostream* msg_stream) {\n stan_model* m = new stan_model(data_context, seed, msg_stream);\n return *m;\n}\nstan::math::profile_map& get_stan_profile_data() {\n return modelaa9c6a27673f_rbmi_mmrm_namespace::profiles__;\n}\n#endif\n#endif"), mk_cppmodule = function (object) { prep_call_sampler(object) model_cppname <- object@model_cpp$model_cppname mod <- get("module", envir = object@dso@.CXXDSOMISC, inherits = FALSE) eval(call("$", mod, paste("stan_fit4", model_cppname, sep = ""))) }, dso = new("cxxdso", sig = list(fileaa9c5c0bb1fd = character(0)), dso_saved = TRUE, dso_filename = "fileaa9c5c0bb1fd", modulename = "stan_fit4modelaa9c6a27673f_rbmi_mmrm_mod", system = "x86_64, darwin20", cxxflags = "CXXFLAGS = -falign-functions=64 -Wall -g -O2 $(LTO)", .CXXDSOMISC = <environment>)), data = list(N = 340L, P = 8L, G = 1L, n_visit = 3L, n_pat = 2L, pat_G = c(1, 1), pat_n_pt = c(60, 80), pat_n_visit = c(3, 2), pat_sigma_index = list( c(1, 2, 3), c(1, 2, 999)), y = c(0.574036883477536, 0.139105398171835, 0.176977232131814, -1.16935503183362, -1.21591438062592, -1.08964625296442, -1.58121597188229, -1.03382417471284, -0.813329718686652, -0.659208380311965, -0.596830231476417, -0.428690642917848, 1.19340919052579, 1.19324384350827, 1.14076998750238, 0.508301722783451, 0.285101803715272, -0.080275904172036, 0.420951505580432, 0.729295857616047, 0.606493590677628, 1.36133467580718, 0.785097805390841, 0.850728879541265, 1.59518647362531, 1.99123113996823, 1.62317993728234, -1.01500989395391, -1.29590087043236, -1.09167466441661, -1.92326503149754, -1.6438232242331, -2.09494484800456, -0.916339955699448, -1.21279752527853, -0.824654473194631, 0.160587572122124, 0.533787595446638, 0.509785626882552, 0.625441265387049, 0.869595429439695, 0.606754094352448, 0.416887452319262, 0.0527870943089152, -0.290746253299322, -0.0405562348050075, 0.138363916063878, 0.199662379261492, -0.0292589587575427, 0.184625776371717, 0.596974269230419, 0.460701441061857, 0.606107749594927, 0.675147663778399, 1.66002871020651, 1.84142634216536, 1.99191103632866, 1.10904044059033, 0.414249192109747, 0.672496352831966, 0.789592735008045, 0.677931435319177, 0.543093801624015, -0.899674446856585, -0.98731077385762, -1.31298117495064, -0.790921720329644, -0.407036721173524, -0.221080281874233, 1.43428989449746, 1.46018790177788, 1.29835543116011, 1.93804574039781, 1.67293427649682, 1.58849553103043, -1.01450332611738, -0.842813717586719, -0.629379340054354, -0.267415091390975, -0.657913697591214, -0.595234267484234, 0.120679904254403, 0.178945058091685, 0.258466389498271, -0.449519107207705, -0.418311943730978, -0.607987725151117, 0.475470586549514, 0.465961733342575, 0.628255437173784, -0.269181976386959, -0.489283917847538, -0.480553495290893, 0.0126097945506521, 0.188893941324742, -0.00785768753235569, -1.64359171150621, -1.51956953580399, -1.83648421907106, -0.334174407608112, 0.173961399031304, 0.512090615451255, 0.815812442864812, 1.00796572845926, 1.08799807363422, -0.499836503819652, -0.194917201294718, -0.347531934556113, 0.0967719422298859, 0.0448422069391228, -0.0380229372755732, 0.89027308837742, 0.923859648959303, 0.693773508152388, 0.0200206385163762, 0.139715592410514, 0.355610280019225, -0.554244284647314, -0.337491541079209, -0.0476717323926215, 1.74097877156221, 1.74584951452271, 1.87453426708686, 1.53665210908614, 1.27351842521603, 0.782842422447314, 0.780226954526566, 0.533531436107968, 0.471968788026106, -1.50987360873711, -1.4213975714265, -1.32130245857978, -1.53643170343886, -1.96117485895016, -1.63868589576792, -0.0924369697453538, -0.236384532458898, -0.416943554508815, 0.524748546809872, 1.01066900867255, 1.03925121395702, -1.47575422517021, -1.84351894914451, -1.96406505049471, -0.958450708965594, -1.00073327233577, -1.15403786283949, 1.30985852929418, 0.677518704253333, 0.633123295459129, -1.00859795137567, -0.486351090482834, -0.629987247106474, -0.703803757755735, -0.423015811624416, -0.68260822611802, 1.29970709660456, 0.950166424412677, 0.700867866424596, 0.885973022497435, 0.69557127871005, 0.683167942482159, -0.448047722773281, -0.345122636504496, -0.44919578758697, -0.350389882825743, 0.218778679169879, 0.460591876155823, -1.13643984701411, -1.37594847426891, -1.38924924598483, 1.05497189150375, 1.44213159591197, 1.44244341883156, 0.989297127175544, 1.28938706033795, 0.974021459136989, 0.0997949939225535, 0.0977838256866348, 0.152771705034039, 0.0563099671794369, -0.109302500409893, -0.770343945290974, -0.840203655711169, -1.06621935574191, -0.818506844251997, 0.483309359293258, -0.077312553568267, -1.82787954030716, -1.51850241691396, 0.385765772873327, 0.318930612816827, 0.0529827480329966, -0.256048504100078, 0.755705549841678, 0.841931688341897, 0.712085742232759, 0.644772892574503, -1.14155231656161, -1.4428233579315, -1.05021841671635, -1.39451198199354, -0.267077536825949, -0.707269786974737, -1.45623361118695, -1.14058269087304, -0.768761960571674, -0.570612606157713, -0.764238909934079, -1.27539783051645, -0.845195950602966, -1.35937750037253, -0.403679174325962, -0.181315302498918, -0.138311056457262, -0.0706441028516794, 0.508084112959793, 0.620362254164868, 0.480866966820087, 0.576196611808272, -1.14455494641288, -1.35096340386058, 0.550675910279237, 0.825037960508207, 1.61286207896739, 1.67916701468662, -0.0647658829962172, -0.266749386462743, -0.282320438961451, -0.774105813761362, -0.361641848210371, -0.0216017435394257, 1.42387705128602, 1.23207105462671, 0.265776870737417, 0.515102201270055, 0.771782662506529, 0.702063509689375, 0.616876705707196, 0.789738509590256, 0.971362277748469, 0.738026162904697, 0.534226435971703, 0.045703700581884, -1.14177405496358, -0.787101784899203, -0.872756204009667, -1.09360825060612, -0.968020452649131, -0.801211978871774, 1.52141486581132, 1.40439441624722, 1.79137191952656, 1.47694519657773, -1.9360028677803, -1.12357338931634, -0.442997879839818, -0.564318885154016, 0.000626204404452472, -0.27075649131444, -2.48336093478472, -1.92707335118885, 0.366175672975573, -0.232532349854313, 0.445155531890445, 0.769878306810777, -1.60791560029354, -1.71327349294041, -0.296398526983666, -0.367067711718188, -0.838340929698702, -1.43768210681618, -0.14561552296282, -0.0941334404913737, -0.0164394933059202, 0.0218434792651633, 1.07286404251878, 1.18723665035218, -0.138595585019409, 0.0131822076110258, -0.399908316171867, -0.868639920226593, 0.7981208661895, 0.719218310535082, 0.914826810253791, 0.705606688741132, 2.27692308241729, 2.20307732877354, -1.38708681157136, -1.25624461004343, 0.0942383865047231, 0.619729596216058, -1.97029658377716, -2.12452350098981, -0.546587071142227, -0.965112515688056, -0.0103825230207238, 0.275006295304356, 1.39505387642203, 1.13837400657299, 0.653655010243044, 0.592910348706071, 2.26190956828622, 2.20569250608316, -0.979542016063513, -0.948366938019486, 0.0839816881469963, 0.496574287039973, 0.547933703350616, 0.492939941225062, -0.375076840950679, -0.483574365532446, 1.3297009420447, 1.69844054391049, -1.13265693702497, -0.849277428071457, -1.13312754672772, -0.666195857133899, 0.459008637041986, 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0.00222305092368008, 0.00332949514226805, -0.246165884693855, 0, -0.0972018727205666, -0.0932785531887564, -0.0803448339241552, 0.191330027057476, -0.00444610184736015, -0.00665899028453591, 0.0732625048297967, -0.175220044568803), Sigma_init = list( c(0.135928026518976, 0.0348523498539469, 0.00728419213566572, 0.0348523498539469, 0.0345033646543252, 0.00223741352686764, 0.00728419213566572, 0.00223741352686764, 0.0129045826930864 ))), pars = c("beta", "Sigma"), chains = 1, warmup = 200, thin = 2, iter = 204, init = list(list(theta = c(-5.9502531301265e-05, -0.745820423193819, -0.00612121696522099, 0.00358076868728952, 0.552831864322152, -0.281246666814181, -0.00509794025644147, -0.00632342582509301), sigma = list(Placebo = c(0.135928026518976, 0.0348523498539469, 0.00728419213566572, 0.0348523498539469, 0.0345033646543252, 0.00223741352686764, 0.00728419213566572, 0.00223741352686764, 0.0129045826930864), TRT = c(0.135928026518976, 0.0348523498539469, 0.00728419213566572, 0.0348523498539469, 0.0345033646543252, 0.00223741352686764, 0.00728419213566572, 0.00223741352686764, 0.0129045826930864)))), refresh = 0, seed = 2053082391L)
11: do.call(rstan::sampling, sampling_args)
12: doWithOneRestart(return(expr), restart)
13: withOneRestart(expr, restarts[[1L]])
14: withRestarts(expr, muffleStop = function() list())
15: withCallingHandlers(withRestarts(expr, muffleStop = function() list()), message = function(m) { env$message <- c(env$message, m$message) invokeRestart("muffleMessage") }, warning = function(w) { env$warning <- c(env$warning, w$message) invokeRestart("muffleWarning") }, error = function(e) { env$error <- c(env$error, e$message) invokeRestart("muffleStop") })
16: record({ do.call(rstan::sampling, sampling_args)})
17: fit_mcmc(designmat = model_df_scaled[, -1, drop = FALSE], outcome = model_df_scaled[, 1, drop = TRUE], group = data2[[vars$group]], visit = data2[[vars$visit]], subjid = data2[[vars$subjid]], method = method, quiet = quiet)
18: draws.bayes(d$dat, d$dat_ice, d$vars, meth, quiet = TRUE)
19: draws(d$dat, d$dat_ice, d$vars, meth, quiet = TRUE)
20: withCallingHandlers(expr, warning = function(w) if (inherits(w, classes)) tryInvokeRestart("muffleWarning"))
21: suppressWarnings({ draws(d$dat, d$dat_ice, d$vars, meth, quiet = TRUE)})
22: eval(code, test_env)
23: eval(code, test_env)
24: withCallingHandlers({ eval(code, test_env) if (!handled && !is.null(test)) { skip_empty() }}, expectation = handle_expectation, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error)
25: doTryCatch(return(expr), name, parentenv, handler)
26: tryCatchOne(expr, names, parentenv, handlers[[1L]])
27: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
28: doTryCatch(return(expr), name, parentenv, handler)
29: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]), names[nh], parentenv, handlers[[nh]])
30: tryCatchList(expr, classes, parentenv, handlers)
31: tryCatch(withCallingHandlers({ eval(code, test_env) if (!handled && !is.null(test)) { skip_empty() }}, expectation = handle_expectation, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error), error = handle_fatal, skip = function(e) { })
32: test_code(desc, code, env = parent.frame(), default_reporter = local_interactive_reporter())
33: test_that("bayes", { set.seed(40123) d <- get_data(140) meth <- method_bayes(n_samples = 2, burn_in = 200, burn_between = 2) dobj <- suppressWarnings({ draws(d$dat, d$dat_ice, d$vars, meth, quiet = TRUE) }) standard_checks(dobj, d, meth) expect_length(dobj$samples, 2) expect_true(all(vapply(dobj$samples, function(x) all(x$ids == levels(d$dat$id)), logical(1))))})
34: eval(code, test_env)
35: eval(code, test_env)
36: withCallingHandlers({ eval(code, test_env) if (!handled && !is.null(test)) { skip_empty() }}, expectation = handle_expectation, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error)
37: doTryCatch(return(expr), name, parentenv, handler)
38: tryCatchOne(expr, names, parentenv, handlers[[1L]])
39: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
40: doTryCatch(return(expr), name, parentenv, handler)
41: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]), names[nh], parentenv, handlers[[nh]])
42: tryCatchList(expr, classes, parentenv, handlers)
43: tryCatch(withCallingHandlers({ eval(code, test_env) if (!handled && !is.null(test)) { skip_empty() }}, expectation = handle_expectation, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error), error = handle_fatal, skip = function(e) { })
44: test_code(test = NULL, code = exprs, env = env, default_reporter = StopReporter$new())
45: source_file(path, env = env(env), desc = desc, error_call = error_call)
46: FUN(X[[i]], ...)
47: lapply(test_paths, test_one_file, env = env, desc = desc, error_call = error_call)
48: doTryCatch(return(expr), name, parentenv, handler)
49: tryCatchOne(expr, names, parentenv, handlers[[1L]])
50: tryCatchList(expr, classes, parentenv, handlers)
51: tryCatch(code, testthat_abort_reporter = function(cnd) { cat(conditionMessage(cnd), "\n") NULL})
52: with_reporter(reporters$multi, lapply(test_paths, test_one_file, env = env, desc = desc, error_call = error_call))
53: test_files_serial(test_dir = test_dir, test_package = test_package, test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, desc = desc, load_package = load_package, error_call = error_call)
54: test_files(test_dir = path, test_paths = test_paths, test_package = package, reporter = reporter, load_helpers = load_helpers, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, load_package = load_package, parallel = parallel)
55: test_dir("testthat", package = package, reporter = reporter, ..., load_package = "installed")
56: test_check("rbmi")
An irrecoverable exception occurred. R is aborting now ...
Flavor: r-release-macos-x86_64