ppforest2 0.1.1

Packaging and build

• R: The R package now compiles the C++ core directly through Makevars instead of CMake, with no network access or downloaded dependencies at install time. Eigen is provided by RcppEigen; nlohmann/json and pcg headers are vendored under inst/include. This makes the package installable on CRAN’s offline build machines. (fmt and csv-parser, used only by the CLI, are no longer part of the R build.)
• R: Self-registering strategies are compiled directly into the shared object, removing the previous whole-archive linking workaround.
• R: Compiler flags for the direct build mirror the standalone C++ build — EIGEN_NO_AUTOMATIC_RESIZING on all platforms and EIGEN_DONT_VECTORIZE on Windows.
• Core: Replaced C++20 designated initializers and fixed member-initialization order in stats/GroupPartition and stats/Simulation so the code compiles warning-free under a strict C++17 GCC (-Wall -Wextra -pedantic).
• Core: A compile-time EIGEN_VERSION_AT_LEAST(3, 4, 0) guard fails the build with a clear message if an incompatible Eigen is supplied (e.g. via RcppEigen).
• make r-vendor-deps re-vendors the committed json/pcg headers after a version bump in core/Dependencies.cmake.

Documentation

• R: DESCRIPTION uses Authors@R and cites the projection-pursuit tree and forest references with DOIs.
• R: Examples for the parsnip and plot methods use \donttest with requireNamespace() guards instead of \dontrun, so they run under --run-donttest when the suggested packages are available.

CRAN

• Added cran-comments.md. The package passes R CMD check --as-cran with no errors or warnings; remaining notes (new submission, cosmetic pragmas in the vendored nlohmann/json headers) are documented for the reviewer.

ppforest2 0.1.0

New features

• Core: Projection-pursuit oblique decision trees and random forests for classification, using LDA/PDA optimization.
• Core: Random uniform variable selection per split for forest diversity.
• Core: Three variable importance measures: permuted (VI1), projections (VI2), and weighted projections (VI3).
• Core: Out-of-bag error and confusion matrix for forests, with bootstrap sample indices persisted for recomputation. oob_error is NA_real_ (R) / “not available” (CLI) when no observation has any out-of-bag tree.
• Core: Degenerate split detection when projection-pursuit cannot find a useful projection.
• Core: OpenMP multi-threaded forest training.
• Core: JSON serialization for trained models. Optional metrics fields use a uniform null-or-value representation so downstream tooling can distinguish “computed but empty” from other shapes without special-casing.
• Core: Cross-platform reproducibility — identical results for the same seed on Linux, macOS, and Windows, enforced by golden-file tests in CI.
• R: pptr() and pprf() with formula and matrix interfaces. Returned models carry an S3 class vector identifying both model type and mode (e.g. c("pprf_classification", "pprf", "ppmodel")).
• R: predict() returns group labels (type = "class") or vote proportions (type = "prob") for classification.
• R: summary() displays training and OOB confusion matrices.
• R: Lazy OOB accessors — oob_error(), oob_predictions(), oob_samples(), bag_samples(), permuted_importance(), weighted_importance() — compute from the training data stored on the model on first access and memoize in an environment cache, so training is fast and repeated access is free. oob_predictions() returns a factor with NA for rows with no OOB tree.
• R: Permuted variable importance may be negative; this is meaningful signal (“within noise”) rather than a sentinel, so callers should rely on the ranking rather than clipping at zero. Weighted projection importance is non-negative by construction.
• R: Degenerate split warnings when projection-pursuit cannot separate groups.
• R: save_json() and load_json() for model persistence.
• R: tidymodels/parsnip integration: pp_tree() and pp_rand_forest() model specifications.
• R: ggplot2 visualizations — tree diagrams, variable importance plots, projection histograms, and decision boundary plots.
• R: Bundled classification datasets: crab, crabs, fishcatch, glass, image, leukemia, lymphoma, NCI60, olive, parkinson, and wine. (Use datasets::iris from base R for iris examples.)
• CLI: train fits a tree or forest from CSV and saves as JSON.
• CLI: predict applies a saved model to new data.
• CLI: evaluate runs train/test evaluation with smart convergence.
• CLI: summarize displays model configuration, data summary, and metrics from a saved model JSON. --data recomputes metrics from training data.
• CLI: benchmark runs multi-scenario performance benchmarks with baseline comparison.
• CLI: serve exposes a saved model over HTTP — GET / returns the model summary as JSON (or an HTML dashboard for browsers showing configuration, training metrics, and variable importance), GET /health is a liveness probe, and POST /predict accepts a feature CSV and returns predictions (JSON for API clients, an HTML predictions page for browsers, with a Download CSV button and confusion matrix when the request CSV includes a response column). Results are cached in-memory with shareable ?id=… URLs; the dashboard binds to 127.0.0.1:8080 by default.

Experimental features

Regression support is included but untested in production workloads. API surface and defaults may change in future releases.

• Core: Regression training via a ByCutpoint grouping strategy that quantile-slices the continuous response, a MeanResponse leaf, and MinSize / MinVariance / CompositeStop (stop::any) stop rules.
• Core: Regression metrics — MSE, MAE, and R² — computed for training and out-of-bag predictions. Forest OOB error is reported as MSE.
• R: Regression auto-detected when y is numeric (not a factor). predict() returns a numeric vector (type = "response").
• R: Regression strategy wrappers: grouping_by_cutpoint(), leaf_mean_response(), stop_min_size(), stop_min_variance(), stop_any().
• R: summary() displays MSE / MAE / R² for regression models. oob_predictions() returns a numeric vector with NA_real_ for rows with no OOB tree.
• R: save_json() / load_json() preserve regression mode; parsnip pp_tree() / pp_rand_forest() accept mode = "regression".
• CLI: --mode classification|regression selects the training mode; regression reads the last CSV column as the continuous response. predict returns numeric predictions and MSE/MAE/R²; evaluate reports MSE for regression.
• R: Bundled regression dataset california_housing (20,433 × 9, predict median_house_value). For smaller regression examples use datasets::mtcars from base R.