peppwR 0.1.0

Initial CRAN release.

Core Features

• fit_distributions() fits candidate distributions (gamma, lognormal, normal, inverse Gaussian, inverse gamma) to peptide abundance data

• power_analysis() performs power analysis in two modes:

  • Aggregate mode: Specify distribution and parameters directly
  • Per-peptide mode: Use fitted distributions from pilot data

• Three analysis questions supported via find parameter:

  • find = "sample_size": What N do I need for target power?
  • find = "power": What’s my power at given N?
  • find = "effect_size": What’s the minimum detectable effect?

Statistical Tests

• Wilcoxon rank-sum test (test = "wilcoxon", default)
• Bootstrap-t test (test = "bootstrap_t")
• Bayes factor t-test (test = "bayes_t")

Missing Data Handling

• compute_missingness() calculates NA rates per peptide
• Dataset-level MNAR detection via abundance-missingness correlation
• simulate_with_missingness() incorporates missing data patterns in power simulations

FDR-Aware Mode

• apply_fdr = TRUE in per-peptide mode simulates whole-peptidome experiments with Benjamini-Hochberg correction
• Configurable prop_null for expected proportion of true nulls

Diagnostic Plots

• plot_density_overlay(): Observed histogram with fitted density curve
• plot_qq(): QQ plots for goodness-of-fit assessment
• plot_power_heatmap(): N x effect size power lookup grid
• plot_power_vs_effect(): Power sensitivity at fixed N
• plot_param_distribution(): Distribution of fit quality across peptidome
• plot_missingness(): NA rate distribution and abundance vs missingness

Empirical Bootstrap

• on_fit_failure = "empirical" option uses bootstrap resampling when parametric fitting fails