Changes in version 0.1.0 (2026-03-30)                  

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