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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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.