Package: peppwR 0.1.0

Dan MacLean

peppwR: Power Analysis for Phosphopeptide Abundance Hypothesis Test

Estimate best fit distributions and do power analysis for hypothesis tests on phosphopeptide abundance data.

Authors:Dan MacLean [aut, cre]

peppwR_0.1.0.tar.gz
peppwR_0.1.0.zip(r-4.7-any)peppwR_0.1.0.zip(r-4.6-any)peppwR_0.1.0.zip(r-4.5-any)
peppwR_0.1.0.tgz(r-4.6-any)peppwR_0.1.0.tgz(r-4.5-any)
peppwR_0.1.0.tar.gz(r-4.7-any)peppwR_0.1.0.tar.gz(r-4.6-any)
peppwR_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
peppwR/json (API)

# Install 'peppwR' in R:
install.packages('peppwR', repos = c('https://teammaclean.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/teammaclean/peppwr/issues

Pkgdown/docs site:https://teammaclean.github.io

On CRAN:

Conda:

phosphoproteomicsphosphoproteomics-dataproteomicsproteomics-data-analysis

5.23 score 21 scripts 311 downloads 27 exports 71 dependencies

Last updated from:32460de4ca. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develNOTE183
source / vignettesOK254
linux-releaseNOTE136
macos-releaseNOTE175
macos-oldrelNOTE198
windows-develNOTE110
windows-releaseNOTE82
windows-oldrelNOTE109
wasm-releaseOK223

Exports:compute_dataset_mnarcompute_missingnessfit_distributionsget_distribution_presetis_count_datanew_peppwr_fitsnew_peppwr_powerplot_density_overlayplot_missingnessplot_param_distributionplot_power_heatmapplot_power_vs_effectplot_qqpower_analysisrun_power_simrun_power_sim_empiricalrun_power_sim_fdrrun_power_sim_with_missingnesssimulate_empiricalsimulate_experimentsimulate_with_missingnesstest_bayes_ttest_bootstrap_ttest_rankprodtest_wilcoxonvalidate_peppwr_fitsvalidate_peppwr_power

Dependencies:actuarassertthatbbmlebdsmatrixclicowplotcpp11cvardplyrexpintextraDistrfarverfastICAfBasicsfGarchfitdistrplusgbutilsgenericsggplot2gluegssgtableGUILDSintervalsisobandlabelinglatticelifecyclelogitnormmagrittrMASSMatrixmvtnormnakagaminloptrnumDerivpillarpkgconfigpoilogpoweRlawpracmapurrrR6rbibutilsRColorBrewerRcppRcppArmadilloRcppParallelRdpackRfastrlangS7sadsscalesspatialstablediststringistringrsurvivaltibbletidyrtidyselecttimeDatetimeSeriesunivariateMLutf8vctrsVGAMviridisLitewithrzigg

Getting Started with peppwR
Introduction | Installation | Setup | Generate Synthetic Pilot Data | Fit Distributions | Visualize Distribution Fits | Assess Fit Quality | A Note on Sample Size and Distribution Detection | Power Analysis - Aggregate Mode | Finding Required Sample Size | Finding Power at Fixed N | Power Analysis - Per-Peptide Mode | Interpreting Per-Peptide Results | Summary | Next Steps
Rendered fromgetting-started.Rmdusingknitr::rmarkdown

Last update: 2026-02-06
Started: 2026-01-29

How to find the best fitting distribution
Rendered fromfind-fits.Rmdusingknitr::rmarkdown

Last update: 2024-04-29
Started: 2024-04-29

Readme and manuals

Help Manual

Help pageTopics
Compute dataset-level MNAR metriccompute_dataset_mnar
Compute missingness statistics for a vector of valuescompute_missingness
Fit distributions to peptide abundance datafit_distributions
Get distributions for a presetget_distribution_preset
Check if data appears to be count data (non-negative integers)is_count_data
Create a new peppwr_fits objectnew_peppwr_fits
Create a new peppwr_power objectnew_peppwr_power
Plot density overlay: observed histogram with fitted density curveplot_density_overlay
Plot missingness statisticsplot_missingness
Plot distribution of fitted parameters across peptidomeplot_param_distribution
Plot power heatmap: N x effect size gridplot_power_heatmap
Plot power vs effect size at fixed Nplot_power_vs_effect
Plot QQ plots for goodness-of-fitplot_qq
Plot method for peppwr_fitsplot.peppwr_fits
Plot method for peppwr_powerplot.peppwr_power
Power analysis for peptide abundance datapower_analysis
Power analysis with specified distribution (aggregate mode)power_analysis.character power_analysis.default
Power analysis for per-peptide mode using fitted distributionspower_analysis.peppwr_fits
Print method for peppwr_fitsprint.peppwr_fits
Print method for peppwr_powerprint.peppwr_power
Print method for summary.peppwr_fitsprint.summary.peppwr_fits
Print method for summary.peppwr_powerprint.summary.peppwr_power
Run power simulationrun_power_sim
Run power simulation using empirical bootstraprun_power_sim_empirical
Run FDR-aware power simulation for whole peptidomerun_power_sim_fdr
Run power simulation with missingnessrun_power_sim_with_missingness
Simulate experiment using empirical bootstrapsimulate_empirical
Simulate an experiment with control and treatment groupssimulate_experiment
Simulate experiment with realistic missingnesssimulate_with_missingness
Summary method for peppwr_fitssummary.peppwr_fits
Summary method for peppwr_powersummary.peppwr_power
Bayes factor t-testtest_bayes_t
Bootstrap-t testtest_bootstrap_t
Rank Products testtest_rankprod
Wilcoxon rank-sum testtest_wilcoxon
Validate a peppwr_fits objectvalidate_peppwr_fits
Validate a peppwr_power objectvalidate_peppwr_power