This package is considered a duplicate. The official version of this package is found at:https://magronah.r-universe.dev/power.nb

Package: power.nb 0.1.0

Michael Agronah

power.nb: Power and Sample Size Calculation for Differential Abundance Microbiome Studies

Provides functions for estimating statistical power and required sample sizes in differential abundance microbiome studies using negative binomial models. The methods are based on Agronah and Bolker (2025) <doi:10.1371/journal.pone.0318820>. The package includes tools for simulation-based power analysis and sample size estimation using generalized additive models (GAMs), and visualization utilities for exploring the relationship between power, effect size, abundance, and sample size.

Authors:Michael Agronah [aut, cre], Ben Bolker [aut]

power.nb_0.1.0.tar.gz
power.nb_0.1.0.tar.gz(r-4.7-any)power.nb_0.1.0.tar.gz(r-4.6-any)
power.nb_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
power.nb/json (API)
NEWS

# Install 'power.nb' in R:

install.packages('power.nb', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/magronah/power.nb/issues

Pkgdown/docs site:https://michaelagronah.com

On CRAN:

2.70 score 1 scripts 25 exports 134 dependencies

Last updated from:82f9ba0273. Checks:4 OK. Indexed: no.

Target	Result	Time
linux-devel-x86_64	OK	300
source / vignettes	OK	380
linux-release-x86_64	OK	297
wasm-release	OK	196

Exports:contour_plot_fun countdata_sim_fun deseq_fun_est deseqfun dispersion_fit dispersion_fun dnormmix dnormmix0 filter_low_count gam_fit gen_parnames genmixpars logfoldchange_fit logfoldchange_sim_fun logmean_fit logmean_sim_fun myrnormmix optimal.comp polyfun power_fun_ss read_data rnormmix0 sample_size_ss_interp ss_solver uniroot_ss

Dependencies:abind askpass backports base64enc beeswarm BH Biobase BiocGenerics BiocParallel bslib cachem Cairo checkmate class classInt cli codetools cpp11 crosstalk curl data.table DBI DelayedArray DEoptim DESeq2 digest doParallel dplyr e1071 evaluate farver fastmap fitdistrplus fontawesome foreach formatR Formula formula.tools fs futile.logger futile.options generics GenomicRanges ggbeeswarm ggplot2 ggrastr glue gtable highr htmltools htmlwidgets httr IRanges isoband iterators jquerylib jsonlite kernlab KernSmooth knitr labeling lambda.r later latex2exp lattice lazyeval lifecycle locfit lubridate magrittr MASS Matrix MatrixGenerics matrixStats memoise metR mgcv mime minpack.lm mixtools nlme openssl operator.tools otel pillar pkgconfig plotly plyr png promises proxy purrr R6 ragg rappdirs RColorBrewer Rcpp RcppArmadillo rlang rmarkdown s2 S4Arrays S4Vectors S7 sass scales scam segmented Seqinfo sf snow SparseArray stringi stringr SummarizedExperiment survival sys systemfonts textshaping tibble tidyr tidyselect timechange tinytex units utf8 vctrs vipor viridisLite withr wk xfun XVector yaml

Power and Sample Size Estimation for Microbiome Analysis

Michael Agronah and Benjamin Bolker

Rendered fromstub.rmdusingknitr::rmarkdownon Jun 15 2026.

Last update: 2026-06-15
Started: 2026-06-15

Citation

Readme and manuals

Help Manual

Help page	Topics
Contour plot for showing predicted power	contour_plot_fun
Simulate Count Data for Microbiome Studies	countdata_sim_fun
Fold change and p-value estimations for simulations	deseq_fun_est
Estimate log fold changes using 'DESeq2'.	deseqfun
Fit the non-linear function to dispersion estimates	dispersion_fit
Calculate Dispersion for Microbiome Data	dispersion_fun
Density of a Normal Mixture Model	dnormmix
Density function for the mixture of Gaussian distributions	dnormmix0
Filter to remove low abundant taxa	filter_low_count
Title	gam_fit
Generate Parameter Names for Mixture Model	gen_parnames
generate normal mixture parameters (prob vector, mean vector, sd vector for a specified set of 'x' values (logmean)	genmixpars
Fit a mixture of Gaussian distributions to log fold change	logfoldchange_fit
Simulate Log Fold Change Values	logfoldchange_sim_fun
Fit a mixture of Gaussian Distributions to log mean count of taxa.	logmean_fit
Simulate Log Means for OTUs	logmean_sim_fun
Simulating from a mixture of Gaussian	myrnormmix
Objective function	nllfun
Computes the optimal number of gaussian components for log mean count	optimal.comp
General-purpose log-likelihood function, vectorized sum(pars*x^i)	polyfun
Fit a smooth power model for sample size estimation	power_fun_ss
Extract specified data from a list of datasets	read_data
general-purpose normal-mixture deviate generator: takes _matrices_ of probabilities, means, sds	rnormmix0
Estimate sample size required to achieve a target statistical power	sample_size_ss_interp
Solve for the sample size required to achieve a target statistical power	ss_solver
Sample Size estimation function using uniroot	uniroot_ss