Package: depower 2026.1.30.999

depower: Power Analysis for Differential Expression Studies

Provides a convenient framework to simulate, test, power, and visualize data for differential expression studies with lognormal or negative binomial outcomes. Supported designs are two-sample comparisons of independent or dependent outcomes. Power may be summarized in the context of controlling the per-family error rate or family-wise error rate. Negative binomial methods are described in Yu, Fernandez, and Brock (2017) <doi:10.1186/s12859-017-1648-2> and Yu, Fernandez, and Brock (2020) <doi:10.1186/s12859-020-3541-7>.

Authors:Brett Klamer [aut, cre], Lianbo Yu [aut]

depower_2026.1.30.999.tar.gz
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depower_2026.1.30.999.tgz(r-4.6-any)depower_2026.1.30.999.tgz(r-4.5-any)
depower_2026.1.30.999.tar.gz(r-4.7-any)depower_2026.1.30.999.tar.gz(r-4.6-any)
depower_2026.1.30.999.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
depower/json (API)
NEWS

# Install 'depower' in R:

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

Bug tracker:https://bitbucket.org/bklamer/depower

On CRAN:

4.45 score 62 scripts 231 downloads 27 exports 74 dependencies

Last updated from:962a783745. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	231
source / vignettes	OK	230
linux-release-x86_64	OK	223
macos-release-arm64	OK	126
macos-oldrel-arm64	OK	130
windows-devel	OK	255
windows-release	OK	268
windows-oldrel	OK	213
wasm-release	OK	136

Exports:add_power_ci add_power_pi asymptotic eval_power_ci eval_power_pi glm_nb glmm_bnb glmm_poisson lrt_bnb lrt_nb mle_bnb_alt mle_bnb_null mle_nb_alt mle_nb_null nll_bnb_alt nll_bnb_null nll_nb_alt nll_nb_null power sim_bnb sim_log_lognormal sim_nb simulated t_test_paired t_test_welch wald_test_bnb wald_test_nb

Dependencies:backports BH boot broom callr cli colorspace cowplot cpp11 crayon Deriv doBy dplyr farver forecast fracdiff generics ggplot2 glmmTMB glue gtable isoband labeling lattice lifecycle lme4 lmtest magrittr MASS Matrix mgcv microbenchmark minqa modelr multidplyr mvnfast nlme nloptr nnet numDeriv pbkrtest pillar pkgconfig processx ps purrr qs2 R6 rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen RcppParallel Rdpack reformulas rlang S7 sandwich scales stringfish stringi stringr tibble tidyr tidyselect timeDate TMB urca utf8 vctrs viridisLite withr zoo

Help page	Topics
Add confidence intervals for power estimates	add_power_ci
Add Bayesian posterior predictive intervals for power estimates	add_power_pi
Test statistic distribution under the null	asymptotic distribution simulated
Evaluate confidence intervals for power estimates	eval_power_ci
Evaluate Bayesian posterior predictive intervals for power estimates	eval_power_pi
GLM for NB ratio of means	glm_nb
GLMM for BNB ratio of means	glmm_bnb
GLMM for Poisson ratio of means	glmm_poisson
Likelihood ratio test for BNB ratio of means	lrt_bnb
Likelihood ratio test for NB ratio of means	lrt_nb
MLE for BNB	mle_bnb mle_bnb_alt mle_bnb_null
MLE for NB	mle_nb mle_nb_alt mle_nb_null
Negative log-likelihood for BNB	nll_bnb nll_bnb_alt nll_bnb_null
Negative log-likelihood for NB	nll_nb nll_nb_alt nll_nb_null
Plot power objects	plot.depower
Simulated power	power
Simulate BNB data	sim_bnb
Simulate log-transformed lognormal data	sim_log_lognormal
Simulate NB data	sim_nb
Paired and one-sample t-Tests	t_test_paired
Welch's t-Test	t_test_welch
Wald test for BNB ratio of means	wald_test_bnb
Wald test for NB ratio of means	wald_test_nb

Package: depower 2026.1.30.999

depower: Power Analysis for Differential Expression Studies

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