Package: simdistr 1.0.1

Bernardo Sousa-Pinto

simdistr: Assessment of Data Trial Distributions According to the Carlisle-Stouffer Method

Assessment of the distributions of baseline continuous and categorical variables in randomised trials. This method is based on the Carlisle-Stouffer method with Monte Carlo simulations. It calculates p-values for each trial baseline variable, as well as combined p-values for each trial - these p-values measure how compatible are distributions of trials baseline variables with random sampling. This package also allows for graphically plotting the cumulative frequencies of computed p-values. Please note that code was partly adapted from Carlisle JB, Loadsman JA. (2017) <doi:10.1111/anae.13650>.

Authors:Bernardo Sousa-Pinto [aut, cre], Joao Julio Cerqueira [ctb], Cristina Costa-Santos [ctb], John B Carlisle [ctb], John A Loadsman [ctb], Armando Teixeira-Pinto [aut], Hernani Goncalves [aut]

simdistr_1.0.1.tar.gz
simdistr_1.0.1.zip(r-4.7)simdistr_1.0.1.zip(r-4.6)simdistr_1.0.1.zip(r-4.5)
simdistr_1.0.1.tgz(r-4.6-any)simdistr_1.0.1.tgz(r-4.5-any)
simdistr_1.0.1.tar.gz(r-4.7-any)simdistr_1.0.1.tar.gz(r-4.6-any)
simdistr_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
simdistr/json (API)
NEWS

# Install 'simdistr' in R:
install.packages('simdistr', repos = c('https://razvanazamfirei.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • example_trials - Data of baseline variables of seven randomised trials.

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 2 scripts 133 downloads 1 exports 0 dependencies

Last updated from:3d00296d21. Checks:9 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK95
source / vignettesOK109
linux-release-x86_64OK122
macos-release-arm64OK104
macos-oldrel-arm64OK116
windows-develOK63
windows-releaseOK86
windows-oldrelOK63
wasm-releaseOK83

Exports:sim_distr

Dependencies: