Package: stepjglm 0.0.1

stepjglm: Variable Selection for Joint Modeling of Mean and Dispersion

A Package for selecting variables for the joint modeling of mean and dispersion (including models for mixture experiments) based on hypothesis testing and the quality of model's fit. In each iteration of the selection process, a criterion for checking the goodness of fit is used as a filter for choosing the terms that will be evaluated by a hypothesis test. Pinto & Pereira (2021) <arxiv:2109.07978>.

Authors:Leandro A. Pereira [aut, cre], Edmilson R. Pinto [aut]

stepjglm_0.0.1.tar.gz
stepjglm_0.0.1.zip(r-4.7-any)stepjglm_0.0.1.zip(r-4.6-any)stepjglm_0.0.1.zip(r-4.5-any)
stepjglm_0.0.1.tgz(r-4.6-any)stepjglm_0.0.1.tgz(r-4.5-any)
stepjglm_0.0.1.tar.gz(r-4.7-any)stepjglm_0.0.1.tar.gz(r-4.6-any)
stepjglm_0.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
stepjglm/json (API)

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

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 576 downloads 1 exports 20 dependencies

Last updated from:7860f67810. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK139
source / vignettesOK170
linux-release-x86_64OK147
macos-release-arm64OK218
macos-oldrel-arm64OK140
windows-develOK87
windows-releaseOK121
windows-oldrelOK88
wasm-releaseOK132

Exports:stepjglm

Dependencies:bootdemingDerivlatticelme4MASSMatrixmcrminqanlmenloptrrbibutilsRcppRcppArmadilloRcppEigenRdpackreformulasrlangrobslopesrsq