Package: r-cran-parameters
Version: 0.21.5-1
Architecture: all
Maintainer: Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
Original-Maintainer: Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
Installed-Size: 2790
Depends: r-api-4.0, r-cran-bayestestr (>= 0.13.1), r-cran-datawizard (>= 0.9.1), r-cran-insight (>= 0.19.8)
Recommends: r-cran-aer, r-cran-afex, r-cran-bayesfactor, r-cran-bayesfm, r-cran-bbmle, r-cran-bh, r-cran-blme, r-cran-boot, r-cran-brglm2, r-cran-brms, r-cran-broom, r-cran-caic4, r-cran-car, r-cran-cardata, r-cran-clubsandwich, r-cran-cluster, r-cran-coda, r-cran-dbscan, r-cran-drr, r-cran-effectsize (>= 0.8.6), r-cran-emmeans (>= 1.7.0), r-cran-estimatr, r-cran-factoextra, r-cran-factominer, r-cran-fastica, r-cran-fpc, r-cran-gam, r-cran-gee, r-cran-geepack, r-cran-ggeffects (>= 1.3.2), r-cran-ggplot2, r-cran-glmmtmb, r-cran-gparotation, r-cran-haven, r-cran-httr, r-cran-hmisc, r-cran-knitr, r-cran-lavaan, r-cran-lme4, r-cran-lmertest, r-cran-lmtest, r-cran-logspline, r-cran-mass, r-cran-matrix, r-cran-mclogit, r-cran-mclust, r-cran-metafor, r-cran-mgcv, r-cran-mice, r-cran-multcomp, r-cran-nfactors, r-cran-nlme, r-cran-nnet, r-cran-openxlsx, r-cran-ordinal, r-cran-pbkrtest, r-cran-performance (>= 0.10.8), r-cran-plm, r-cran-poorman, r-cran-posterior, r-cran-pscl, r-cran-psych, r-cran-pvclust, r-cran-quantreg, r-cran-randomforest, r-cran-rmarkdown, r-cran-rms, r-cran-rstanarm, r-cran-sandwich, r-cran-survey, r-cran-survival, r-cran-testthat, r-cran-tidyselect, r-cran-tmb, r-cran-vgam, r-cran-withr
Suggests: r-cran-marginaleffects (>= 0.16.0)
Section: gnu-r
Priority: optional
Homepage: https://cran.r-project.org/package=parameters
Description: GNU R processing of model parameters
 Utilities for processing the parameters of various statistical models.
 Beyond computing p values, CIs, and other indices for a wide variety of
 models (see support list of insight; Lüdecke, Waggoner & Makowski (2019)
 <doi:10.21105/joss.01412>), this package implements features like
 standardization or bootstrapping of parameters and models, feature
 reduction (feature extraction and variable selection) as well as
 conversion between indices of effect size.
