Package: r-cran-marginaleffects
Version: 0.32.0-1
Architecture: amd64
Maintainer: Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
Original-Maintainer: Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
Installed-Size: 2478
Depends: r-api-4.0, r-cran-backports, r-cran-checkmate, r-cran-data.table (>= 1.16.4), r-cran-generics, r-cran-formula, r-cran-insight (>= 1.3.0), r-cran-rlang
Recommends: r-cran-aer, r-cran-amelia, r-cran-afex, r-cran-arrow, r-cran-bayestestr, r-cran-bench, r-cran-bh, r-cran-blme, r-cran-boot, r-cran-brglm2, r-cran-brms, r-cran-broom, r-cran-car, r-cran-cardata, r-cran-collapse, r-cran-conflicted, r-cran-covr, r-cran-distributional, r-cran-dplyr, r-cran-emmeans, r-cran-estimatr, r-cran-fontquiver, r-cran-future, r-cran-future.apply, r-cran-gam, r-cran-geepack, r-cran-ggplot2, r-cran-ggrepel, r-cran-glmmtmb, r-cran-haven, r-cran-here, r-cran-kableextra, r-cran-lme4, r-cran-lmertest, r-cran-magrittr, r-cran-matchit, r-cran-mass, r-cran-mclogit, r-cran-mgcv, r-cran-mice, r-cran-multcomp, r-cran-mvtnorm, r-cran-nlme, r-cran-nnet, r-cran-numderiv, r-cran-ordinal, r-cran-parameters, r-cran-partykit, r-cran-patchwork, r-cran-pkgdown, r-cran-pbapply, r-cran-plm, r-cran-polspline, r-cran-posterior, r-cran-pscl, r-cran-purrr, r-cran-quantreg, r-cran-rcmdcheck, r-cran-remotes, r-cran-reticulate, r-cran-rmarkdown, r-cran-rms, r-cran-robust, r-cran-robustbase, r-cran-rsample, r-cran-rstanarm, r-cran-rstantools, r-cran-rstudioapi, r-cran-rsvg, r-cran-sandwich, r-cran-spelling, r-cran-survey, r-cran-survival, r-cran-svglite, r-cran-systemfit, r-cran-systemfonts, r-cran-tibble, r-cran-tidyr, r-cran-tidyverse, r-cran-tinytest, r-cran-withr, r-cran-yaml, r-cran-knitr, r-cran-testthat (>= 3.0.0)
Section: gnu-r
Priority: optional
Homepage: https://cran.r-project.org/package=marginaleffects
Description: GNU R predictions, comparisons, slopes, marginal means, and hypothesis
 Tests Compute and plot predictions, slopes, marginal means, and
 comparisons (contrasts, risk ratios, odds, etc.) for over 100 classes of
 statistical and machine learning models in R. Conduct linear and non-
 linear hypothesis tests, or equivalence tests. Calculate uncertainty
 estimates using the delta method, bootstrapping, or simulation-based
 inference.
