Package: r-cran-marginaleffects
Version: 0.32.0-2
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: 2482
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
Section: gnu-r
Priority: optional
Homepage: https://cran.r-project.org/package=marginaleffects
Description: 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. Details can be found
 in Arel-Bundock, Greifer, and Heiss (2024) <doi:10.18637/jss.v111.i09>.
