Package: modelbased 0.15.0.12

Dominique Makowski

modelbased: Estimation of Model-Based Predictions, Contrasts and Means

Implements a general interface for model-based estimations for a wide variety of models, used in the computation of marginal means, contrast analysis and predictions. For a list of supported models, see 'insight::supported_models()'.

Authors:Dominique Makowski [aut, cre], Daniel Lüdecke [aut], Mattan S. Ben-Shachar [aut], Indrajeet Patil [aut], Rémi Thériault [aut]

modelbased_0.15.0.12.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
modelbased/json (API)

# Install 'modelbased' in R:

install.packages('modelbased', repos = c('https://easystats.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/easystats/modelbased/issues

Pkgdown/docs site:https://easystats.github.io

Datasets:

coffee_data - Sample dataset from a course about analysis of factorial designs
efc - Sample dataset from the EFC Survey
fish - Sample data set
puppy_love - More puppy therapy data

On CRAN:

contrast-analysis contrasts easystats estimate ggplot2 hacktoberfest marginal marginal-effects means predict

13.03 score 261 stars 5 packages 610 scripts 20k downloads 31 exports 4 dependencies

Last updated from:61e1620fe5. Checks:7 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	NOTE	287
source / vignettes	OK	312
linux-release-x86_64	NOTE	292
macos-release-arm64	NOTE	132
macos-oldrel-arm64	NOTE	119
windows-devel	NOTE	193
windows-release	NOTE	180
windows-oldrel	NOTE	195
wasm-release	OK	221

Exports:collapse_by_group describe_nonlinear display estimate_contrasts estimate_expectation estimate_grouplevel estimate_link estimate_means estimate_prediction estimate_relation estimate_slopes estimate_smooth find_inversions get_emcontrasts get_emmeans get_emtrends get_marginalcontrasts get_marginalmeans get_marginaltrends pool_contrasts pool_predictions pool_slopes print_html print_md reshape_grouplevel residualize_over_grid smoothing standardize unstandardize visualisation_recipe zero_crossings

Dependencies:bayestestR datawizard insight parameters

Overview of Vignettes

Rendered fromoverview_of_vignettes.Rmdusingknitr::rmarkdown

Last update: 2026-04-26
Started: 2022-05-26

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Converting modelbased-objects into raw data frames	as.data.frame.estimate_contrasts
Sample dataset from a course about analysis of factorial designs	coffee_data
Collapse raw data by random effect groups	collapse_by_group
Describe the smooth term (for GAMs) or non-linear predictors	describe_nonlinear describe_nonlinear.data.frame estimate_smooth
Printing modelbased-objects	display.estimate_contrasts format.estimate_contrasts print.estimate_contrasts
Sample dataset from the EFC Survey	efc
Estimate Marginal Contrasts	estimate_contrasts estimate_contrasts.default
Model-based predictions	estimate_expectation estimate_link estimate_prediction estimate_relation
Group-specific parameters of mixed models random effects	estimate_grouplevel estimate_grouplevel.brmsfit estimate_grouplevel.default reshape_grouplevel reshape_grouplevel.estimate_grouplevel
Estimate Marginal Means (Model-based average at each factor level)	estimate_means
Estimate Marginal Effects	estimate_slopes
Sample data set	fish
Consistent API for 'emmeans' and 'marginaleffects'	get_emcontrasts get_emmeans get_emtrends get_marginalcontrasts get_marginalmeans get_marginaltrends
Global options from the modelbased package	modelbased-options
Automated plotting for 'modelbased' objects	plot.estimate_means plot.estimate_predicted tinyplot.estimate_means visualisation_recipe.estimate_grouplevel visualisation_recipe.estimate_predicted visualisation_recipe.estimate_slopes
Pool contrasts and comparisons from 'estimate_contrasts()'	pool_contrasts
Pool Predictions and Estimated Marginal Means	pool_predictions pool_slopes
More puppy therapy data	puppy_love
Compute partial residuals from a data grid	residualize_over_grid residualize_over_grid.data.frame
Smoothing a vector or a time series	smoothing
Find zero-crossings and inversion points	find_inversions zero_crossings