Package: modelbased 0.10.0.9

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]

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NEWS

# 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 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

On CRAN:

Conda:

contrast-analysiscontrastseasystatsestimateggplot2hacktoberfestmarginalmarginal-effectsmeanspredict

12.44 score 244 stars 4 packages 315 scripts 19k downloads 28 exports 4 dependencies

Last updated 2 hours agofrom:ad85a0d07a. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKApr 01 2025
R-4.5-winOKApr 01 2025
R-4.5-macOKApr 01 2025
R-4.5-linuxOKApr 01 2025
R-4.4-winOKApr 01 2025
R-4.4-macOKApr 01 2025
R-4.4-linuxOKApr 01 2025
R-4.3-winOKApr 01 2025
R-4.3-macOKApr 01 2025

Exports:describe_nonlinearestimate_contrastsestimate_expectationestimate_grouplevelestimate_linkestimate_meansestimate_predictionestimate_relationestimate_slopesestimate_smoothfind_inversionsget_emcontrastsget_emmeansget_emtrendsget_marginalcontrastsget_marginalmeansget_marginaltrendspool_contrastspool_predictionspool_slopesprint_htmlprint_mdreshape_grouplevelsmoothingstandardizeunstandardizevisualisation_recipezero_crossings

Dependencies:bayestestRdatawizardinsightparameters

Overview of Vignettes

Rendered fromoverview_of_vignettes.Rmdusingknitr::rmarkdownon Apr 01 2025.

Last update: 2025-04-01
Started: 2022-05-26

Readme and manuals

Help Manual

Help pageTopics
Sample dataset from a course about analysis of factorial designscoffee_data
Describe the smooth term (for GAMs) or non-linear predictorsdescribe_nonlinear describe_nonlinear.data.frame estimate_smooth
Sample dataset from the EFC Surveyefc
Estimate Marginal Contrastsestimate_contrasts estimate_contrasts.default
Model-based predictionsestimate_expectation estimate_link estimate_prediction estimate_relation
Group-specific parameters of mixed models random effectsestimate_grouplevel estimate_grouplevel.brmsfit estimate_grouplevel.default reshape_grouplevel
Estimate Marginal Means (Model-based average at each factor level)estimate_means
Estimate Marginal Effectsestimate_slopes
Sample data setfish
Consistent API for 'emmeans' and 'marginaleffects'get_emcontrasts get_emmeans get_emtrends get_marginalcontrasts get_marginalmeans get_marginaltrends
Global options from the modelbased packagemodelbased-options
Pool contrasts and comparisons from 'estimate_contrasts()'pool_contrasts
Pool Predictions and Estimated Marginal Meanspool_predictions pool_slopes
Printing modelbased-objectsprint.estimate_contrasts
Smoothing a vector or a time seriessmoothing
Automated plotting for 'modelbased' objectsvisualisation_recipe.estimate_grouplevel visualisation_recipe.estimate_predicted visualisation_recipe.estimate_slopes
Find zero-crossings and inversion pointsfind_inversions zero_crossings