bayestestR - Understand and Describe Bayesian Models and Posterior Distributions
Provides utilities to describe posterior distributions and Bayesian models. It includes point-estimates such as Maximum A Posteriori (MAP), measures of dispersion (Highest Density Interval - HDI; Kruschke, 2015 <doi:10.1016/C2012-0-00477-2>) and indices used for null-hypothesis testing (such as ROPE percentage, pd and Bayes factors). References: Makowski et al. (2021) <doi:10.21105/joss.01541>.
Last updated 3 days ago
bayes-factorsbayesfactorbayesianbayesian-frameworkcredible-intervaleasystatshacktoberfesthdimapposterior-distributionsrope
563 stars 16.37 score 2 dependencies 75 dependentsmodelbased - 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()'.
Last updated 1 months ago
contrast-analysiscontrastseasystatsestimateggplot2hacktoberfestmarginalmarginal-effectsmeanspredict
233 stars 11.83 score 6 dependencies 3 dependentspsycho - Efficient and Publishing-Oriented Workflow for Psychological Science
The main goal of the psycho package is to provide tools for psychologists, neuropsychologists and neuroscientists, to facilitate and speed up the time spent on data analysis. It aims at supporting best practices and tools to format the output of statistical methods to directly paste them into a manuscript, ensuring statistical reporting standardization and conformity.
Last updated 4 years ago
apaapa6bayesiancorrelationformatinterpretationmixed-modelsneurosciencepsychopsychologyrstanarmstatistics
144 stars 10.70 score 39 dependencies 5 dependents