performance - Assessment of Regression Models Performance

Utilities for computing measures to assess model quality, which are not directly provided by R's 'base' or 'stats' packages. These include e.g. measures like r-squared, intraclass correlation coefficient (Nakagawa, Johnson & Schielzeth (2017) <doi:10.1098/rsif.2017.0213>), root mean squared error or functions to check models for overdispersion, singularity or zero-inflation and more. Functions apply to a large variety of regression models, including generalized linear models, mixed effects models and Bayesian models. References: Lüdecke et al. (2021) <doi:10.21105/joss.03139>.

Last updated 6 days ago

aiceasystatshacktoberfestloomachine-learningmixed-modelsmodelsperformancer2statistics

971 stars 9.67 score 3 dependencies 47 dependents

insight - Easy Access to Model Information for Various Model Objects

A tool to provide an easy, intuitive and consistent access to information contained in various R models, like model formulas, model terms, information about random effects, data that was used to fit the model or data from response variables. 'insight' mainly revolves around two types of functions: Functions that find (the names of) information, starting with 'find_', and functions that get the underlying data, starting with 'get_'. The package has a consistent syntax and works with many different model objects, where otherwise functions to access these information are missing.

Last updated 15 hours ago

easystatshacktoberfestinsightmodelsnamespredictorsrandom

388 stars 9.45 score 0 dependencies 196 dependents

easystats - Framework for Easy Statistical Modeling, Visualization, and Reporting

A meta-package that installs and loads a set of packages from 'easystats' ecosystem in a single step. This collection of packages provide a unifying and consistent framework for statistical modeling, visualization, and reporting. Additionally, it provides articles targeted at instructors for teaching 'easystats', and a dashboard targeted at new R users for easily conducting statistical analysis by accessing summary results, model fit indices, and visualizations with minimal programming.

Last updated 4 days ago

dataanalyticsdatascienceeasystatshacktoberfestmodelsperformance-metricsregression-modelsstatistics

1.1k stars 9.30 score 38 dependencies

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 hours ago

bayes-factorsbayesfactorbayesianbayesian-frameworkcredible-intervaleasystatshacktoberfesthdimapposterior-distributionsrope

550 stars 8.82 score 2 dependencies 76 dependents

see - Model Visualisation Toolbox for 'easystats' and 'ggplot2'

Provides plotting utilities supporting packages in the 'easystats' ecosystem (<https://github.com/easystats/easystats>) and some extra themes, geoms, and scales for 'ggplot2'. Color scales are based on <https://materialui.co/>. References: Lüdecke et al. (2021) <doi:10.21105/joss.03393>.

Last updated 10 days ago

data-visualizationeasystatsggplot2hacktoberfestplottingseestatisticsvisualisationvisualization

853 stars 8.81 score 36 dependencies 2 dependents

report - Automated Reporting of Results and Statistical Models

The aim of the 'report' package is to bridge the gap between R’s output and the formatted results contained in your manuscript. This package converts statistical models and data frames into textual reports suited for publication, ensuring standardization and quality in results reporting.

Last updated 17 days ago

anovasapaautomated-report-generationautomaticbayesiandescribeeasystatshacktoberfestmanuscriptmodelsreportreportingreportsscientificstatsmodels

682 stars 8.30 score 6 dependencies 2 dependents

parameters - Processing of Model Parameters

Utilities for processing the parameters of various statistical models. Beyond computing p values, CIs, and other indices for a wide variety of models (see list of supported models using the function 'insight::supported_models()'), this package implements features like bootstrapping or simulating of parameters and models, feature reduction (feature extraction and variable selection) as well as functions to describe data and variable characteristics (e.g. skewness, kurtosis, smoothness or distribution).

Last updated 2 days ago

betabootstrapciconfidence-intervalsdata-reductioneasystatsfafeature-extractionfeature-reductionhacktoberfestparameterspcapvaluesregression-modelsrobust-statisticsstandardizestandardized-estimatesstatistical-models

419 stars 8.14 score 3 dependencies 56 dependents

datawizard - Easy Data Wrangling and Statistical Transformations

A lightweight package to assist in key steps involved in any data analysis workflow: (1) wrangling the raw data to get it in the needed form, (2) applying preprocessing steps and statistical transformations, and (3) compute statistical summaries of data properties and distributions. It is also the data wrangling backend for packages in 'easystats' ecosystem. References: Patil et al. (2022) <doi:10.21105/joss.04684>.

Last updated 6 days ago

datadplyrhacktoberfestjanitormanipulationreshapetidyrwrangling

202 stars 8.01 score 1 dependencies 107 dependents

effectsize - Indices of Effect Size

Provide utilities to work with indices of effect size for a wide variety of models and hypothesis tests (see list of supported models using the function 'insight::supported_models()'), allowing computation of and conversion between indices such as Cohen's d, r, odds, etc. References: Ben-Shachar et al. (2020) <doi:10.21105/joss.02815>.

Last updated 6 days ago

anovacohens-dcomputeconversioncorrelationeffect-sizeeffectsizehacktoberfesthedges-ginterpretationstandardizationstandardizedstatistics

332 stars 7.71 score 5 dependencies 34 dependents

correlation - Methods for Correlation Analysis

Lightweight package for computing different kinds of correlations, such as partial correlations, Bayesian correlations, multilevel correlations, polychoric correlations, biweight correlations, distance correlations and more. Part of the 'easystats' ecosystem. References: Makowski et al. (2020) <doi:10.21105/joss.02306>.

Last updated 1 months ago

bayesianbayesian-correlationsbiserialcorcorrelationcorrelation-analysiscorrelationseasystatsgammagaussian-graphical-modelshacktoberfestmatrixmultilevel-correlationsoutlierspartialpartial-correlationsregressionrobustspearman

425 stars 7.37 score 4 dependencies 10 dependents

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()'.

Last updated 2 months ago

contrast-analysiscontrastseasystatsestimateggplot2hacktoberfestmarginalmarginal-effectsmeanspredict

232 stars 6.05 score 6 dependencies 3 dependents