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.
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easystatshacktoberfestinsightmodelsnamespredictorsrandom
17.62 score 435 stars 273 dependents 618 scripts 217k downloads
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>.
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bayes-factorsbayesfactorbayesianbayesian-frameworkcredible-intervaleasystatshacktoberfesthdimapposterior-distributionsrope
17.21 score 589 stars 94 dependents 3.5k scripts 114k downloads
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>.
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anovacohens-dcomputeconversioncorrelationeffect-sizeeffectsizehacktoberfesthedges-ginterpretationstandardizationstandardizedstatistics
17.01 score 349 stars 34 dependents 3.4k scripts 74k downloads
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>.
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aiceasystatshacktoberfestloomachine-learningmixed-modelsmodelsperformancer2statistics
16.67 score 1.1k stars 58 dependents 7.4k scripts 121k downloads
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).
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betabootstrapciconfidence-intervalsdata-reductioneasystatsfafeature-extractionfeature-reductionhacktoberfestparameterspcapvaluesregression-modelsrobust-statisticsstandardizestandardized-estimatesstatistical-models
16.01 score 492 stars 61 dependents 2.4k scripts 110k downloads
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.
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anovasapaautomated-report-generationautomaticbayesiandescribeeasystatshacktoberfestmanuscriptmodelsreportreportingreportsscientificstatsmodels
14.93 score 714 stars 3 dependents 1.6k scripts 13k downloads
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>.
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datadplyrhacktoberfestjanitormanipulationreshapetidyrwrangling
14.91 score 235 stars 132 dependents 531 scripts 173k downloads
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>.
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bayesianbayesian-correlationsbiserialcorcorrelationcorrelation-analysiscorrelationseasystatsgammagaussian-graphical-modelshacktoberfestmatrixmultilevel-correlationsoutlierspartialpartial-correlationsregressionrobustspearman
14.43 score 453 stars 13 dependents 990 scripts 28k downloads
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>.
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data-visualizationeasystatsggplot2hacktoberfestplottingseestatisticsvisualisationvisualization
13.53 score 946 stars 4 dependents 2.7k scripts 26k downloads
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()'.
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contrast-analysiscontrastseasystatsestimateggplot2hacktoberfestmarginalmarginal-effectsmeanspredict
13.08 score 261 stars 5 dependents 527 scripts 25k downloads
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.
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dataanalyticsdatascienceeasystatshacktoberfestmodelsperformance-metricsregression-modelsstatistics
12.87 score 1.2k stars 2.5k scripts 8.9k downloads