Showing 9 of total 9 results (show query)
richarddmorey
BayesFactor:Computation of Bayes Factors for Common Designs
A suite of functions for computing various Bayes factors for simple designs, including contingency tables, one- and two-sample designs, one-way designs, general ANOVA designs, and linear regression.
Maintained by Richard D. Morey. Last updated 1 years ago.
71.3 match 133 stars 13.70 score 1.7k scripts 21 dependents
easystats
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>.
Maintained by Dominique Makowski. Last updated 11 days ago.
bayes-factorsbayesfactorbayesianbayesian-frameworkcredible-intervaleasystatshacktoberfesthdimapposterior-distributionsrope
14.3 match 579 stars 16.82 score 2.2k scripts 82 dependents
laplacesdemonr
LaplacesDemon:Complete Environment for Bayesian Inference
Provides a complete environment for Bayesian inference using a variety of different samplers (see ?LaplacesDemon for an overview).
Maintained by Henrik Singmann. Last updated 12 months ago.
6.3 match 93 stars 13.45 score 1.8k scripts 60 dependents
jongheepark
MCMCpack:Markov Chain Monte Carlo (MCMC) Package
Contains functions to perform Bayesian inference using posterior simulation for a number of statistical models. Most simulation is done in compiled C++ written in the Scythe Statistical Library Version 1.0.3. All models return 'coda' mcmc objects that can then be summarized using the 'coda' package. Some useful utility functions such as density functions, pseudo-random number generators for statistical distributions, a general purpose Metropolis sampling algorithm, and tools for visualization are provided.
Maintained by Jong Hee Park. Last updated 7 months ago.
8.1 match 13 stars 9.40 score 2.6k scripts 150 dependents
gasparl
neatStats:Neat and Painless Statistical Reporting
User-friendly, clear and simple statistics, primarily for publication in psychological science. The main functions are wrappers for other packages, but there are various additions as well. Every relevant step from data aggregation to reportable printed statistics is covered for basic experimental designs.
Maintained by Gáspár Lukács. Last updated 2 years ago.
bayesfactorconfidence-intervalspipelinestatistical-analysisstatistics
11.0 match 3 stars 4.18 scoreeasystats
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).
Maintained by Daniel Lüdecke. Last updated 19 hours ago.
betabootstrapciconfidence-intervalsdata-reductioneasystatsfafeature-extractionfeature-reductionhacktoberfestparameterspcapvaluesregression-modelsrobust-statisticsstandardizestandardized-estimatesstatistical-models
2.0 match 453 stars 15.65 score 1.8k scripts 56 dependentseasystats
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.
Maintained by Rémi Thériault. Last updated 1 months ago.
anovasapaautomated-report-generationautomaticbayesiandescribeeasystatshacktoberfestmanuscriptmodelsreportreportingreportsscientificstatsmodels
1.8 match 698 stars 14.48 score 1.1k scripts 3 dependents
pcarbo
varbvs:Large-Scale Bayesian Variable Selection Using Variational Methods
Fast algorithms for fitting Bayesian variable selection models and computing Bayes factors, in which the outcome (or response variable) is modeled using a linear regression or a logistic regression. The algorithms are based on the variational approximations described in "Scalable variational inference for Bayesian variable selection in regression, and its accuracy in genetic association studies" (P. Carbonetto & M. Stephens, 2012, <DOI:10.1214/12-BA703>). This software has been applied to large data sets with over a million variables and thousands of samples.
Maintained by Peter Carbonetto. Last updated 2 years ago.
3.3 match 4.85 score 146 scripts 2 dependents
cran
extRemes:Extreme Value Analysis
General functions for performing extreme value analysis. In particular, allows for inclusion of covariates into the parameters of the extreme-value distributions, as well as estimation through MLE, L-moments, generalized (penalized) MLE (GMLE), as well as Bayes. Inference methods include parametric normal approximation, profile-likelihood, Bayes, and bootstrapping. Some bivariate functionality and dependence checking (e.g., auto-tail dependence function plot, extremal index estimation) is also included. For a tutorial, see Gilleland and Katz (2016) <doi: 10.18637/jss.v072.i08> and for bootstrapping, please see Gilleland (2020) <doi: 10.1175/JTECH-D-20-0070.1>.
Maintained by Eric Gilleland. Last updated 4 months ago.
3.3 match 2 stars 3.75 score 5 dependents