Showing 18 of total 18 results (show query)
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
16.7 match 579 stars 16.82 score 2.2k scripts 82 dependents
meierluk
hdi:High-Dimensional Inference
Implementation of multiple approaches to perform inference in high-dimensional models.
Maintained by Lukas Meier. Last updated 4 years ago.
61.6 match 2 stars 4.47 score 139 scripts 7 dependents
mjskay
tidybayes:Tidy Data and 'Geoms' for Bayesian Models
Compose data for and extract, manipulate, and visualize posterior draws from Bayesian models ('JAGS', 'Stan', 'rstanarm', 'brms', 'MCMCglmm', 'coda', ...) in a tidy data format. Functions are provided to help extract tidy data frames of draws from Bayesian models and that generate point summaries and intervals in a tidy format. In addition, 'ggplot2' 'geoms' and 'stats' are provided for common visualization primitives like points with multiple uncertainty intervals, eye plots (intervals plus densities), and fit curves with multiple, arbitrary uncertainty bands.
Maintained by Matthew Kay. Last updated 6 months ago.
bayesian-data-analysisbrmsggplot2jagsstantidy-datavisualization
5.5 match 732 stars 14.88 score 7.3k scripts 19 dependentsmjskay
ggdist:Visualizations of Distributions and Uncertainty
Provides primitives for visualizing distributions using 'ggplot2' that are particularly tuned for visualizing uncertainty in either a frequentist or Bayesian mode. Both analytical distributions (such as frequentist confidence distributions or Bayesian priors) and distributions represented as samples (such as bootstrap distributions or Bayesian posterior samples) are easily visualized. Visualization primitives include but are not limited to: points with multiple uncertainty intervals, eye plots (Spiegelhalter D., 1999) <https://ideas.repec.org/a/bla/jorssa/v162y1999i1p45-58.html>, density plots, gradient plots, dot plots (Wilkinson L., 1999) <doi:10.1080/00031305.1999.10474474>, quantile dot plots (Kay M., Kola T., Hullman J., Munson S., 2016) <doi:10.1145/2858036.2858558>, complementary cumulative distribution function barplots (Fernandes M., Walls L., Munson S., Hullman J., Kay M., 2018) <doi:10.1145/3173574.3173718>, and fit curves with multiple uncertainty ribbons.
Maintained by Matthew Kay. Last updated 4 months ago.
ggplot2uncertaintyuncertainty-visualizationvisualizationcpp
3.3 match 856 stars 15.24 score 3.1k scripts 61 dependents
zhengxiaouvic
rmBayes:Performing Bayesian Inference for Repeated-Measures Designs
A Bayesian credible interval is interpreted with respect to posterior probability, and this interpretation is far more intuitive than that of a frequentist confidence interval. However, standard highest-density intervals can be wide due to between-subjects variability and tends to hide within-subject effects, rendering its relationship with the Bayes factor less clear in within-subject (repeated-measures) designs. This urgent issue can be addressed by using within-subject intervals in within-subject designs, which integrate four methods including the Wei-Nathoo-Masson (2023) <doi:10.3758/s13423-023-02295-1>, the Loftus-Masson (1994) <doi:10.3758/BF03210951>, the Nathoo-Kilshaw-Masson (2018) <doi:10.1016/j.jmp.2018.07.005>, and the Heck (2019) <doi:10.31234/osf.io/whp8t> interval estimates.
Maintained by Zhengxiao Wei. Last updated 1 years ago.
bayesian-inferencecredible-intervalhdirepeated-measuresstanwithin-subjectcpp
11.0 match 2 stars 3.00 score 2 scripts
ccs-lab
hBayesDM:Hierarchical Bayesian Modeling of Decision-Making Tasks
Fit an array of decision-making tasks with computational models in a hierarchical Bayesian framework. Can perform hierarchical Bayesian analysis of various computational models with a single line of coding (Ahn et al., 2017) <doi:10.1162/CPSY_a_00002>.
Maintained by Woo-Young Ahn. Last updated 11 months ago.
bayesiancomputationaldecision-makinghierarchical-bayesian-analysismodelingreinforcement-learning
3.0 match 237 stars 8.71 score 270 scripts
ropensci
fellingdater:Estimate, report and combine felling dates of historical tree-ring series
fellingdater is an R package that aims to facilitate the analysis and interpretation of tree-ring data from wooden cultural heritage objects and structures. The package standardizes the process of computing and combining felling date estimates, both for individual and groups of related tree-ring series.
Maintained by Kristof Haneca. Last updated 10 months ago.
dendrochronologysapwoodtree-rings
5.1 match 9 stars 5.08 score 7 scripts
oeysan
bfw:Bayesian Framework for Computational Modeling
Derived from the work of Kruschke (2015, <ISBN:9780124058880>), the present package aims to provide a framework for conducting Bayesian analysis using Markov chain Monte Carlo (MCMC) sampling utilizing the Just Another Gibbs Sampler ('JAGS', Plummer, 2003, <https://mcmc-jags.sourceforge.io>). The initial version includes several modules for conducting Bayesian equivalents of chi-squared tests, analysis of variance (ANOVA), multiple (hierarchical) regression, softmax regression, and for fitting data (e.g., structural equation modeling).
Maintained by Øystein Olav Skaar. Last updated 3 years ago.
bayesian-data-analysisbayesian-statisticsjagsmcmcpsychological-sciencecpp
4.3 match 10 stars 5.89 score 31 scripts
flyaflya
causact:Fast, Easy, and Visual Bayesian Inference
Accelerate Bayesian analytics workflows in 'R' through interactive modelling, visualization, and inference. Define probabilistic graphical models using directed acyclic graphs (DAGs) as a unifying language for business stakeholders, statisticians, and programmers. This package relies on interfacing with the 'numpyro' python package.
Maintained by Adam Fleischhacker. Last updated 2 months ago.
bayesian-inferencedagsposterior-probabilityprobabilistic-graphical-modelsprobabilistic-programming
3.2 match 45 stars 7.15 score 52 scripts
ngumbang
HDInterval:Highest (Posterior) Density Intervals
A generic function and a set of methods to calculate highest density intervals for a variety of classes of objects which can specify a probability density distribution, including MCMC output, fitted density objects, and functions.
Maintained by Ngumbang Juat. Last updated 2 years ago.
3.3 match 6.80 score 936 scripts 49 dependents
andregustavom
mlquantify:Algorithms for Class Distribution Estimation
Quantification is a prominent machine learning task that has received an increasing amount of attention in the last years. The objective is to predict the class distribution of a data sample. This package is a collection of machine learning algorithms for class distribution estimation. This package include algorithms from different paradigms of quantification. These methods are described in the paper: A. Maletzke, W. Hassan, D. dos Reis, and G. Batista. The importance of the test set size in quantification assessment. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI20, pages 2640–2646, 2020. <doi:10.24963/ijcai.2020/366>.
Maintained by Andre Maletzke. Last updated 3 years ago.
2.0 match 7 stars 3.54 score 1 scripts
nenuial
geographer:Geography Vizualisations
Provides function and objects to establish vizualisations for my Geography lessons.
Maintained by Pascal Burkhard. Last updated 21 days ago.
1.8 match 1 stars 2.78 score
myaseen208
stability:Stability Analysis of Genotype by Environment Interaction (GEI)
Functionalities to perform Stability Analysis of Genotype by Environment Interaction (GEI) to identify superior and stable genotypes under diverse environments. It performs Eberhart & Russel's ANOVA (1966) (<doi:10.2135/cropsci1966.0011183X000600010011x>), Finlay and Wilkinson (1963) Joint Linear Regression (<doi:10.1071/AR9630742>), Wricke (1962, 1964) Ecovalence, Shukla's stability variance parameter (1972) (<doi:10.1038/hdy.1972.87>) and Kang's (1991) (<doi:10.2134/agronj1991.00021962008300010037x>) simultaneous selection for high yielding and stable parameter.
Maintained by Muhammad Yaseen. Last updated 6 years ago.
0.5 match 3 stars 4.17 score 33 scripts 1 dependents
jmbh
inet:Performing Inference on Networks with Regularization
Performs inference with the lasso in Gaussian Graphical Models. The package consists of wrappers for functions from the hdi package.
Maintained by Jonas Haslbeck. Last updated 3 years ago.
0.5 match 2.00 score 7 scripts
