Convert fitted objects from various R mixed-model packages into tidy data frames along the lines of the broom package. The package provides three S3 generics for each model: tidy(), which summarizes a model's statistical findings such as coefficients of a regression; augment(), which adds columns to the original data such as predictions, residuals and cluster assignments; and glance(), which provides a one-row summary of model-level statistics.

Extend the bigmemory package with table', tapply', and split support for big.matrix objects. The functions may also be used with native R matrices for improving speed and memory-efficiency.

This package provides functions to scrape IQY calls to Bank of Mexico, downloading and ordering the data conveniently.

This package implements three test procedures using bootstrap resampling techniques for assessing treatment effects in one-way ANOVA models with unequal variances (heteroscedasticity). It includes a parametric bootstrap likelihood ratio test (PB_LRT()), a pairwise parametric bootstrap mean test (PPBMT()), and a Rademacher wild pairwise non-parametric bootstrap test (RWPNPBT()). These methods provide robust alternatives to classical ANOVA and standard pairwise comparisons when the assumption of homogeneity of variances is violated.

It makes the creation of networks from sequences of RNA, with this is done the abstraction of characteristics of these networks with a methodology of threshold for the purpose of making a classification between the classes of the sequences. There are four data present in the BASiNET package, "sequences", "sequences2", "sequences-predict" and "sequences2-predict" with 11, 10, 11 and 11 sequences respectively. These sequences were taken from the data set used in the article (LI, Aimin; ZHANG, Junying; ZHOU, Zhongyin, 2014) <doi:10.1186/1471-2105-15-311>, these sequences are used to run examples. The BASiNET was published on Nucleic Acids Research, (ITO, Eric; KATAHIRA, Isaque; VICENTE, Fábio; PEREIRA, Felipe; LOPES, Fabrà cio, 2018) <doi:10.1093/nar/gky462>.

This package provides an Markov-Chain-Monte-Carlo algorithm for Bayesian t-tests on the effect size. The underlying Gibbs sampler is based on a two-component Gaussian mixture and approximates the posterior distributions of the effect size, the difference of means and difference of standard deviations. A posterior analysis of the effect size via the region of practical equivalence is provided, too. For more details about the Gibbs sampler see Kelter (2019) <arXiv:1906.07524>.

Draw horizontal histograms, color scattered points by 3rd dimension, enhance date- and log-axis plots, zoom in X11 graphics, trace errors and warnings, use the unit hydrograph in a linear storage cascade, convert lists to data.frames and arrays, fit multiple functions.

Bayesian inference under log-normality assumption must be performed very carefully. In fact, under the common priors for the variance, useful quantities in the original data scale (like mean and quantiles) do not have posterior moments that are finite (Fabrizi et al. 2012 <doi:10.1214/12-BA733>). This package allows to easily carry out a proper Bayesian inferential procedure by fixing a suitable distribution (the generalized inverse Gaussian) as prior for the variance. Functions to estimate several kind of means (unconditional, conditional and conditional under a mixed model) and quantiles (unconditional and conditional) are provided.

Extract, visualize and summarize aerial movements of birds and insects from weather radar data. See Dokter, A. M. et al. (2018) "bioRad: biological analysis and visualization of weather radar data" <doi:10.1111/ecog.04028> for a software paper describing package and methodologies.

Presence-Only data is best modelled with a Point Process Model. The work of Moreira and Gamerman (2022) <doi:10.1214/21-AOAS1569> provides a way to use exact Bayesian inference to model this type of data, which is implemented in this package.

Binford's hunter-gatherer data includes more than 200 variables coding aspects of hunter-gatherer subsistence, mobility, and social organization for 339 ethnographically documented groups of hunter-gatherers.

Bayesian power/type I error calculation and model fitting using the power prior and the normalized power prior for generalized linear models. Detailed examples of applying the package are available at <doi:10.32614/RJ-2023-016>. Models for time-to-event outcomes are implemented in the R package BayesPPDSurv'. The Bayesian clinical trial design methodology is described in Chen et al. (2011) <doi:10.1111/j.1541-0420.2011.01561.x>, and Psioda and Ibrahim (2019) <doi:10.1093/biostatistics/kxy009>. The normalized power prior is described in Duan et al. (2006) <doi:10.1002/env.752> and Ibrahim et al. (2015) <doi:10.1002/sim.6728>.

Run other estimation and simulation software via the nlmixr2 (Fidler et al (2019) <doi:10.1002/psp4.12445>) interface including PKNCA', NONMEM and Monolix'. While not required, you can get/install the lixoftConnectors package in the Monolix installation, as described at the following url <https://monolixsuite.slp-software.com/r-functions/2024R1/installation-and-initialization>. When lixoftConnectors is available, Monolix can be run directly instead of setting up command line usage.

An interface to the Briq API <https://briq.github.io>. Briq is a tool that aims to promote employee engagement by helping employees recognize and reward each other. Employees can praise and thank one another (for achieving a company goal, for example) by giving virtual credits (known as briqs or bqs') that can be redeemed for various rewards. The Briq API lets you create, read, update and delete users, user groups, transactions and messages. This package provides functions that simplify getting the users, user groups and transactions of your organization into R.

Implementations in cpp of the BayesProject algorithm (see G. Hahn, P. Fearnhead, I.A. Eckley (2020) <doi:10.1007/s11222-020-09966-2>) which implements a fast approach to compute a projection direction for multivariate changepoint detection, as well as the sum-cusum and max-cusum methods, and a wild binary segmentation wrapper for all algorithms.

Implementation of BayesFlux.jl for R; It extends the famous Flux.jl machine learning library to Bayesian Neural Networks. The goal is not to have the fastest production ready library, but rather to allow more people to be able to use and research on Bayesian Neural Networks.

This package provides a C++ library for Bayesian modeling, with an emphasis on Markov chain Monte Carlo. Although boom contains a few R utilities (mainly plotting functions), its primary purpose is to install the BOOM C++ library on your system so that other packages can link against it.

This package creates bubbles within shiny and rmarkdown backgrounds using the bubbly-bg JavaScript library.

Bayesian model and associated tools for generating estimates of total naloxone kit numbers distributed and used from naloxone kit orders data. Provides functions for generating simulated data of naloxone kit use and functions for generating samples from the posterior.

Fits the Bayesian partial least squares regression model introduced in Urbas et al. (2024) <doi:10.1214/24-AOAS1947>. Suitable for univariate and multivariate regression with high-dimensional data.

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).

Bayes factors represent the ratio of probabilities assigned to data by competing scientific hypotheses. However, one drawback of Bayes factors is their dependence on prior specifications that define null and alternative hypotheses. Additionally, there are challenges in their computation. To address these issues, we define Bayes factor functions (BFFs) directly from common test statistics. BFFs express Bayes factors as a function of the prior densities used to define the alternative hypotheses. These prior densities are centered on standardized effects, which serve as indices for the BFF. Therefore, BFFs offer a summary of evidence in favor of alternative hypotheses that correspond to a range of scientifically interesting effect sizes. Such summaries remove the need for arbitrary thresholds to determine "statistical significance." BFFs are available in closed form and can be easily computed from z, t, chi-squared, and F statistics. They depend on hyperparameters "r" and "tau^2", which determine the shape and scale of the prior distributions defining the alternative hypotheses. Plots of BFFs versus effect size provide informative summaries of hypothesis tests that can be easily aggregated across studies.

Statistical methods for analyzing binary replicates, which are noisy binary measurements of latent binary states. Provides scoring functions (average, median, likelihood-based, and Bayesian) to estimate the probability that an individual is in the positive state. Includes maximum a posteriori estimation via the EM algorithm and full Bayesian inference via Stan. Supports classification with inconclusive decisions and prevalence estimation.

Some elementary matrix algebra tools are implemented to manage block matrices or partitioned matrix, i.e. "matrix of matrices" (http://en.wikipedia.org/wiki/Block_matrix). The block matrix is here defined as a new S3 object. In this package, some methods for "matrix" object are rewritten for "blockmatrix" object. New methods are implemented. This package was created to solve equation systems with block matrices for the analysis of environmental vector time series . Bugs/comments/questions/collaboration of any kind are warmly welcomed.