It covers several Bayesian Analysis of Variance (BANOVA) models used in analysis of experimental designs in which both within- and between- subjects factors are manipulated. They can be applied to data that are common in the behavioral and social sciences. The package includes: Hierarchical Bayes ANOVA models with normal response, t response, Binomial (Bernoulli) response, Poisson response, ordered multinomial response and multinomial response variables. All models accommodate unobserved heterogeneity by including a normal distribution of the parameters across individuals. Outputs of the package include tables of sums of squares, effect sizes and p-values, and tables of predictions, which are easily interpretable for behavioral and social researchers. The floodlight analysis and mediation analysis based on these models are also provided. BANOVA uses Stan and JAGS as the computational platform. References: Dong and Wedel (2017) <doi:10.18637/jss.v081.i09>; Wedel and Dong (2020) <doi:10.1002/jcpy.1111>.

This package provides a curated collection of biodiversity and species-related datasets (birds, plants, reptiles, turtles, mammals, bees, marine data and related biological measurements), together with small utilities to load and explore them. The package gathers data sourced from public repositories (including Kaggle and well-known ecological/biological R packages) and standardizes access for researchers, educators, and data analysts working on biodiversity, biogeography, ecology and comparative biology. It aims to simplify reproducible workflows by packaging commonly used example datasets and metadata so they can be easily inspected, visualized, and used for teaching, testing, and prototyping analyses.

Implementations of Bayesian parametric, nonparametric and semiparametric procedures for univariate and multivariate time series. The package is based on the methods presented in C. Kirch et al (2018) <doi:10.1214/18-BA1126>, A. Meier (2018) <https://opendata.uni-halle.de//handle/1981185920/13470> and Y. Tang et al (2023) <doi:10.48550/arXiv.2303.11561>. It was supported by DFG grants KI 1443/3-1 and KI 1443/3-2.

Simulation and parameter estimation of multitype Bienayme - Galton - Watson processes.

This package provides consistent batch means estimation of Monte Carlo standard errors.

Generalization of the Bayesian classification and regression tree (CART) model that partitions subjects into terminal nodes and tailors machine learning model to each terminal node.

This package provides functions for downloading data from the Bank for International Settlements (BIS; <https://www.bis.org/>) in Basel. Supported are only full datasets in (typically) CSV format. The package is lightweight and without dependencies; suggested packages are used only if data is to be transformed into particular data structures, for instance into zoo objects. Downloaded data can optionally be cached, to avoid repeated downloads of the same files.

Computes Bayesian posterior distributions of predictions, marginal effects, and differences of marginal effects for various generalized linear models. Importantly, the posteriors are on the mean (response) scale, allowing for more natural interpretation than summaries on the link scale. Also, predictions and marginal effects of the count probabilities for Poisson and negative binomial models can be computed.

An implementation of intervention effect estimation for DAGs (directed acyclic graphs) learned from binary or continuous data. First, parameters are estimated or sampled for the DAG and then interventions on each node (variable) are propagated through the network (do-calculus). Both exact computation (for continuous data or for binary data up to around 20 variables) and Monte Carlo schemes (for larger binary networks) are implemented.

This package provides a collection of functions for downloading and processing automatic weather station (AWS) data from INMET (Brazilâ s National Institute of Meteorology), designed to support the estimation of reference evapotranspiration (ETo). The package facilitates streamlined access to meteorological data and aims to simplify analyses in agricultural and environmental contexts.

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.

Analysis of gene expression RNA-seq data using Bartlett-Adjusted Likelihood-based LInear model (BALLI). Based on likelihood ratio test, it provides comparisons for effect of one or more variables. See Kyungtaek Park (2018) <doi:10.1101/344929> for more information.

Select balanced and spatially balanced probability samples in multi-dimensional spaces with any prescribed inclusion probabilities. It contains fast (C++ via Rcpp) implementations of the included sampling methods. The local pivotal method by Grafström, Lundström and Schelin (2012) <doi:10.1111/j.1541-0420.2011.01699.x> and spatially correlated Poisson sampling by Grafström (2012) <doi:10.1016/j.jspi.2011.07.003> are included. Also the cube method (for balanced sampling) and the local cube method (for doubly balanced sampling) are included, see Grafström and Tillé (2013) <doi:10.1002/env.2194>.

Fits boundary line models to datasets as proposed by Webb (1972) <doi:10.1080/00221589.1972.11514472> and makes statistical inferences about their parameters. Provides additional tools for testing datasets for evidence of boundary presence and selecting initial starting values for model optimization prior to fitting the boundary line models. It also includes tools for conducting post-hoc analyses such as predicting boundary values and identifying the most limiting factor (Miti, Milne, Giller, Lark (2024) <doi:10.1016/j.fcr.2024.109365>). This ensures a comprehensive analysis for datasets that exhibit upper boundary structures.

This package implements Bayesian spatio-temporal factor analysis models for multivariate data observed across space and time. The package provides tools for model fitting via Markov chain Monte Carlo (MCMC), spatial and temporal interpolation, and visualization of latent factors and loadings to support inference and exploration of underlying spatio-temporal patterns. Designed for use in environmental, ecological, or public health applications, with support for posterior prediction and uncertainty quantification. Includes functions such as BSTFA() for model fitting and plot_factor() to visualize the latent processes. Functions are based on and extended from methods described in Berrett, et al. (2020) <doi:10.1002/env.2609>.

Allows access to data from the Brazilian Public Security Information System (SINESP) by state and municipality. It should be emphasized that the package only extracts the data and facilitates its manipulation in R. Therefore, its sole purpose is to support empirical research. All data credits belong to SINESP, an integrated information platform developed and maintained by the National Secretariat of Public Security (SENASP) of the Ministry of Justice and Public Security. <https://www.gov.br/mj/pt-br/assuntos/sua-seguranca/seguranca-publica/sinesp-1>.

Two practical tests are provided for assessing whether multiple covariates in a treatment group and a matched control group are balanced in observational studies.

This is a sub national population projection model for calculating population development. The model uses a cohort component method. Further reading: Stanley K. Smith: A Practitioner's Guide to State and Local Population Projections. 2013. <doi:10.1007/978-94-007-7551-0>.

Fit Bayesian graduation mortality using the Heligman-Pollard model, as seen in Heligman, L., & Pollard, J. H. (1980) <doi:10.1017/S0020268100040257> and Dellaportas, Petros, et al. (2001) <doi:10.1111/1467-985X.00202>, and dynamic linear model (Campagnoli, P., Petris, G., and Petrone, S. (2009) <doi:10.1007/b135794_2>). While Heligman-Pollard has parameters with a straightforward interpretation yielding some rich analysis, the dynamic linear model provides a very flexible adjustment of the mortality curves by controlling the discount factor value. Closing methods for both Heligman-Pollard and dynamic linear model were also implemented according to Dodd, Erengul, et al. (2018) <https://www.jstor.org/stable/48547511>. The Bayesian Lee-Carter model is also implemented to fit historical mortality tables time series to predict the mortality in the following years and to do improvement analysis, as seen in Lee, R. D., & Carter, L. R. (1992) <doi:10.1080/01621459.1992.10475265> and Pedroza, C. (2006) <doi:10.1093/biostatistics/kxj024>. Journal publication available at <doi:10.18637/jss.v113.i09>.

Fits novel models for the conditional relative risk, risk difference and odds ratio <doi:10.1080/01621459.2016.1192546>.

Bayesian Generalized Linear Regression.

Computation of bootstrap p-values through inversion of confidence intervals, including convenience functions for regression models and tests of location.

Bagging bandwidth selection methods for the Parzen-Rosenblatt and Nadaraya-Watson estimators. These bandwidth selectors can achieve greater statistical precision than their non-bagged counterparts while being computationally fast. See Barreiro-Ures et al. (2020) <doi:10.1093/biomet/asaa092> and Barreiro-Ures et al. (2021) <doi:10.48550/arXiv.2105.04134>.

Making probabilistic projections of life expectancy for all countries of the world, using a Bayesian hierarchical model <doi:10.1007/s13524-012-0193-x>. Subnational projections are also supported.