Reference Analysis for Bayesian Meta-Analysis

Functionality for performing a principled reference analysis in the Bayesian normal-normal hierarchical model used for Bayesian meta-analysis, as described in Ott, Plummer and Roos (2021) . Computes a reference posterior, induced by a minimally informative improper reference prior for the between-study (heterogeneity) standard deviation. Determines additional proper anti-conservative (and conservative) prior benchmarks. Includes functions for reference analyses at both the posterior and the prior level, which, given the data, quantify the informativeness of a heterogeneity prior of interest relative to the minimally informative reference prior and the proper prior benchmarks. The functions operate on data sets which are compatible with the 'bayesmeta' package.

Extracting and Visualizing Bayesian Graphical Models

Fit and visualize the results of a Bayesian analysis of networks commonly found in psychology. The package supports fitting cross-sectional network models fitted using the packages 'BDgraph', 'bgms' and 'BGGM'. The package provides the parameter estimates, posterior inclusion probabilities, inclusion Bayes factor, and the posterior density of the parameters. In addition, for 'BDgraph' and 'bgms' it allows to assess the posterior structure space. Furthermore, the package comes with an extensive suite for visualizing results.

Sparse Generalized Additive Models

Fits sparse frequentist GAMs (SF-GAM) for continuous and discrete responses in the exponential dispersion family with the group lasso, group smoothly clipped absolute deviation (SCAD), and group minimax concave (MCP) penalties . Also fits sparse Bayesian generalized additive models (SB-GAM) with the spike-and-slab group lasso (SSGL) penalty of Bai et al. (2021) . B-spline basis functions are used to model the sparse additive functions. Stand-alone functions for group-regularized negative binomial regression, group-regularized gamma regression, and group-regularized regression in the exponential dispersion family with the SSGL penalty are also provided.

Bayesian Analysis of Finite Mixture of Plackett-Luce Models

Fit finite mixtures of Plackett-Luce models for partial top rankings/orderings within the Bayesian framework. It provides MAP point estimates via EM algorithm and posterior MCMC simulations via Gibbs Sampling. It also fits MLE as a special case of the noninformative Bayesian analysis with vague priors. In addition to inferential techniques, the package assists other fundamental phases of a model-based analysis for partial rankings/orderings, by including functions for data manipulation, simulation, descriptive summary, model selection and goodness-of-fit evaluation. Main references on the methods are Mollica and Tardella (2017) and Mollica and Tardella (2014) .

Censored Distributions for 'greta'

Provides additional censored distributions for use with 'greta', a probabilistic programming framework for Bayesian modeling. Includes censored versions of Normal, Log-Normal, Student's T, Gamma, Exponential, Weibull, Pareto, and Beta distributions with support for right, left, and interval censoring. For details on 'greta', see Golding (2019) . The methods are implemented using 'TensorFlow' and 'TensorFlow Probability' for efficient computation.

Stepwise Elimination and Term Reordering for Mixed-Effects Regression

Finds the largest possible regression model that will still converge for various types of regression analyses (including mixed models and generalized additive models) and then optionally performs stepwise elimination similar to the forward and backward effect-selection methods in SAS, based on the change in log-likelihood or its significance, Akaike's Information Criterion, the Bayesian Information Criterion, the explained deviance, or the F-test of the change in R².

Bayesian Spectral Analysis Models using Gaussian Process Priors

Contains functions to perform Bayesian inference using a spectral analysis of Gaussian process priors. Gaussian processes are represented with a Fourier series based on cosine basis functions. Currently the package includes parametric linear models, partial linear additive models with/without shape restrictions, generalized linear additive models with/without shape restrictions, and density estimation model. To maximize computational efficiency, the actual Markov chain Monte Carlo sampling for each model is done using codes written in FORTRAN 90. This software has been developed using funding supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (no. NRF-2016R1D1A1B03932178 and no. NRF-2017R1D1A3B03035235).

Functions for Hierarchical Bayesian Estimation: A Flexible Approach

Functions for estimating models using a Hierarchical Bayesian (HB) framework. The flexibility comes in allowing the user to specify the likelihood function directly instead of assuming predetermined model structures. Types of models that can be estimated with this code include the family of discrete choice models (Multinomial Logit, Mixed Logit, Nested Logit, Error Components Logit and Latent Class) as well ordered response models like ordered probit and ordered logit. In addition, the package allows for flexibility in specifying parameters as either fixed (non-varying across individuals) or random with continuous distributions. Parameter distributions supported include normal, positive/negative log-normal, positive/negative censored normal, and the Johnson SB distribution. Kenneth Train's Matlab and Gauss code for doing Hierarchical Bayesian estimation has served as the basis for a few of the functions included in this package. These Matlab/Gauss functions have been rewritten to be optimized within R. Considerable code has been added to increase the flexibility and usability of the code base. Train's original Gauss and Matlab code can be found here: < http://elsa.berkeley.edu/Software/abstracts/train1006mxlhb.html> See Train's chapter on HB in Discrete Choice with Simulation here: < http://elsa.berkeley.edu/books/choice2.html>; and his paper on using HB with non-normal distributions here: < http://eml.berkeley.edu//~train/trainsonnier.pdf>. The authors would also like to thank the invaluable contributions of Stephane Hess and the Choice Modelling Centre: < https://cmc.leeds.ac.uk/>.

Bayesian Exponential Smoothing Models with Trend Modifications

An implementation of a number of Global Trend models for time series forecasting that are Bayesian generalizations and extensions of some Exponential Smoothing models. The main differences/additions include 1) nonlinear global trend, 2) Student-t error distribution, and 3) a function for the error size, so heteroscedasticity. The methods are particularly useful for short time series. When tested on the well-known M3 dataset, they are able to outperform all classical time series algorithms. The models are fitted with MCMC using the 'rstan' package.

Bayesian Augmented Control for Clinical Trials

Implements the Bayesian Augmented Control (BAC, a.k.a. Bayesian historical data borrowing) method under clinical trial setting by calling 'Just Another Gibbs Sampler' ('JAGS') software. In addition, the 'BACCT' package evaluates user-specified decision rules by computing the type-I error/power, or probability of correct go/no-go decision at interim look. The evaluation can be presented numerically or graphically. Users need to have 'JAGS' 4.0.0 or newer installed due to a compatibility issue with 'rjags' package. Currently, the package implements the BAC method for binary outcome only. Support for continuous and survival endpoints will be added in future releases. We would like to thank AbbVie's Statistical Innovation group and Clinical Statistics group for their support in developing the 'BACCT' package.