Analysis and Visualization of Macroevolutionary Dynamics on Phylogenetic Trees

Provides functions for analyzing and visualizing complex macroevolutionary dynamics on phylogenetic trees. It is a companion package to the command line program BAMM (Bayesian Analysis of Macroevolutionary Mixtures) and is entirely oriented towards the analysis, interpretation, and visualization of evolutionary rates. Functionality includes visualization of rate shifts on phylogenies, estimating evolutionary rates through time, comparing posterior distributions of evolutionary rates across clades, comparing diversification models using Bayes factors, and more.

Flexible Genotyping for Polyploids

Implements empirical Bayes approaches to genotype polyploids from next generation sequencing data while accounting for allele bias, overdispersion, and sequencing error. The main functions are flexdog() and multidog(), which allow the specification of many different genotype distributions. Also provided are functions to simulate genotypes, rgeno(), and read-counts, rflexdog(), as well as functions to calculate oracle genotyping error rates, oracle_mis(), and correlation with the true genotypes, oracle_cor(). These latter two functions are useful for read depth calculations. Run browseVignettes(package = "updog") in R for example usage. See Gerard et al. (2018) and Gerard and Ferrao (2020) for details on the implemented methods.

Gaussian Mixture Models (GMM)

Multimodal distributions can be modelled as a mixture of components. The model is derived using the Pareto Density Estimation (PDE) for an estimation of the pdf. PDE has been designed in particular to identify groups/classes in a dataset. Precise limits for the classes can be calculated using the theorem of Bayes. Verification of the model is possible by QQ plot, Chi-squared test and Kolmogorov-Smirnov test. The package is based on the publication of Ultsch, A., Thrun, M.C., Hansen-Goos, O., Lotsch, J. (2015) .

Model Menu for Radiant: Business Analytics using R and Shiny

The Radiant Model menu includes interfaces for linear and logistic regression, naive Bayes, neural networks, classification and regression trees, model evaluation, collaborative filtering, decision analysis, and simulation. The application extends the functionality in 'radiant.data'.

Model Wrappers for Discriminant Analysis

Bindings for additional classification models for use with the 'parsnip' package. Models include flavors of discriminant analysis, such as linear (Fisher (1936) ), regularized (Friedman (1989) ), and flexible (Hastie, Tibshirani, and Buja (1994) ), as well as naive Bayes classifiers (Hand and Yu (2007) ).

Replicability Analysis for Multiple Studies of High Dimension

Estimation of Bayes and local Bayes false discovery rates for replicability analysis (Heller & Yekutieli, 2014 ; Heller at al., 2015 ).

Density Estimation via Bayesian Inference Engines

Bayesian density estimates for univariate continuous random samples are provided using the Bayesian inference engine paradigm. The engine options are: Hamiltonian Monte Carlo, the no U-turn sampler, semiparametric mean field variational Bayes and slice sampling. The methodology is described in Wand and Yu (2020) .

Bayesian Model Averaging for Random and Fixed Effects Meta-Analysis

Computes the posterior model probabilities for standard meta-analysis models (null model vs. alternative model assuming either fixed- or random-effects, respectively). These posterior probabilities are used to estimate the overall mean effect size as the weighted average of the mean effect size estimates of the random- and fixed-effect model as proposed by Gronau, Van Erp, Heck, Cesario, Jonas, & Wagenmakers (2017, ). The user can define a wide range of non-informative or informative priors for the mean effect size and the heterogeneity coefficient. Moreover, using pre-compiled Stan models, meta-analysis with continuous and discrete moderators with Jeffreys-Zellner-Siow (JZS) priors can be fitted and tested. This allows to compute Bayes factors and perform Bayesian model averaging across random- and fixed-effects meta-analysis with and without moderators. For a primer on Bayesian model-averaged meta-analysis, see Gronau, Heck, Berkhout, Haaf, & Wagenmakers (2021, ).

Bayesian Restricted Mean Survival Time for Cluster Effect

The parametric Bayes analysis for the restricted mean survival time (RMST) with cluster effect, as described in Hanada and Kojima (2024) . Bayes estimation with random-effect and frailty-effect can be applied to several parametric models useful in survival time analysis. The RMST under these parametric models can be computed from the obtained posterior samples.

Bayesian Change-Point Detection for Process Monitoring with Fault Detection

Bayes Watch fits an array of Gaussian Graphical Mixture Models to groupings of homogeneous data in time, called regimes, which are modeled as the observed states of a Markov process with unknown transition probabilities. In doing so, Bayes Watch defines a posterior distribution on a vector of regime assignments, which gives meaningful expressions on the probability of every possible change-point. Bayes Watch also allows for an effective and efficient fault detection system that assesses what features in the data where the most responsible for a given change-point. For further details, see: Alexander C. Murph et al. (2023) .