Found 2024 packages in 0.01 seconds
Fitting a Bayesian Sparse Latent Factor Model in Gene Expression Analysis
Set of tools to find coherent patterns in gene expression (microarray) data using a Bayesian Sparse Latent Factor Model (SLFM)
Bayesian Solutions for the Factor Zoo: We Just Ran Two Quadrillion Models
Contains the functions to use the econometric methods in the paper Bryzgalova, Huang, and Julliard (2023)
Overfitting Bayesian Mixtures of Factor Analyzers with Parsimonious Covariance and Unknown Number of Components
Model-based clustering of multivariate continuous data using Bayesian mixtures of factor analyzers (Papastamoulis (2019)
Dynamic ICAR Spatiotemporal Factor Models
Bayesian factor models are effective tools for dimension reduction. This is especially applicable to multivariate large-scale datasets. It allows researchers to understand the latent factors of the data which are the linear or non-linear combination of the variables. Dynamic Intrinsic Conditional Autocorrelative Priors (ICAR) Spatiotemporal Factor Models 'DIFM' package provides function to run Markov Chain Monte Carlo (MCMC), evaluation methods and visual plots from Shin and Ferreira (2023)
Bayesian Latent Variable Analysis
Fit a variety of Bayesian latent variable models, including confirmatory
factor analysis, structural equation models, and latent growth curve models. References: Merkle & Rosseel (2018)
Tools for Working with Categorical Variables (Factors)
Helpers for reordering factor levels (including moving specified levels to front, ordering by first appearance, reversing, and randomly shuffling), and tools for modifying factor levels (including collapsing rare levels into other, 'anonymising', and manually 'recoding').
Bayes Factors, Model Choice and Variable Selection in Linear Models
Bayes factors and posterior probabilities in Linear models, aimed at provide a formal Bayesian answer to testing and variable selection problems.
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,
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.
All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS) and Estimating Multivariate Volatility Models
Multivariate Time Series (MTS) is a general package for analyzing multivariate linear time series and estimating multivariate volatility models. It also handles factor models, constrained factor models, asymptotic principal component analysis commonly used in finance and econometrics, and principal volatility component analysis. (a) For the multivariate linear time series analysis, the package performs model specification, estimation, model checking, and prediction for many widely used models, including vector AR models, vector MA models, vector ARMA models, seasonal vector ARMA models, VAR models with exogenous variables, multivariate regression models with time series errors, augmented VAR models, and Error-correction VAR models for co-integrated time series. For model specification, the package performs structural specification to overcome the difficulties of identifiability of VARMA models. The methods used for structural specification include Kronecker indices and Scalar Component Models. (b) For multivariate volatility modeling, the MTS package handles several commonly used models, including multivariate exponentially weighted moving-average volatility, Cholesky decomposition volatility models, dynamic conditional correlation (DCC) models, copula-based volatility models, and low-dimensional BEKK models. The package also considers multiple tests for conditional heteroscedasticity, including rank-based statistics. (c) Finally, the MTS package also performs forecasting using diffusion index , transfer function analysis, Bayesian estimation of VAR models, and multivariate time series analysis with missing values.Users can also use the package to simulate VARMA models, to compute impulse response functions of a fitted VARMA model, and to calculate theoretical cross-covariance matrices of a given VARMA model.