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spBayes fits univariate and multivariate models with Markov chain Monte Carlo (MCMC). Core functions include:
FSPH faculty member Sudipto Banerjee, department of Biostatistics, created this tool in collaboration with others.
This software suite implements tree-based analysis of quantitative structure activity relationships associated with exposure or escalation experiments in toxicology and nanotoxicology. Given a library of chemical or nanomaterial stressors, their physicochemical characteristics are related to biological outcomes through regression trees with exposure response models on the tree leaves. Inference is based on Bayesian model averaging, providing full predictive distributions of exposure-response relationships. The outcome can be represented as dose-escalation only or escalation protocols involving dose and time of exposure. The software allows estimating variable importance, as well as marginal influence functions. Details about models and applications can be found in Low-Kam et. al (2015).
FSPH faculty member Donatello Telesca, department of Biostatistics, created this tool in collaboration with others.
The MIC2 R package implements Multilevel Integrative Clustering of brain cortical regions, imaged via electroencephalography (EEG). The goal of the package is that of producing statistical inference about patterns of synchronous brain activity, combining and borrowing strength from data collected longitudinally on multiple subjects. Given a longitudinal measure of electrode similarity, intended as coherence, cross correlation, etc., MIC formulates a hierarchical model for grouping electrodes on the cortex. The model jointly clusters electrodes over three levels of a hierarchy, so that a representative map of clustered cortical regions are available both at the level of specific individuals and as a group summary. The package includes, some data preprocessing routines, allowing for the estimation of similarity matrices longitudinally, given segmented multivariate time series. Graphical tools for visualization of results and inference are also implemented.
While the applications context is brain imaging, the technique can be easily generalized to accommodate various scientific contexts requiring the integration of data sources collected in a hierarchical fashion. For more of its details, please refer to our manuscript here (https://arxiv.org/abs/1609.09532).
This tool was created by Damla Senturk, Catherine Sugar, Donatello Telesca, and Qian Li, department of Biostatistics.
This R package implements joint curve registration and regression techniques for functional and longitudinal data. Functional variability is related to a set of predictors through regression models of functions amplitude (summarizing the overall strength of a functional signal) and phase (summarizing average timing of functional features). Supported models, include Gaussian, Poisson and Censored Gaussian sampling. Inference is based on Markov Chain Monte Carlo (MCMC) simulation. Details about modeling capabilities can be found in: Telesca et al. (2012), Erosheva et al. (2014), and Telesca (2015).
FSPH faculty member Donatello Telesca, department of Biostatistics, created this tool in collaboration with others.
The mombf R package implements Bayesian model selection (BMS) and model averaging (BMA) for linear, asymmetric linear, median and quantile regression. This is the main package implementing the family of non-local prior (NLP) distributions (see Johnson and Rossell (2010, 2012) for a more detailed treatment), although other priors (mainly Zellner’s) are also implemented. The main features are:
Particular cases are Bayesian versions of asymmetric least squares, median and quantile regression. This manual introduces some basic notions underlying NLPs and illustrates the use of R functions implementing the main operations required for model selection and averaging. Most of these are internally implemented in C++ so, while they are not optimal in any sense they are designed to be minimally scalable to high dimensions (large p).
FSPH faculty member Donatello Telesca, department of Biostatistics, created this tool in collaboration with others.
MBA generates surfaces interpolated from scattered data using Multilevel B-Splines.
FSPH faculty member Sudipto Banerjee, department of Biostatistics, created this tool in collaboration with others.
This package fits the Bayesian two-Zone Models.
FSPH faculty member Sudipto Banerjee, department of Biostatistics, created this tool in collaboration with others.
BrokenAdaptiveRidge is an R package for performing large scale and high-dimensional L_0-based variable selection for GLM and Cox’s proportional hazards model.
FSPH faculty member Gang Li, department of Biostatistics, created this tool in collaboration with others.
controlTest is an R package for two-sample nonparametric comparison of survival quantiles. The main features are:
FSPH faculty member Gang Li, department of Biostatistics, created this tool in collaboration with others.
powerCompRisk is a power analysis tool for joint testing of cause-specific hazard and overall hazard with competing risks data using R.
FSPH faculty member Gang Li, department of Biostatistics, created this tool in collaboration with others.
JMcmprsk is an R package to fit joint models of continuous or ordinal longitudinal data and time-to-event data with competing risks.
FSPH faculty member Gang Li, department of Biostatistics, created this tool in collaboration with others.
ZIBseq is an R package to Detect abundance differences across clinical conditions. Besides, it takes the sparse nature of metagenomic data into account and handles compositional data efficiently.
FSPH faculty member Gang Li, department of Biostatistics, created this tool in collaboration with others.