Stochastic Approximation Monte Carlo (SAMC) Sampler and Methods
c(person("Yichen","Cheng",role="aut",email="ycheng11@gsu.edu"),
person("Ick Hoon","Jin", role = "aut", email="IckHoon.Jin.32@nd.edu"),
person("Faming","Liang", role = "aut", email = "fmliang@purdue.edu"),
person("Kisung","You", role = c("aut", "cre"), email = "kyou@nd.edu"))
Stochastic Approximation Monte Carlo (SAMC) is one of the celebrated Markov chain Monte Carlo (MCMC) algorithms. It is known to be capable of sampling from multimodal or doubly intractable distributions. We provide generic SAMC samplers for continuous distributions. User-specified densities in R and C++ are both supported. We also provide functions for specific problems that exploit SAMC computation. See Liang et al (2010) <doi:10.1002/9780470669723> for complete introduction to the method.
knitr, rmarkdown, microbenchmark, pander, geoR, RandomFields
Yichen Cheng [aut],
Ick Hoon Jin [aut],
Faming Liang [aut],
Kisung You [aut, cre]

A Semi-Supervised Method for Prediction of Phenotype Event Times
c(
person("Yuri", "Ahuja", role = c("aut", "cre"), email = "Yuri_Ahuja@hms.harvard.edu"),
person("Tianxi", "Cai", role = c("aut")),
person(family = "PARSE LTD", role = c("aut"))
)
A novel semi-supervised machine learning algorithm to predict phenotype event times using Electronic Health Record (EHR) data.
stats, mvtnorm, nlme, pROC, abind, nloptr, foreach,
doParallel, parallel, Rcpp
Yuri Ahuja [aut, cre],
Tianxi Cai [aut],
PARSE LTD [aut]

Programs for fitting Gaussian linear mixed models (LMM).

This program can be used to fit Gaussian linear mixed models (LMM). Univariate and multivariate response models, multiple variance components, as well as, certain correlation and covariance structures are supported. In many occasions, the user can pick one of the several mixed model fitting algorithms, which are explained further in the details section. Some algorithms are specific to certain types of models (univariate or multivariate, diagonal or non-diagonal residual, one or multiple variance components, etc,...).