Author: Haoming Jiang, Yukun Ma, Han Liu, Kathryn Roeder, Xingguo Li, and Tuo Zhao

Title: Selection and Misclassification Bias Adjustment for Logistic
person("Alexander", "Rix", email = "alexrix@umich.edu", role = "cre"),
person("Lauren", "Beesley", email = "lbeesley@umich.edu", role = "aut")
Health research using data from electronic health records (EHR) has gained
popularity, but misclassification of EHR-derived disease status and lack of
representativeness of the study sample can result in substantial bias in
effect estimates and can impact power and type I error for association
tests. Here, the assumed target of inference is the relationship between
binary disease status and predictors modeled using a logistic regression
model. 'SAMBA' implements several methods for obtaining bias-corrected
point estimates along with valid standard errors as proposed in Beesley and
Suggests: knitr, rmarkdown, ggplot2, scales, MASS

Title: Stochastic Approximation Monte Carlo (SAMC) Sampler and Methods
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"))
Description: 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.
Imports: Rcpp, RcppXPtrUtils, utils, Rdpack
Suggests: knitr, rmarkdown, microbenchmark, pander, geoR, RandomFields

Title: A Semi-Supervised Method for Prediction of Phenotype Event Times
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"))
Description: A novel semi-supervised machine learning algorithm to predict phenotype event times using Electronic Health Record (EHR) data.
Imports: stats, mvtnorm, nlme, pROC, abind, nloptr, foreach,
Packaged: 2021-01-04 02:54:21 UTC; yuriahuja