Survival Trees to Fit Left-Truncated and Right-Censored Survival
Data
Recursive partition algorithms designed for fitting survival tree
with left-truncated and right censored (LTRC) data. Such LTRC trees can also
be used to fit survival tree with time-varying covariates.
c(
person("Wei", "Fu",  email = "weifu.scott@gmail.com", role = c("aut", "cre")),
person("Jeffrey", "Simonoff", role = "aut")
)
Wei Fu [aut, cre],
Jeffrey Simonoff [aut]

Survival Trees to Fit Left-Truncated and Right-Censored and
Interval-Censored Survival Data
Recursive partition algorithms designed for fitting survival tree with left-truncated and right censored (LTRC) data, as well as interval-censored data.
The LTRC trees can also be used to fit survival tree with time-varying covariates.

partykit (>= 1.2.0), rpart, survival, interval,inum

partykit (>= 1.2.0), rpart, survival, inum, icenReg
Formula, rpart.plot, knitr, rmarkdown, interval

partykit (>= 1.2.0), rpart, survival, inum, icenReg, Icens,
interval

Latent Unknown Clustering with Integrated Data
An implementation for the 'LUCID' method to jointly estimate latent unknown clusters/subgroups with integrated data. An EM algorithm is used to obtain the latent cluster assignment and model parameter estimates. Feature selection is achieved by applying the regularization method.
mvtnorm, nnet, glmnet, glasso, Matrix, lbfgs, stats, methods,
networkD3, foreach, doParallel

mvtnorm, nnet, glmnet, glasso, Matrix, lbfgs, stats, methods,
boot, networkD3, foreach, doParallel

Yinqi Zhao, Cheng Peng, Zhao Yang, David V. Conti
An implementation for the 'LUCID' model (Peng (2019) <doi:10.1093/bioinformatics/btz667>) to jointly estimate latent unknown clusters/subgroups with integrated data.
An EM algorithm is used to obtain the latent cluster assignment and model parameter estimates.
Feature selection is achieved by applying the L1 regularization method.
mclust, nnet, networkD3, parallel, boot, lbfgs, glasso, glmnet

Yinqi Zhao, David V. Conti, Cheng Peng, Zhao Yang

An implementation of LUCID model (Peng (2019) <doi:10.1093/bioinformatics/btz667>).
LUCID conducts integrated clustering using exposures, omics data (and outcome
of interest). An EM algorithm is implemented to estimate MLE of LUCID model.
LUCID features integrated variable selection, incorporation of missing omics
data, bootstrap inference and visualization via Sankey diagram.
mclust, nnet, networkD3, parallel, boot, glasso, glmnet,
jsonlite