Xuan You <youxuan90@gmail.com>, Yichen Cheng <ycheng11@gsu.edu>
Includes R functions for the estimation of tumor clones percentages for both snp data and (whole) genome sequencing data. See Cheng, Y., Dai, J. Y., Paulson, T. G., Wang, X., Li, X., Reid, B. J., & Kooperberg, C. (2017). Quantification of multiple tumor clones using gene array and sequencing data. The Annals of Applied Statistics, 11(2), 967-991, <doi:10.1214/17-AOAS1026> for more details.

Estimation of the jump rate and simulation for
piecewise-deterministic Markov processes
This package deals with the estimation of the jump rate
for piecewise-deterministic Markov processes (PDMPs), from only
one observation of the process within a long time. The main
functions provide an estimate of this function. The state space
may be discrete or continuous. The associated paper is given in
References. Other functions provide a method to simulate random
variables from their (conditional) hazard rate, and then to
simulate PDMPs.

This package deals with the estimation of the jump rate for piecewise-deterministic Markov processes (PDMPs), from only one observation of the process within a long time. The main functions provide an estimate of this function. The state space may be discrete or continuous. The associated paper has been published in Scandinavian Journal of Statistics and is given in references. Other functions provide a method to simulate random variables from their (conditional) hazard rate, and then to simulate PDMPs.

Diagnostic Tools and Unit Tests for Statistical Estimators
person("Dmitry", "Otryakhin", email = "d.otryakhin.acad@protonmail.ch", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-4700-7221"))
Dmitry Otryakhin [aut, cre] (<https://orcid.org/0000-0002-4700-7221>)
Extension of 'testthat' package to make unit tests on empirical distributions of estimators and functions for diagnostics of their finite-sample performance.

Perform the Joint Graphical Lasso and Selects Tuning Parameters
Can be used to simultaneously estimate networks (Gaussian Graphical Models) in data from different groups or classes via Joint Graphical Lasso. Tuning parameters are selected via information criteria (AIC / BIC / eBIC) or crossvalidation.