Title: Non-Parametric Maximum Likelihood Estimate for Cohort Samplings
Description: To compute the non-parametric maximum likelihood estimates (NPMLEs) and penalized NPMLEs with SCAD, HARD and LASSO penalties for nested case-control or case-cohort sampling design with time matching under Cox's regression model. It also proposes the standard error formula for estimator using observed profile likelihood. For details about (penalized) NPNLEs see the original paper "Penalized Full Likelihood Approach to Variable Selection for Cox's Regression Model under Nested Case-Control Sampling" by Wang et al. (2019) <doi:10.1007/s10985-019-09475-z>.
Author: Jie-Huei Wang, Chun-Hao Pan, I-Shou Chang, and Chao A. Hsiung
Depends: R (>= 3.4.3), MASS, survival, splines

Title: Non-parametric Maximum-Likelihood Estimation for Competing-Risks
Author: Chung-Hsing Chen <chchen@nhri.org.tw>, I-Shou Chang <ischang@nhri.org.tw> and Chao A. Hsiung <hsiung@nhri.org.tw>
Description: The package computes non-parametric Maximum-Likelihood Estimate and its asymptotic variance in a semiparametric mixture model for competing-risks data, as described in Chang et al. (2007b).

Title: Type-specific Failure Rate and Hazard Rate on Competing Risks
Author: Chung-Hsing Chen, I-Shou Chang and Chao A. Hsiung
Description: Given a failure type, the function computes covariate-specific probability of failure over time and covariate-specific conditional hazard rate based on possibly right-censored competing risk data. Specifically, it computes the non-parametric maximum-likelihood estimates of these quantities and their asymptotic variances in a semi-parametric mixture model for competing-risks data, as described in Chang et al. (2007a).

Title: Type-Specific Failure Rate and Hazard Rate on Competing Risks

Title: Two-Groups Mixture Model with Covariates
person( "Nabarun", "Deb", email=  "nd2560@columbia.edu", role =  c("aut", "cre")),
person("Sujayam", "Saha", email =  "sujayam@berkeley.edu", role = "ctb"),
person("Adityanand", "Guntuboyina", email = "aditya@stat.berkeley.edu", role =  "ctb"),
person("Bodhisattva", "Sen", email = "bodhi@stat.columbia.edu", role =  "ctb")
Description: We develop three procedures for estimation in a two-groups model with covariates. One of them is a nonparametric maximum likelihood estimation based approach when the signal distribution is an infinite Gaussian location mixture and the signal proportion is a logistic function of the available covariates. Two other functions - marg1() and marg2() have also been implemented for inference in the above framework. All these methods can be used for inference in multiple hypotheses testing. For more information, see the paper: Deb, Saha, Guntuboyina and Sen (2019),``Two-component Mixture Model in the Presence of Covariates'' <arXiv:1810.07897v2>.
SystemRequirements: MOSEK (http://www.mosek.com) and MOSEK license.
Imports: R.utils, dplyr, spatstat, methods, latexpdf, Matrix, FDRreg,
Rcpp, REBayes, CAMAN, progress, pbapply, Hmisc, pracma, mosaic
Packaged: 2020-11-25 03:40:50 UTC; Nabarun Deb