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High-dimensional variable selection in the proportional hazards (PH) model has many successful applications in different areas. In practice, data may involve confounding variables that do not satisfy the PH assumption, in which case the stratified proportional hazards (SPH) model can be adopted to control the confounding effects by stratification without directly modeling the confounding effects. However, there is a lack of computationally efficient statistical software for high-dimensional variable selection in the SPH model. In this work an R package, SurvBoost, is developed to implement the gradient boosting algorithm for fitting the SPH model with high-dimensional covariate variables. Simulation studies demonstrate that in many scenarios SurvBoost can achieve better selection accuracy and reduce computational time substantially compared to the existing R package that implements boosting algorithms without stratification. The proposed R package is also illustrated by an analysis of gene expression data with survival outcome in The Cancer Genome Atlas study. In addition, a detailed hands-on tutorial for SurvBoost is provided. [ABSTRACT FROM AUTHOR]

We discuss implementation of a profile likelihood method for estimating a Pearson correlation coefficient from bivariate data with censoring and/or missing values. The method is implemented in an R package clikcorr which calculates maximum likelihood estimates of the correlation coefficient when the data are modeled with either a Gaussian or a Student t-distribution, in the presence of left, right, or interval censored and/or missing data. The R package includes functions for conducting inference and also provides graphical functions for visualizing the censored data scatter plot and profile log likelihood function. The performance of clikcorr in a variety of circumstances is evaluated through extensive simulation studies. We illustrate the package using two dioxin exposure datasets. [ABSTRACT FROM AUTHOR]