Description: A compositional, multivariate and univariate time series outlier ensemble.
It uses the four R packages 'forecast', 'tsoutliers', 'otsad' and 'anomalize' to detect time
series outliers (Kandanaarachchi, Menendez 2020) <doi:10.13140/RG.2.2.32217.95845>.
Suggests: knitr, rmarkdown, tourr, stringr, broom, maptools

Title: Regression estimation based on the compound covariate methed
Maintainer: Takeshi Emura, Graduate Institute of Statistics, National
Description: Calculate regression coefficients and their standard
errors under the Cox proportional hazard model with the large

Title: Regression estimation based on the compound shrinkage method
deviations under the Cox proportional hazard model with the

Description: Calculate regression coefficients and their standard deviations under the Cox proportional hazard model with the large number of covariates.

Title: Regression estimation based on the compound covariate method
Description: Calculate regression coefficients and their standard deviations under the Cox proportional hazard model with the large number of covariates. A shrinkage type estimation based on the compound covariate method (Emura, Chen & Chen, 2012 PLoS ONE) and a univariate gene selection method under dependent censoring (Emura & Chen, 2014 Statistical Methods in Medical Research) are implemented in this package.

Title: Regression Estimation Based on the Compound Covariate Method
Description: Calculate regression coefficients and their standard deviations under the Cox proportional hazard model with a large number of covariates. A shrinkage type estimation based on the compound covariate method (Emura, Chen & Chen, 2012 PLoS ONE) and a univariate gene selection method under dependent censoring (Emura & Chen, 2014 Statistical Methods in Medical Research) are implemented. Algorithms for simulating gene expressions in these references are also given.

Title: Estimation and Gene Selection Based on the Compound Covariate
Method Under the Cox Proportional Hazard Model
Author: Takeshi Emura, Hsuan-Yu Chen, Yi-Hau Chen
Description: Cox regression for survival data with high-dimensional gene expressions.
Available are statistical methods in Emura et al (2012 PLoS ONE) <DOI:10.1371/journal.pone.0047627> and
and a survival dataset for non-small-cell lung cancer patients in Chen et al (2007 New Engl J Med) <DOI:10.1056/NEJMoa060096>.