Adaptive Lasso for the Cox Regression with Interval Censored and Possibly Left Truncated Data

Penalized variable selection tools for the Cox proportional hazards model with interval censored and possibly left truncated data. It performs variable selection via penalized nonparametric maximum likelihood estimation with an adaptive lasso penalty. The optimal thresholding parameter can be searched by the package based on the profile Bayesian information criterion (BIC). The asymptotic validity of the methodology is established in Li et al. (2019 ). The unpenalized nonparametric maximum likelihood estimation for interval censored and possibly left truncated data is also available.


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install.packages("ALassoSurvIC")

0.1.0 by Daewoo Pak, 4 months ago


Browse source code at https://github.com/cran/ALassoSurvIC


Authors: Chenxi Li , Daewoo Pak and David Todem


Documentation:   PDF Manual  


GPL (>= 3) license


Imports Rcpp, parallel

Linking to Rcpp


See at CRAN