Perform the functional modeling methods of Huang and Wang (2018) <doi:10.1111/biom.12741> to accommodate dependent error in covariates of the proportional hazards model. The adopted measurement error model has minimal assumptions on the dependence structure, and an instrumental variable is supposed to be available.

Perform variable selection for Cox regression model with interval-censored data. Can deal with both low-dimensional and high-dimensional data. Case-cohort design can be incorporated. Two sets of covariates scenario can also be considered. The references are listed in the URL below.

Calculate the likelihood ratio test p-value and likelihood confidence intervals for misspecified Cox models, as described in Shao and Guo (2025) <doi:10.48550/arXiv.2508.11851>.

Fit Cox proportional hazards models containing both 
 fixed and random effects.  The random effects can have a general form, of which
 familial interactions (a "kinship" matrix) is a particular special case. 
 Note that the simplest case of a mixed effects Cox model, i.e. a single random 
 per-group intercept, is also called a "frailty" model.  The approach is based
 on Ripatti and Palmgren, Biometrics 2002.

A genome-wide survival framework that integrates sequential conditional independent tuples and saddlepoint approximation method, to provide SNP-level false discovery rate control while improving power, particularly for biobank-scale survival analyses with low event rates. The method is based on model-X knockoffs as described in Barber and Candes (2015) <doi:10.1214/15-AOS1337> and fast survival analysis methods from Bi et al. (2020) <doi:10.1016/j.ajhg.2020.06.003>. A shrinkage algorithmic leveraging accelerates multiple knockoffs generation in large genetic cohorts. This CRAN version uses standard Cox regression for association testing. For enhanced performance on very large datasets, users may optionally install the 'SPACox' package from GitHub which provides saddlepoint approximation methods for survival analysis.