We propose a fully efficient sieve maximum likelihood method to estimate genotype-specific distribution of time-to-event outcomes under a nonparametric model. We can handle missing genotypes in pedigrees.  We estimate the time-dependent hazard ratio between two genetic mutation groups using B-splines, while applying nonparametric maximum likelihood estimation to the reference baseline hazard function.  The estimators are calculated via an expectation-maximization algorithm.
Baosheng Liang, Yuanjia Wang and Donglin Zeng

Sayanti Guha Majumdar, Anil Rai, Dwijesh Chandra Mishra
Genomic selection is a specialized form of marker assisted selection. The package contains functions to select important genetic markers and predict phenotype on the basis of fitted training data using integrated model framework (Guha Majumdar et. al. (2019) <doi:10.1089/cmb.2019.0223>) developed by combining one additive (sparse additive models by Ravikumar et. al. (2009) <doi:10.1111/j.1467-9868.2009.00718.x>) and one non-additive (hsic lasso by Yamada et. al. (2014) <doi:10.1162/NECO_a_00537>) model.

c(person("Yaohua", "Hu", email = "mayhhu@szu.edu.cn", role = c("aut","cre","cph")),person("Xinlin", "Hu", email = "ramakstt@gmail.com", role = c("trl")))
Approaches a group sparse solution of an underdetermined linear system. It implements the proximal gradient algorithm to solve a lower regularization model of group sparse learning. For details, please refer to the paper "Y. Hu, C. Li, K. Meng, J. Qin and X. Yang. Group sparse optimization via l_{p,q} regularization. Journal of Machine Learning Research, to appear, 2017".
Yaohua Hu [aut, cre, cph],
Xinlin Hu [trl]

The Generalized Time-Dependent Logistic Family
c(person(given="Jalmar",
family="Carrasco",
role = c("aut","cre"),
email="carrascojalmar@gmail.com"),
person(given="Luciano",
family="Santana",
role = "aut"),
person(given="Lizandra",
family="Fabio",
role = "aut"))
Jalmar Carrasco [aut, cre],
Luciano Santana [aut],
Lizandra Fabio [aut]
Computes the  probability density, survival function,
the hazard rate functions and generates random samples from the
GTDL distribution given by Mackenzie, G. (1996) <doi:10.2307/2348408>.
The likelihood estimates, the randomized quantile (Louzada, F., et al.
(2020) <doi:10.1109/ACCESS.2020.3040525>)
residuals and the normally transformed randomized survival
probability (Li,L., et al. (2021) <doi:10.1002/sim.8852>)
residuals are obtained for the GTDL model.