Softwares

Packages

DTRlearnDynamic treatment regimens (DTRs) are sequential decision rules tailored at each stage by time-varying subject-specific features and intermediate outcomes observed in previous stages. This package implements three methods: O-learning, Q-learning and P-learning (Liu et. al. 2014, 2015) to estimate the optimal DTRs.

GSSE: 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.

Coxnet:  Cox model regularized with net (L1 and Laplacian), elastic-net (L1 and L2) or lasso (L1) penalty. In addition, it can truncate the estimate by a hard threshold, which is selected simultaneously with other tuning parameters. Moreover, it can handle the adaptive version of these regularization forms, such as adaptive lasso and net adjusting for signs of linked coefficients. The package uses one-step coordinate descent algorithm and runs extremely fast by taking into account the sparsity structure of coefficients. Download Report.

APML0 (previously ADMMnet): Fit linear and cox models regularized with net (L1 and Laplacian), elastic-net (L1 and L2), lasso (L1), or L0 penalty, and their adaptive forms, such as adaptive lasso and net adjusting for signs of linked coefficients. In addition, it treats the number of non-zero coefficients as another tuning parameter and simultaneously selects with the regularization parameter. The package uses one-step coordinate descent algorithm and runs extremely fast by taking into account the sparsity structure of coefficients.

R codes

mSEM : Fit mixed effects structural equation models to estimate directed acyclic graphs (DAGs) from observational data. Reference: Li et al. (2018).