Software
lmmprobe
Sparse high-dimensional linear mixed modeling with a partitioned empirical Bayes ECM algorithm
lmmprobe is an R package extends the PROBE algorithm to sparse high-dimensional linear mixed-effect regression models. High-dimensional longitudinal data is increasingly used in a wide range of scientific studies. To properly account for dependence between longitudinal observations, statistical methods for high-dimensional linear mixed models (LMMs) have been developed. However, few packages implementing these high-dimensional LMMs are available in the statistical software R. Additionally, some packages suffer from scalability issues. This package implements Linear Mixed Modeling with PaRtitiOned empirical Bayes ECM (LMM-PROBE), an efficient and accurate Bayesian framework for high-dimensional LMMs. We use empirical Bayes estimators of hyperparameters for increased flexibility and an Expectation-Conditional-Minimization (ECM) algorithm for computationally efficient maximum a posteriori probability (MAP) estimation of parameters. The novelty of the approach lies in its partitioning and parameter expansion as well as its fast and scalable computation. See Zgodic et al. (2022) for more details.
Installation instructions for lmmprobe are available on GitHub.
probe
Sparse high-dimensional linear regression with a partitioned empirical Bayes ECM algorithm
The probe package contains the R software tools to run the PaRtitiOned empirical Bayes Ecm (PROBE) algorithm. PROBE uses minimal prior assumptions on the parameters are used through the use of plug-in empirical Bayes estimates of hyperparameters. Efficient maximum (MAP) estimation is completed through a Parameter-Expanded Expectation-Conditional-Maximization (PX-ECM) algorithm. The PX-ECM results in a robust computationally efficient coordinate-wise optimization, which adjusts for the impact of other predictor variables. The completion of the E-step uses an approach motivated by the popular two-groups approach to multiple testing. The PROBE algorithm is applied to sparse high-dimensional linear regression, which can be completed using one-at-a-time or all-at-once type optimization. PROBE is a novel alternative to Markov chain Monte Carlo, empirical Bayes, and Variational Bayes approaches to fitting sparse linear models. See McLain et al. (2022) for more details.
Installation instructions for probe are available on GitHub. The probe package is available on CRAN with documentation for use.