R Development Core Team

Methods and functions for fitting maximum likelihood models in R.

This model fitting tool incorporates cyclic coordinate descent and majorization-minimization approaches to fit a variety of regression models found in large-scale observational healthcare data. Implementations focus on computational optimization and fine-scale parallelization to yield efficient inference in massive datasets. Please see: Suchard, Simpson, Zorych, Ryan and Madigan (2013) <doi:10.1145/2414416.2414791>.

Provides a function (echo_find()) designed to find rhythms from data using extended harmonic oscillators. For more information, see H. De los Santos et al. (2020) <doi:10.1093/bioinformatics/btz617> .

Performs meta-analysis and meta-regression using standard and robust methods with confidence intervals based on the profile likelihood. Robust methods are based on alternative distributions for the random effect, either the t-distribution (Lee and Thompson, 2008 <doi:10.1002/sim.2897> or Baker and Jackson, 2008 <doi:10.1007/s10729-007-9041-8>) or mixtures of normals (Beath, 2014 <doi:10.1002/jrsm.1114>).

Provides a function (mosaic_find()) designed to find rhythmic and non-rhythmic trends in multi-omics time course data using model selection and joint modeling, a method called MOSAIC (Multi-Omics Selection with Amplitude Independent Criteria). For more information, see H. De los Santos et al. (2020) <doi:10.1093/bioinformatics/btaa877>.