Extended and enhanced hierarchical logistic regression models (called Huisman-Olff-Fresco in biology, see Huisman et al. 1993 JVS <doi:10.1111/jvs.12050>) models. Response curves along one-dimensional gradients including no response, monotone, plateau, unimodal and bimodal models.

Model-Based Gene Clustering for Genomics Data from
Paired/Matched Designs
Yunfeng Li <colinlee1999@gmail.com> and Weiliang Qiu <stwxq@channing.harvard.edu>
Perform model based gene clustering for genomics data generated from paired/matched design based on mixture of extended lognormal normal Bayesian hierarchical models (See Li Y, Morrow J, Raby B, Tantisira K, Weiss ST, Huang W, Qiu W. (2017), <doi:10.1371/journal.pone.0174602>).
graphics, methods, utils, iCheck, samr, GSEAlm, globaltest

Create list comprehensions (and other types of comprehension) similar to those in
'python', 'haskell', and other languages. List comprehension in 'R' converts a
regular for() loop into a vectorized lapply() function. Support for looping
with multiple variables, parallelization, and across non-standard objects included. Package
also contains a variety of functions to help with list comprehension and
higher order functions for manipulating lists.

Create list comprehensions (and other types of comprehension) similar to those in
'python', 'haskell', and other languages. List comprehension in 'R' converts a
regular for() loop into a vectorized lapply() function. Support for looping
with multiple variables, parallelization, and across non-standard objects included. Package
also contains a variety of functions to help with list comprehension.

Fitting log-Linear Models in Sparse Contingency Tables
Log-linear modeling is a popular method for the analysis of contingency table data. When the table is sparse, the data can fall on the boundary of the convex support, and we say that "the MLE does not exist" in the sense that some parameters cannot be estimated. However, an extended MLE always exists, and a subset of the original parameters will be estimable. The 'eMLEloglin' package determines which sampling zeros contribute to the non-existence of the MLE. These problematic zero cells can be removed from the contingency table and the model can then be fit (as far as is possible) using the glm() function.