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.
Suggests: knitr, rmarkdown, stats, parallel

also contains a variety of functions to help with list comprehension.

Title: Fitting log-Linear Models in Sparse Contingency Tables
Description: 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.

Title: Ensemble Methods for Multiple Change-Point Detection
Maintainer: Karolos K. Korkas <kkorkas@yahoo.co.uk>
Description: Implements a segmentation algorithm for multiple change-point detection in univariate time series using the Ensemble Binary Segmentation of Korkas (2020) <arXiv:2003.03649>.
Imports: Rcpp (>= 0.12.12), foreach, iterators, doParallel, methods,

Title: Quantitative Exploration of Elastic Net Families for Generalized
Imports: glmnet, stats, graphics, methods, grDevices, Matrix, progress,
Description: Provides a quantitative toolkit to explore elastic net families and to uncover correlates contributing to prediction under a cross-validation framework. Fits linear, binomial (logistic) and multinomial models. Candia J and Tsang JS (2018), bioRxiv 305870 (under review).