Title: DMR Detection by Non-Homogeneous Hidden Markov Model from
Description: Perform differential analysis for
methylation array data. Detect differentially
methylated regions (DMRs) from array M-values.
The core is a Non-homogeneous Hidden Markov Model
for estimating spatial correlation and a novel Constrained
Gaussian Mixture Model for modeling the M-value pairs of each individual locus.

Title: Delete or Merge Regressors Algorithms for Linear and Logistic
Model Selection and High-Dimensional Data
Author: Agnieszka Prochenka-Sołtys, Piotr Pokarowski
Description: Model selection algorithms for regression and classification, where the predictors can be numerical and categorical and the number of regressors exceeds the number of observations. The selected model consists of a subset of numerical regressors and partitions of levels of factors. Aleksandra Maj-Kańska, Piotr Pokarowski and Agnieszka Prochenka (2015) <doi:10.1214/15-EJS1050>. Piotr Pokarowski and Jan Mielniczuk (2015) <http://www.jmlr.org/papers/volume16/pokarowski15a/pokarowski15a.pdf>.
Imports: glmnet, grpreg, stats, graphics, utils

Authors@R: c(person("Agnieszka", "Prochenka-Sołtys", email = "ap220756@mimuw.edu.pl", role = c("aut"), comment = "previous maintainer for versions <= 0.2.0"),
person("Piotr", "Pokarowski", role = c("aut")),
person("Szymon", "Nowakowski", email = "s.nowakowski@mimuw.edu.pl", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-1939-9512")))
Description: Model selection algorithms for regression and classification, where the predictors can be continuous or categorical and the number of regressors may exceed the number of observations. The selected model consists of a subset of numerical regressors and partitions of levels of factors. Aleksandra Maj-Kańska, Piotr Pokarowski and Agnieszka Prochenka, 2015. Delete or merge regressors for linear model selection. Electronic Journal of Statistics 9(2): 1749-1778. <https://projecteuclid.org/euclid.ejs/1440507392>. Piotr Pokarowski and Jan Mielniczuk, 2015. Combined l1 and greedy l0 penalized least squares for linear model selection. Journal of Machine Learning Research 16(29): 961-992. <https://www.jmlr.org/papers/volume16/pokarowski15a/pokarowski15a.pdf>. Piotr Pokarowski, Wojciech Rejchel, Agnieszka Sołtys, Michał Frej and Jan Mielniczuk, 2022. Improving Lasso for model selection and prediction. Scandinavian Journal of Statistics, 49(2): 831–863. <doi:10.1111/sjos.12546>.
Author: Agnieszka Prochenka-Sołtys [aut] (previous maintainer for versions <=
Szymon Nowakowski [aut, cre] (<https://orcid.org/0000-0002-1939-9512>)