Delete or Merge Regressors Algorithms for Linear and Logistic
Model Selection and High-Dimensional Data
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>.

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")))
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>.
Agnieszka Prochenka-Sołtys [aut] (previous maintainer for versions <=
0.2.0),
Piotr Pokarowski [aut],
Szymon Nowakowski [aut, cre] (<https://orcid.org/0000-0002-1939-9512>)