Packaged: 2019-09-29 21:14:35 UTC; ludvigolsen

Packaged: 2019-12-01 19:05:13 UTC; ludvigolsen

dplyr (>= 0.8.5), ggimage (>= 0.2.7), ggplot2, lifecycle, lme4
recipes (>= 0.1.10), rlang (>= 0.4.0), rsvg, stats, stringr,
rmarkdown, testthat (>= 2.3.2), xpectr (>= 0.3.0)
Packaged: 2020-04-13 22:22:41 UTC; ludvigolsen

dplyr (>= 0.8.5), ggplot2, lifecycle, lme4 (>= 1.1-23), MuMIn
randomForest (>= 4.6-14), rmarkdown, rsvg, testthat (>= 2.3.2),
Packaged: 2020-04-17 22:36:12 UTC; ludvigolsen

Packaged: 2020-05-28 17:13:16 UTC; ludvigolsen

Imports: broom (>= 0.7.1), broom.mixed (>= 0.2.6), checkmate (>=
stringr, tibble (>= 3.0.3), tidyr (>= 1.1.2), utils
Packaged: 2020-10-07 20:43:18 UTC; ludvigolsen

nnet (>= 7.3-12), randomForest (>= 4.6-14), rmarkdown, rsvg,
Packaged: 2020-10-18 21:23:57 UTC; ludvigolsen

Packaged: 2021-02-17 13:19:48 UTC; ludvigolsen

parameters (>= 0.13.0), plyr, pROC (>= 1.16.0), purrr, rearrr
Packaged: 2021-06-07 14:00:56 UTC; ludvigolsen

Packaged: 2021-06-17 10:27:04 UTC; ludvigolsen

Packaged: 2021-10-05 18:28:53 UTC; ludvigolsen

parameters (>= 0.15.0), plyr, pROC (>= 1.16.0), purrr, rearrr
Packaged: 2021-11-14 16:51:52 UTC; ludvigolsen

merDeriv (>= 0.2-4), nnet (>= 7.3-12), randomForest (>=
4.6-14), rmarkdown, rsvg, testthat (>= 2.3.2), xpectr (>=
Packaged: 2022-07-14 16:25:49 UTC; ludvigolsen

Packaged: 2022-08-26 13:10:41 UTC; ludvigolsen

ggplot2, groupdata2 (>= 2.0.1), lifecycle, lme4 (>= 1.1-23),
(>= 7.3-12), randomForest (>= 4.6-14), rmarkdown, rsvg,

Title: Majorization Minimization by Coordinate Descent Algorithm for
Concave Penalized Logistic Regression for High Dimensional Data
Description: The package uses majorization minimization by coordinate
descent (MMCD) algorithm to compute the solution surface for
concave penalized logistic regression models. The SCAD and MCP
(default) are two concave penalties considered in this
implementation. The package provides three types of solutions
surfaces, one computed along the regulation parameter kappa
(default), the one along the penalty parameter lambda, and the
one computed using a hybrid algorithm. The package also
provides three tuning parameter selection methods, one based on
AIC, one based on BIC and one based on k-fold Cross-validated