Model Classifier for Binary Classification
c(
person("Choonghyun", "Ryu",, "choonghyun.ryu@gmail.com", role = c("aut", "cre"))
)
A collection of tools that support data splitting, predictive modeling, and model evaluation.
A typical function is to split a dataset into a training dataset and a test dataset.
Then compare the data distribution of the two datasets.
Another feature is to support the development of predictive models and to compare the performance of several predictive models,
helping to select the best model.
dlookr, dplyr, stats, methods, caTools, ggmosaic, tidyr,
tidyselect, unbalanced, rlang, tibble, cli, party, rpart,
ranger, MLmetrics, MASS, RColorBrewer, ROCR, future, purrr

caTools, cli (>= 1.1.0), dlookr, dplyr (>= 0.7.6), future,
ggmosaic, MASS, MLmetrics, methods, party, purrr, ROCR, ranger,
rlang, rpart, stats, tibble, tidyr, tidyselect, unbalanced,
xgboost

caTools, cli (>= 1.1.0), dlookr, dplyr (>= 0.7.6), future,
ggmosaic, MASS, MLmetrics, methods, party, purrr, ROCR, ranger,
rlang, rpart, stats, tibble, tidyr, tidyselect, unbalanced,
xgboost, glmnet

caTools, cli (>= 1.1.0), dlookr, dplyr (>= 0.7.6), future,
ggmosaic, MASS, MLmetrics, methods, party, purrr, ROCR, ranger,
rlang, rpart, stats, tibble, tidyr, tidyselect, xgboost, glmnet
knitr, ISLR, mice, mlbench, rmarkdown, stringi

caTools, cli (>= 1.1.0), dlookr, dplyr (>= 0.7.6), future,
ggmosaic, MASS, MLmetrics, methods, parallelly, party, purrr,
ROCR, ranger, rlang, rpart, stats, tibble, tidyr, tidyselect,
xgboost, glmnet

Fit GLM's with High-Dimensional k-Way Fixed Effects
c(person("Amrei", "Stammann", email = "amrei.stammann@hhu.de", role = c("aut", "cre")),
person("Daniel", "Czarnowske", email = "daniel.czarnowske@hhu.de", role = c("aut")))
Provides a routine to concentrate out factors with many levels during the
optimization of the log-likelihood function of the corresponding generalized linear model (glm).
The package is based on the algorithm proposed by Stammann (2018) <arXiv:1707.01815> and is
restricted to glm's that are based on maximum likelihood estimation and non-linear. It also offers
an efficient algorithm to recover estimates of the fixed effects in a post-estimation routine.
The package also includes robust and multi-way clustered standard errors.
data.table, Formula, MASS, Rcpp, stats, utils
Amrei Stammann [aut, cre],
Daniel Czarnowske [aut]