Description: Recommended Learners for 'mlr3'. Extends 'mlr3'
with interfaces to essential machine learning packages on CRAN.
This includes, but is not limited to: (penalized) linear and logistic
regression, linear and quadratic discriminant analysis, k-nearest neighbors,
naive Bayes, support vector machines, and gradient boosting.
Imports: data.table, mlr3 (>= 0.1.1), mlr3misc, paradox, R6
Suggests: checkmate, DiceKriging, e1071, glmnet, kknn, lgr, MASS,

with interfaces to essential machine learning packages on CRAN.  This
includes, but is not limited to: (penalized) linear and logistic
regression, linear and quadratic discriminant analysis, k-nearest
neighbors, naive Bayes, support vector machines, and gradient
Imports: data.table, mlr3 (>= 0.1.2), mlr3misc, paradox, R6

Imports: data.table, mlr3 (>= 0.1.4), mlr3misc (>= 0.1.5), paradox, R6
Suggests: bibtex, checkmate, DiceKriging, e1071, glmnet, kknn, lgr,

Imports: data.table, mlr3 (>= 0.1.4), mlr3misc (>= 0.1.7), paradox, R6

Imports: data.table, mlr3 (>= 0.2.0), mlr3misc (>= 0.2.0), paradox, R6
Suggests: bibtex, checkmate, DiceKriging, e1071, glmnet, kknn, knitr,
lgr, MASS, nnet, ranger, rmarkdown, testthat, xgboost

Description: Recommended Learners for 'mlr3'. Extends 'mlr3' and 'mlr3proba'
Imports: data.table, mlr3 (>= 0.5.0), mlr3misc (>= 0.5.0), paradox, R6
Suggests: bibtex, checkmate, DiceKriging, distr6, e1071, glmnet, kknn,
knitr, lgr, MASS, mlr3proba (>= 0.2.1), nnet, pracma, ranger,

Imports: data.table, mlr3 (>= 0.6.0), mlr3misc (>= 0.5.0), paradox, R6
knitr, lgr, MASS, mlr3proba (>= 0.2.2), nnet, pracma, ranger,

Suggests: checkmate, DiceKriging, distr6, e1071, glmnet, kknn, knitr,
lgr, MASS, mlr3proba (>= 0.2.2), nnet, pracma, ranger, rgenoud,

lgr, MASS, mlr3proba (>= 0.2.5), nnet, pracma, ranger, rgenoud,

Imports: data.table, mlr3misc (>= 0.5.0), paradox, R6
lgr, MASS, mlr3proba (>= 0.3.2), nnet, pracma, ranger, rgenoud,

Imports: checkmate, data.table, mlr3misc (>= 0.5.0), paradox, R6
Suggests: DiceKriging, distr6, e1071, glmnet, kknn, knitr, lgr, MASS,
mlr3proba (>= 0.2.5), nnet, pracma, ranger, rgenoud, rmarkdown,