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topepotopepo
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re-enable Sexpr in man files
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‎NEWS.md

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* The method that `parsnip` stores the model information has changed. Any custom models from previous versions will need to use the new method for registering models.
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* The mode need to be declared for models that can be used for more than one mode prior to fitting and/or translation).
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* For `surv_reg()`, the engine that uses the `survival` package is now called `survival` instead of `survreg`.
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## New Features
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‎R/boost_tree.R

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#'
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#' \pkg{xgboost} classification
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::boost_tree(mode = "classification"), "xgboost")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::boost_tree(mode = "classification"), "xgboost")}
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#'
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#' \pkg{xgboost} regression
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::boost_tree(mode = "regression"), "xgboost")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::boost_tree(mode = "regression"), "xgboost")}
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#'
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#' \pkg{C5.0} classification
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::boost_tree(mode = "classification"), "C5.0")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::boost_tree(mode = "classification"), "C5.0")}
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#'
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#' \pkg{spark} classification
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::boost_tree(mode = "classification"), "spark")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::boost_tree(mode = "classification"), "spark")}
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#'
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#' \pkg{spark} regression
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::boost_tree(mode = "regression"), "spark")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::boost_tree(mode = "regression"), "spark")}
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#'
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#' @note For models created using the spark engine, there are
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#' several differences to consider. First, only the formula

‎R/decision_tree.R

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#'
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#' \pkg{rpart} classification
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::decision_tree(mode = "classification"), "rpart")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::decision_tree(mode = "classification"), "rpart")}
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#'
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#' \pkg{rpart} regression
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::decision_tree(mode = "regression"), "rpart")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::decision_tree(mode = "regression"), "rpart")}
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#'
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#' \pkg{C5.0} classification
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::decision_tree(mode = "classification"), "C5.0")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::decision_tree(mode = "classification"), "C5.0")}
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#'
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#' \pkg{spark} classification
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::decision_tree(mode = "classification"), "spark")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::decision_tree(mode = "classification"), "spark")}
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#'
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#' \pkg{spark} regression
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::decision_tree(mode = "regression"), "spark")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::decision_tree(mode = "regression"), "spark")}
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#'
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#' @note For models created using the spark engine, there are
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#' several differences to consider. First, only the formula

‎R/linear_reg.R

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#'
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#' \pkg{lm}
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::linear_reg(), "lm")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::linear_reg(), "lm")}
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#'
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#' \pkg{glmnet}
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::linear_reg(), "glmnet")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::linear_reg(), "glmnet")}
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#'
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#' \pkg{stan}
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::linear_reg(), "stan")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::linear_reg(), "stan")}
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#'
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#' \pkg{spark}
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::linear_reg(), "spark")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::linear_reg(), "spark")}
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#'
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#' \pkg{keras}
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::linear_reg(), "keras")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::linear_reg(), "keras")}
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#'
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#' When using `glmnet` models, there is the option to pass
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#' multiple values (or no values) to the `penalty` argument. This

‎R/logistic_reg.R

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#'
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#' \pkg{glm}
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::logistic_reg(), "glm")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::logistic_reg(), "glm")}
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#'
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#' \pkg{glmnet}
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::logistic_reg(), "glmnet")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::logistic_reg(), "glmnet")}
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#'
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#' \pkg{stan}
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::logistic_reg(), "stan")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::logistic_reg(), "stan")}
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#'
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#' \pkg{spark}
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::logistic_reg(), "spark")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::logistic_reg(), "spark")}
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#'
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#' \pkg{keras}
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::logistic_reg(), "keras")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::logistic_reg(), "keras")}
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#'
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#' When using `glmnet` models, there is the option to pass
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#' multiple values (or no values) to the `penalty` argument. This

‎R/mars.R

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#'
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#' \pkg{earth} classification
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::mars(mode = "classification"), "earth")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::mars(mode = "classification"), "earth")}
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#'
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#' \pkg{earth} regression
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::mars(mode = "regression"), "earth")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::mars(mode = "regression"), "earth")}
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#'
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#' Note that, when the model is fit, the \pkg{earth} package only has its
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#' namespace loaded. However, if `multi_predict` is used, the package is

‎R/mlp.R

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#'
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#' \pkg{keras} classification
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::mlp(mode = "classification"), "keras")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::mlp(mode = "classification"), "keras")}
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#'
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#' \pkg{keras} regression
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::mlp(mode = "regression"), "keras")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::mlp(mode = "regression"), "keras")}
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#'
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#' \pkg{nnet} classification
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::mlp(mode = "classification"), "nnet")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::mlp(mode = "classification"), "nnet")}
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#'
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#' \pkg{nnet} regression
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::mlp(mode = "regression"), "nnet")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::mlp(mode = "regression"), "nnet")}
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#'
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#' @importFrom purrr map_lgl
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#' @seealso [varying()], [fit()]

‎R/multinom_reg.R

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#'
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#' \pkg{glmnet}
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::multinom_reg(), "glmnet")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::multinom_reg(), "glmnet")}
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#'
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#' \pkg{spark}
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::multinom_reg(), "spark")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::multinom_reg(), "spark")}
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#'
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#' \pkg{keras}
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::multinom_reg(), "keras")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::multinom_reg(), "keras")}
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#'
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#' When using `glmnet` models, there is the option to pass
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#' multiple values (or no values) to the `penalty` argument. This

‎R/nearest_neighbor.R

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#'
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#' \pkg{kknn} (classification or regression)
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::nearest_neighbor(), "kknn")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::nearest_neighbor(mode = "regression"), "kknn")}
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#'
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#' @note
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#' For `kknn`, the underlying modeling function used is a restricted

‎R/nullmodel.R

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#'
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#' \pkg{parsnip} classification
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::null_model(mode = "classification"), "parsnip")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::null_model(mode = "classification"), "parsnip")}
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#'
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#' \pkg{parsnip} regression
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::null_model(mode = "regression"), "parsnip")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::null_model(mode = "regression"), "parsnip")}
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#'
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#' @importFrom purrr map_lgl
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#' @seealso [varying()], [fit()]

‎R/rand_forest.R

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#'
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#' \pkg{ranger} classification
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::rand_forest(mode = "classification"), "ranger")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::rand_forest(mode = "classification"), "ranger")}
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#'
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#' \pkg{ranger} regression
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::rand_forest(mode = "regression"), "ranger")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::rand_forest(mode = "regression"), "ranger")}
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#'
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#' \pkg{randomForests} classification
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::rand_forest(mode = "classification"), "randomForest")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::rand_forest(mode = "classification"), "randomForest")}
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#'
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#' \pkg{randomForests} regression
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::rand_forest(mode = "regression"), "randomForest")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::rand_forest(mode = "regression"), "randomForest")}
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#'
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#' \pkg{spark} classification
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::rand_forest(mode = "classification"), "spark")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::rand_forest(mode = "classification"), "spark")}
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#'
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#' \pkg{spark} regression
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::rand_forest(mode = "regression"), "spark")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::rand_forest(mode = "regression"), "spark")}
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#'
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#' For \pkg{ranger} confidence intervals, the intervals are
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#' constructed using the form `estimate +/- z * std_error`. For

‎R/surv_reg.R

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#' The model can be created using the `fit()` function using the
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#' following _engines_:
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#' \itemize{
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#' \item \pkg{R}: `"flexsurv"`, `"survreg"` (the default)
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#' \item \pkg{R}: `"flexsurv"`, `"survival"` (the default)
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#' }
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#'
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#' @section Engine Details:
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#'
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#' \pkg{flexsurv}
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::surv_reg(), "flexsurv")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::surv_reg(), "flexsurv")}
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#'
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#' \pkg{survreg}
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#' \pkg{survival}
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::surv_reg(), "survreg")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::surv_reg(), "survival")}
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#'
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#' Note that `model = TRUE` is needed to produce quantile
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#' predictions when there is a stratification variable and can be
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#' @export
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translate.surv_reg <- function(x, engine = x$engine, ...) {
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if (is.null(engine)) {
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message("Used `engine = 'survreg'` for translation.")
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engine <- "survreg"
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message("Used `engine = 'survival'` for translation.")
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engine <- "survival"
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}
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x <- translate.default(x, engine, ...)
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x

‎R/svm_poly.R

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#'
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#' \pkg{kernlab} classification
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::svm_poly(mode = "classification"), "kernlab")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::svm_poly(mode = "classification"), "kernlab")}
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#'
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#' \pkg{kernlab} regression
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::svm_poly(mode = "regression"), "kernlab")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::svm_poly(mode = "regression"), "kernlab")}
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#'
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#' @importFrom purrr map_lgl
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#' @seealso [varying()], [fit()]

‎R/svm_rbf.R

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#'
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#' \pkg{kernlab} classification
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::svm_rbf(mode = "classification"), "kernlab")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::svm_rbf(mode = "classification"), "kernlab")}
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#'
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#' \pkg{kernlab} regression
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#'
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# \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::svm_rbf(mode = "regression"), "kernlab")}
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#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::svm_rbf(mode = "regression"), "kernlab")}
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#'
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#' @importFrom purrr map_lgl
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#' @seealso [varying()], [fit()]

‎man/boost_tree.Rd

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‎man/decision_tree.Rd

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‎man/linear_reg.Rd

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‎man/logistic_reg.Rd

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