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topepotopepo
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disabled Rd Sexpr until new data structure ready
1 parent 5dc131d commit 7d9cca7

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‎NAMESPACE

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@@ -215,4 +215,3 @@ importFrom(utils,capture.output)
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importFrom(utils,getFromNamespace)
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importFrom(utils,globalVariables)
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importFrom(utils,head)
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importFrom(utils,stack)

‎R/aaa_models.R

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#' @export
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get_model_env <- function() {
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current <- utils::getFromNamespace("parsnip", ns = "parsnip")
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# current <- get("parsnip")
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# current <- parsnip
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current
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}
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@@ -581,4 +581,4 @@ show_model_info <- function(mod) {
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invisible(NULL)
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}
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# ------------------------------------------------------------------------------
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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(), "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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#'
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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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#'
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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(), "survreg")}
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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

‎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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