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R/arguments.R

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@@ -317,7 +317,7 @@ make_xy_call <- function(object, target, env, call = rlang::caller_env()) {
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#' @param offset A number subtracted off of the number of rows available in the
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#' data.
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#' @return An integer (and perhaps a warning).
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#' @examplesIf !parsnip:::is_cran_check()
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#' @examplesIf !parsnip:::is_cran_check() & rlang::is_installed("kknn") & rlang::is_installed("ranger")
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#' nearest_neighbor(neighbors= 100) |>
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#' set_engine("kknn") |>

R/augment.R

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#' @references
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#' \url{https://www.tidymodels.org/learn/statistics/survival-metrics/}
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#' @export
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#' @examplesIf !parsnip:::is_cran_check()
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#' @examplesIf !parsnip:::is_cran_check() & rlang::is_installed("modeldata")
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#' car_trn <- mtcars[11:32,]
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#' car_tst <- mtcars[ 1:10,]
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#'

R/fit.R

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#' `allow_sparse_x` column of [parsnip::get_encoding()] for sparse input
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#' compatibility.
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#'
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#' @examplesIf !parsnip:::is_cran_check()
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#' @examplesIf !parsnip:::is_cran_check() & rlang::is_installed("modeldata")
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#' # Although `glm()` only has a formula interface, different
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#' # methods for specifying the model can be used
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#'

R/misc.R

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@@ -586,7 +586,7 @@ is_cran_check <- function() {
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#' @param x A fitted parsnip model (class `"model_fit"`) or a fitted workflow.
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#' @param syms Should the column names be converted to symbols? Defaults to `FALSE`.
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#' @return A list with elements `"estimate"` and `"probabilities"`.
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#' @examplesIf !parsnip:::is_cran_check()
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#' @examplesIf !parsnip:::is_cran_check() & rlang::is_installed("modeldata")
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#' library(dplyr)
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#' library(modeldata)
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#' data("two_class_dat")

man/augment.Rd

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man/dot-get_prediction_column_names.Rd

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man/fit.Rd

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man/min_cols.Rd

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tests/testthat.R

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library(parsnip)
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test_check("parsnip")
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tests/testthat/_snaps/adds.md

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