}}\preformatted{## # A tibble: 640 × 5
+\if{html}{\out{
}}\preformatted{## # A tibble: 640 x 5
## term step estimate lambda dev.ratio
##
## 1 (Intercept) 1 20.1 5.15 0
@@ -210,7 +210,7 @@ all_tidy_coefs
## 4 (Intercept) 4 24.7 3.89 0.347
## 5 (Intercept) 5 26.0 3.55 0.429
## 6 (Intercept) 6 27.2 3.23 0.497
-## # ℹ 634 more rows
+## # i 634 more rows
}\if{html}{\out{
}}
\if{html}{\out{
}}\preformatted{length(unique(all_tidy_coefs$lambda))
diff --git a/man/mlp.Rd b/man/mlp.Rd
index a1d38279b..842de7ab0 100644
--- a/man/mlp.Rd
+++ b/man/mlp.Rd
@@ -36,8 +36,8 @@ of model parameters randomly set to zero during model training.}
\item{activation}{A single character string denoting the type of relationship
between the original predictors and the hidden unit layer. The activation
function between the hidden and output layers is automatically set to either
-"linear" or "softmax" depending on the type of outcome. Possible values are:
-"linear", "softmax", "relu", and "elu"}
+"linear" or "softmax" depending on the type of outcome. Possible values
+depend on the engine being used.}
\item{learn_rate}{A number for the rate at which the boosting algorithm adapts
from iteration-to-iteration (specific engines only). This is sometimes referred to
diff --git a/man/other_predict.Rd b/man/other_predict.Rd
index 6000b884a..6c997e28d 100644
--- a/man/other_predict.Rd
+++ b/man/other_predict.Rd
@@ -8,6 +8,9 @@
\alias{predict_classprob.model_fit}
\alias{predict_hazard.model_fit}
\alias{predict_confint.model_fit}
+\alias{predict_confint}
+\alias{predict_predint}
+\alias{predict_predint.model_fit}
\alias{predict_linear_pred.model_fit}
\alias{predict_linear_pred}
\alias{predict_numeric.model_fit}
@@ -27,6 +30,14 @@
\method{predict_confint}{model_fit}(object, new_data, level = 0.95, std_error = FALSE, ...)
+predict_confint(object, ...)
+
+predict_predint(object, ...)
+
+\method{predict_predint}{model_fit}(object, new_data, level = 0.95, std_error = FALSE, ...)
+
+predict_predint(object, ...)
+
\method{predict_linear_pred}{model_fit}(object, new_data, ...)
predict_linear_pred(object, ...)
diff --git a/man/parsnip_update.Rd b/man/parsnip_update.Rd
index 7cbf4ef0d..51961a67f 100644
--- a/man/parsnip_update.Rd
+++ b/man/parsnip_update.Rd
@@ -423,8 +423,8 @@ of model parameters randomly set to zero during model training.}
\item{activation}{A single character string denoting the type of relationship
between the original predictors and the hidden unit layer. The activation
function between the hidden and output layers is automatically set to either
-"linear" or "softmax" depending on the type of outcome. Possible values are:
-"linear", "softmax", "relu", and "elu"}
+"linear" or "softmax" depending on the type of outcome. Possible values
+depend on the engine being used.}
\item{smoothness}{An non-negative number representing the the relative
smoothness of the class boundary. Smaller examples result in model flexible
diff --git a/man/rmd/aaa.Rmd b/man/rmd/aaa.Rmd
index c848e568f..3aa20ac19 100644
--- a/man/rmd/aaa.Rmd
+++ b/man/rmd/aaa.Rmd
@@ -3,6 +3,7 @@
options(useFancyQuotes = FALSE)
options(dplyr.print_min = 6, dplyr.print_max = 6)
options(cli.width = 85)
+options(cli.unicode = FALSE)
options(crayon.enabled = FALSE)
options(pillar.min_title_chars = Inf)
diff --git a/man/rmd/gen_additive_mod_mgcv.Rmd b/man/rmd/gen_additive_mod_mgcv.Rmd
index dddf85839..c1b3c6834 100644
--- a/man/rmd/gen_additive_mod_mgcv.Rmd
+++ b/man/rmd/gen_additive_mod_mgcv.Rmd
@@ -60,7 +60,7 @@ gen_additive_mod() %>%
The smoothness of the terms will need to be manually specified (e.g., using `s(x, df = 10)`) in the formula. Tuning can be accomplished using the `adjust_deg_free` parameter.
-When using a workflow, pass the _model formula_ to [add_model()]'s `formula` argument, and a simplified _preprocessing formula_ elsewhere.
+When using a workflow, pass the _model formula_ to [workflows::add_model()]'s `formula` argument, and a simplified _preprocessing formula_ elsewhere.
```{r}
spec <-
diff --git a/man/rmd/gen_additive_mod_mgcv.md b/man/rmd/gen_additive_mod_mgcv.md
index ffc84d81a..69b00ca78 100644
--- a/man/rmd/gen_additive_mod_mgcv.md
+++ b/man/rmd/gen_additive_mod_mgcv.md
@@ -96,7 +96,7 @@ gen_additive_mod() %>%
The smoothness of the terms will need to be manually specified (e.g., using `s(x, df = 10)`) in the formula. Tuning can be accomplished using the `adjust_deg_free` parameter.
-When using a workflow, pass the _model formula_ to [add_model()]'s `formula` argument, and a simplified _preprocessing formula_ elsewhere.
+When using a workflow, pass the _model formula_ to [workflows::add_model()]'s `formula` argument, and a simplified _preprocessing formula_ elsewhere.
```r
diff --git a/man/rmd/glmnet-details.md b/man/rmd/glmnet-details.md
index ed6c119d2..3c76f8f11 100644
--- a/man/rmd/glmnet-details.md
+++ b/man/rmd/glmnet-details.md
@@ -34,7 +34,7 @@ predict(fit, mtcars[1:3,])
```
```
-## # A tibble: 3 × 1
+## # A tibble: 3 x 1
## .pred
##
## 1 22.2
@@ -51,12 +51,12 @@ multi_predict(fit, mtcars[1:3,], penalty = c(0.00, 0.01))
```
```
-## # A tibble: 3 × 1
+## # A tibble: 3 x 1
## .pred
##
-## 1
-## 2
-## 3
+## 1
+## 2
+## 3
```
```r
@@ -67,7 +67,7 @@ multi_predict(fit, mtcars[1:3,], penalty = c(0.00, 0.01)) %>%
```
```
-## # A tibble: 6 × 3
+## # A tibble: 6 x 3
## penalty .pred .row
##
## 1 0 22.6 1
@@ -138,7 +138,7 @@ predict(fit_ridge, mtcars[1:3,])
```
```
-## # A tibble: 3 × 1
+## # A tibble: 3 x 1
## .pred
##
## 1 22.1
@@ -160,7 +160,7 @@ tidy(fit)
```
```
-## # A tibble: 11 × 3
+## # A tibble: 11 x 3
## term estimate penalty
##
## 1 (Intercept) 35.3 1
@@ -169,7 +169,7 @@ tidy(fit)
## 4 hp -0.0101 1
## 5 drat 0 1
## 6 wt -2.59 1
-## # ℹ 5 more rows
+## # i 5 more rows
```
Note that there is a `tidy()` method for `glmnet` objects in the `broom` package. If this is used directly on the underlying `glmnet` object, it returns _all of coefficients on the path_:
@@ -182,7 +182,7 @@ all_tidy_coefs
```
```
-## # A tibble: 640 × 5
+## # A tibble: 640 x 5
## term step estimate lambda dev.ratio
##
## 1 (Intercept) 1 20.1 5.15 0
@@ -191,7 +191,7 @@ all_tidy_coefs
## 4 (Intercept) 4 24.7 3.89 0.347
## 5 (Intercept) 5 26.0 3.55 0.429
## 6 (Intercept) 6 27.2 3.23 0.497
-## # ℹ 634 more rows
+## # i 634 more rows
```
```r
diff --git a/man/rmd/mlp_brulee.md b/man/rmd/mlp_brulee.md
index da6084e88..c637678f7 100644
--- a/man/rmd/mlp_brulee.md
+++ b/man/rmd/mlp_brulee.md
@@ -7,14 +7,12 @@ For this engine, there are multiple modes: classification and regression
-This model has 7 tuning parameters:
+This model has 6 tuning parameters:
- `hidden_units`: # Hidden Units (type: integer, default: 3L)
- `penalty`: Amount of Regularization (type: double, default: 0.0)
-- `mixture`: Proportion of Lasso Penalty (type: double, default: 0.0)
-
- `epochs`: # Epochs (type: integer, default: 100L)
- `dropout`: Dropout Rate (type: double, default: 0.0)
diff --git a/man/rmd/proportional_hazards_glmnet.md b/man/rmd/proportional_hazards_glmnet.md
index c294ca1ad..424e33406 100644
--- a/man/rmd/proportional_hazards_glmnet.md
+++ b/man/rmd/proportional_hazards_glmnet.md
@@ -88,7 +88,7 @@ predict(mod, pred_data, type = "survival", time = 500) %>%
```
```
-## # A tibble: 2 × 5
+## # A tibble: 2 x 5
## .eval_time .pred_survival age ecog.ps rx
##
## 1 500 0.666 50 1 1
diff --git a/man/rmd/surv_reg_flexsurv.Rmd b/man/rmd/surv_reg_flexsurv.Rmd
deleted file mode 100644
index 2d7202ce5..000000000
--- a/man/rmd/surv_reg_flexsurv.Rmd
+++ /dev/null
@@ -1,50 +0,0 @@
-```{r, child = "aaa.Rmd", include = FALSE}
-```
-
-`r descr_models("surv_reg", "survival")`
-
-## Tuning Parameters
-
-```{r flexsurv-param-info, echo = FALSE}
-defaults <-
- tibble::tibble(parsnip = c("dist"),
- default = c("'weibull'"))
-
-param <-
- surv_reg() %>%
- set_engine("flexsurv") %>%
- set_mode("regression") %>%
- make_parameter_list(defaults)
-```
-
-This model has `r nrow(param)` tuning parameters:
-
-```{r flexsurv-param-list, echo = FALSE, results = "asis"}
-param$item
-```
-
-
-## Translation from parsnip to the original package
-
-```{r flexsurv-reg, warning = FALSE}
-surv_reg(dist = character(1)) %>%
- set_engine("flexsurv") %>%
- set_mode("regression") %>%
- translate()
-```
-
-## Other details
-
-The main interface for this model uses the formula method since the model specification typically involved the use of [survival::Surv()].
-
-For this engine, stratification cannot be specified via [`strata()`], please see [flexsurv::flexsurvreg()] for alternative specifications.
-
-## Saving fitted model objects
-
-```{r child = "template-butcher.Rmd"}
-```
-
-
-## References
-
-- Jackson, C. 2016. `flexsurv`: A Platform for Parametric Survival Modeling in R. _Journal of Statistical Software_, 70(8), 1 - 33.
diff --git a/man/rmd/surv_reg_flexsurv.md b/man/rmd/surv_reg_flexsurv.md
deleted file mode 100644
index 9aaab07b8..000000000
--- a/man/rmd/surv_reg_flexsurv.md
+++ /dev/null
@@ -1,52 +0,0 @@
-
-
-
-For this engine, there is a single mode: regression
-
-## Tuning Parameters
-
-
-
-This model has 1 tuning parameters:
-
-- `dist`: Distribution (type: character, default: 'weibull')
-
-
-## Translation from parsnip to the original package
-
-
-```r
-surv_reg(dist = character(1)) %>%
- set_engine("flexsurv") %>%
- set_mode("regression") %>%
- translate()
-```
-
-```
-## Parametric Survival Regression Model Specification (regression)
-##
-## Main Arguments:
-## dist = character(1)
-##
-## Computational engine: flexsurv
-##
-## Model fit template:
-## flexsurv::flexsurvreg(formula = missing_arg(), data = missing_arg(),
-## weights = missing_arg(), dist = character(1))
-```
-
-## Other details
-
-The main interface for this model uses the formula method since the model specification typically involved the use of [survival::Surv()].
-
-For this engine, stratification cannot be specified via [`strata()`], please see [flexsurv::flexsurvreg()] for alternative specifications.
-
-## Saving fitted model objects
-
-
-This model object contains data that are not required to make predictions. When saving the model for the purpose of prediction, the size of the saved object might be substantially reduced by using functions from the [butcher](https://butcher.tidymodels.org) package.
-
-
-## References
-
-- Jackson, C. 2016. `flexsurv`: A Platform for Parametric Survival Modeling in R. _Journal of Statistical Software_, 70(8), 1 - 33.
diff --git a/man/rmd/surv_reg_survival.Rmd b/man/rmd/surv_reg_survival.Rmd
deleted file mode 100644
index fbdb24708..000000000
--- a/man/rmd/surv_reg_survival.Rmd
+++ /dev/null
@@ -1,55 +0,0 @@
-```{r, child = "aaa.Rmd", include = FALSE}
-```
-
-`r descr_models("surv_reg", "survival")`
-
-## Tuning Parameters
-
-```{r survival-param-info, echo = FALSE}
-defaults <-
- tibble::tibble(parsnip = c("dist"),
- default = c("'weibull'"))
-
-param <-
- surv_reg() %>%
- set_engine("survival") %>%
- set_mode("regression") %>%
- make_parameter_list(defaults)
-```
-
-This model has `r nrow(param)` tuning parameters:
-
-```{r survival-param-list, echo = FALSE, results = "asis"}
-param$item
-```
-
-## Translation from parsnip to the original package
-
-```{r survival-reg, warning = FALSE}
-surv_reg(dist = character(1)) %>%
- set_engine("survival") %>%
- set_mode("regression") %>%
- translate()
-```
-
-## Other details
-
-Note that `model = TRUE` is needed to produce quantile predictions when there is a stratification variable and can be overridden in other cases.
-
-The main interface for this model uses the formula method since the model specification typically involved the use of [survival::Surv()].
-
-The model formula can include _special_ terms, such as [survival::strata()]. The allows the model scale parameter to differ between groups contained in the function. The column used inside `strata()` is treated as qualitative no matter its type. To learn more about using special terms in formulas with tidymodels, see [`?model_formula`][parsnip::model_formula].
-
-For example, in this model, the numeric column `rx` is used to estimate two different scale parameters for each value of the column:
-
-```{r}
-library(survival)
-
-surv_reg() %>%
- fit(Surv(futime, fustat) ~ age + strata(rx), data = ovarian) %>%
- extract_fit_engine()
-```
-
-## References
-
-- Kalbfleisch, J. D. and Prentice, R. L. 2002 _The statistical analysis of failure time data_, Wiley.
diff --git a/man/rmd/surv_reg_survival.md b/man/rmd/surv_reg_survival.md
deleted file mode 100644
index 56e7e7765..000000000
--- a/man/rmd/surv_reg_survival.md
+++ /dev/null
@@ -1,76 +0,0 @@
-
-
-
-For this engine, there is a single mode: regression
-
-## Tuning Parameters
-
-
-
-This model has 1 tuning parameters:
-
-- `dist`: Distribution (type: character, default: 'weibull')
-
-## Translation from parsnip to the original package
-
-
-```r
-surv_reg(dist = character(1)) %>%
- set_engine("survival") %>%
- set_mode("regression") %>%
- translate()
-```
-
-```
-## Parametric Survival Regression Model Specification (regression)
-##
-## Main Arguments:
-## dist = character(1)
-##
-## Computational engine: survival
-##
-## Model fit template:
-## survival::survreg(formula = missing_arg(), data = missing_arg(),
-## weights = missing_arg(), dist = character(1), model = TRUE)
-```
-
-## Other details
-
-Note that `model = TRUE` is needed to produce quantile predictions when there is a stratification variable and can be overridden in other cases.
-
-The main interface for this model uses the formula method since the model specification typically involved the use of [survival::Surv()].
-
-The model formula can include _special_ terms, such as [survival::strata()]. The allows the model scale parameter to differ between groups contained in the function. The column used inside `strata()` is treated as qualitative no matter its type. To learn more about using special terms in formulas with tidymodels, see [`?model_formula`][parsnip::model_formula].
-
-For example, in this model, the numeric column `rx` is used to estimate two different scale parameters for each value of the column:
-
-
-```r
-library(survival)
-
-surv_reg() %>%
- fit(Surv(futime, fustat) ~ age + strata(rx), data = ovarian) %>%
- extract_fit_engine()
-```
-
-```
-## Call:
-## survival::survreg(formula = Surv(futime, fustat) ~ age + strata(rx),
-## data = data, model = TRUE)
-##
-## Coefficients:
-## (Intercept) age
-## 12.8734120 -0.1033569
-##
-## Scale:
-## rx=1 rx=2
-## 0.7695509 0.4703602
-##
-## Loglik(model)= -89.4 Loglik(intercept only)= -97.1
-## Chisq= 15.36 on 1 degrees of freedom, p= 8.88e-05
-## n= 26
-```
-
-## References
-
-- Kalbfleisch, J. D. and Prentice, R. L. 2002 _The statistical analysis of failure time data_, Wiley.
diff --git a/man/rpart_train.Rd b/man/rpart_train.Rd
index 227d55a36..5927233b9 100644
--- a/man/rpart_train.Rd
+++ b/man/rpart_train.Rd
@@ -46,7 +46,9 @@ those cases.}
A fitted rpart model.
}
\description{
-\code{rpart_train} is a wrapper for \code{rpart()} tree-based models
+\code{rpart_train()} is a wrapper for \code{rpart()} tree-based models
where all of the model arguments are in the main function.
+
+The function is now deprecated, as parsnip uses \code{rpart::rpart()} directly.
}
\keyword{internal}