Don't link to mixOmics (#751)

* Don't link to mixOmics

This causes a Note about mixOmics not being available for cross referencing when parsnip is checked as a revdep of any package that it depends on, like hardhat. That forces a manual inspection by CRAN.

* hardhat update

* re-doc

Co-authored-by: Max Kuhn <[email protected]>

Author: DavisVaughan

DESCRIPTION

@@ -25,7 +25,7 @@ Imports:
     ggplot2,
     globals,
     glue,
-    hardhat (>= 1.0.0),
+    hardhat (>= 1.1.0),
     lifecycle,
     magrittr,
     prettyunits,

man/rmd/C5_rules_C5.0.md

@@ -7,10 +7,12 @@ For this engine, there is a single mode: classification
 
 
 
-This model has 1 tuning parameters:
+This model has 2 tuning parameters:
 
 - `trees`: # Trees (type: integer, default: 1L)
 
+- `min_n`: Minimal Node Size (type: integer, default: 2L)
+
 Note that C5.0 has a tool for _early stopping_ during boosting where less iterations of boosting are performed than the number requested. `C5_rules()` turns this feature off (although it can be re-enabled using [C50::C5.0Control()]).
 
 ## Translation from parsnip to the underlying model call  (classification)

man/rmd/bag_tree_rpart.md

@@ -107,7 +107,8 @@ bag_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1
 ## 
 ## Model fit template:
 ## ipred::bagging(formula = missing_arg(), data = missing_arg(), 
-##     cp = double(1), maxdepth = integer(1), minsplit = integer(1))
+##     weights = missing_arg(), cp = double(1), maxdepth = integer(1), 
+##     minsplit = integer(1))
 ```
 
 

man/rmd/boost_tree_mboost.md

@@ -42,7 +42,7 @@ boost_tree() %>%
 ## 
 ## Model fit template:
 ## censored::blackboost_train(formula = missing_arg(), data = missing_arg(), 
-##     family = mboost::CoxPH())
+##     weights = missing_arg(), family = mboost::CoxPH())
 ```
 
 `censored::blackboost_train()` is a wrapper around [mboost::blackboost()] (and other functions) that makes it easier to run this model. 

man/rmd/decision_tree_partykit.md

@@ -104,7 +104,8 @@ decision_tree(tree_depth = integer(1), min_n = integer(1)) %>%
 ## 
 ## Model fit template:
 ## parsnip::ctree_train(formula = missing_arg(), data = missing_arg(), 
-##     maxdepth = integer(1), minsplit = min_rows(0L, data))
+##     weights = missing_arg(), maxdepth = integer(1), minsplit = min_rows(0L, 
+##         data))
 ```
 
 `censored::cond_inference_surv_ctree()` is a wrapper around [partykit::ctree()] (and other functions) that makes it easier to run this model. 

man/rmd/decision_tree_rpart.md

@@ -99,8 +99,8 @@ decision_tree(
 ## 
 ## Model fit template:
 ## pec::pecRpart(formula = missing_arg(), data = missing_arg(), 
-##     cp = double(1), maxdepth = integer(1), minsplit = min_rows(0L, 
-##         data))
+##     weights = missing_arg(), cp = double(1), maxdepth = integer(1), 
+##     minsplit = min_rows(0L, data))
 ```
 
 ## Preprocessing requirements

man/rmd/discrim_flexible_earth.md

@@ -44,8 +44,9 @@ discrim_flexible(
 ## Computational engine: earth 
 ## 
 ## Model fit template:
-## mda::fda(formula = missing_arg(), data = missing_arg(), nprune = integer(0), 
-##     degree = integer(0), pmethod = character(0), method = earth::earth)
+## mda::fda(formula = missing_arg(), data = missing_arg(), weights = missing_arg(), 
+##     nprune = integer(0), degree = integer(0), pmethod = character(0), 
+##     method = earth::earth)
 ```
 
 ## Preprocessing requirements

man/rmd/discrim_linear_mda.md

@@ -34,8 +34,8 @@ discrim_linear(penalty = numeric(0)) %>%
 ## Computational engine: mda 
 ## 
 ## Model fit template:
-## mda::fda(formula = missing_arg(), data = missing_arg(), lambda = numeric(0), 
-##     method = mda::gen.ridge, keep.fitted = FALSE)
+## mda::fda(formula = missing_arg(), data = missing_arg(), weights = missing_arg(), 
+##     lambda = numeric(0), method = mda::gen.ridge, keep.fitted = FALSE)
 ```
 
 ## Preprocessing requirements

man/rmd/gen_additive_mod_mgcv.md

@@ -26,7 +26,7 @@ gen_additive_mod(adjust_deg_free = numeric(1), select_features = logical(1)) %>%
 ```
 
 ```
-## GAM Specification (regression)
+## GAM Model Specification (regression)
 ## 
 ## Main Arguments:
 ##   select_features = logical(1)
@@ -50,7 +50,7 @@ gen_additive_mod(adjust_deg_free = numeric(1), select_features = logical(1)) %>%
 ```
 
 ```
-## GAM Specification (classification)
+## GAM Model Specification (classification)
 ## 
 ## Main Arguments:
 ##   select_features = logical(1)

man/rmd/mlp_brulee.md

@@ -53,7 +53,7 @@ mlp(
 ```
 
 ```
-## Single Layer Neural Network Specification (regression)
+## Single Layer Neural Network Model Specification (regression)
 ## 
 ## Main Arguments:
 ##   hidden_units = integer(1)
@@ -91,7 +91,7 @@ mlp(
 ```
 
 ```
-## Single Layer Neural Network Specification (classification)
+## Single Layer Neural Network Model Specification (classification)
 ## 
 ## Main Arguments:
 ##   hidden_units = integer(1)

man/rmd/mlp_keras.md

@@ -36,7 +36,7 @@ mlp(
 ```
 
 ```
-## Single Layer Neural Network Specification (regression)
+## Single Layer Neural Network Model Specification (regression)
 ## 
 ## Main Arguments:
 ##   hidden_units = integer(1)
@@ -70,7 +70,7 @@ mlp(
 ```
 
 ```
-## Single Layer Neural Network Specification (classification)
+## Single Layer Neural Network Model Specification (classification)
 ## 
 ## Main Arguments:
 ##   hidden_units = integer(1)

man/rmd/mlp_nnet.md

@@ -33,7 +33,7 @@ mlp(
 ```
 
 ```
-## Single Layer Neural Network Specification (regression)
+## Single Layer Neural Network Model Specification (regression)
 ## 
 ## Main Arguments:
 ##   hidden_units = integer(1)
@@ -64,7 +64,7 @@ mlp(
 ```
 
 ```
-## Single Layer Neural Network Specification (classification)
+## Single Layer Neural Network Model Specification (classification)
 ## 
 ## Main Arguments:
 ##   hidden_units = integer(1)

man/rmd/pls_mixOmics.Rmd

@@ -42,7 +42,7 @@ pls(num_comp = integer(1), predictor_prop = double(1)) %>%
  - Determines the number of predictors in the data.
  - Adjusts `num_comp` if the value is larger than the number of factors.
  - Determines whether sparsity is required based on the value of `predictor_prop`.
- - Sets the `keepX` argument of [mixOmics::spls()] for sparse models. 
+ - Sets the `keepX` argument of `mixOmics::spls()` for sparse models. 
 
 ## Translation from parsnip to the underlying model call  (classification)
 

man/rmd/pls_mixOmics.md

@@ -47,7 +47,7 @@ pls(num_comp = integer(1), predictor_prop = double(1)) %>%
  - Determines the number of predictors in the data.
  - Adjusts `num_comp` if the value is larger than the number of factors.
  - Determines whether sparsity is required based on the value of `predictor_prop`.
- - Sets the `keepX` argument of [mixOmics::spls()] for sparse models. 
+ - Sets the `keepX` argument of `mixOmics::spls()` for sparse models. 
 
 ## Translation from parsnip to the underlying model call  (classification)
 

man/rmd/proportional_hazards_glmnet.md

@@ -43,7 +43,7 @@ proportional_hazards(penalty = double(1), mixture = double(1)) %>%
 ## 
 ## Model fit template:
 ## censored::glmnet_fit_wrapper(formula = missing_arg(), data = missing_arg(), 
-##     alpha = double(1))
+##     weights = missing_arg(), alpha = double(1))
 ```
 
 ## Preprocessing requirements

man/rmd/proportional_hazards_survival.md

@@ -28,7 +28,7 @@ proportional_hazards() %>%
 ## 
 ## Model fit template:
 ## survival::coxph(formula = missing_arg(), data = missing_arg(), 
-##     x = TRUE, model = TRUE)
+##     weights = missing_arg(), x = TRUE, model = TRUE)
 ```
 
 ## Other details

man/rmd/rand_forest_partykit.md

@@ -85,7 +85,8 @@ rand_forest() %>%
 ## Computational engine: partykit 
 ## 
 ## Model fit template:
-## parsnip::cforest_train(formula = missing_arg(), data = missing_arg())
+## parsnip::cforest_train(formula = missing_arg(), data = missing_arg(), 
+##     weights = missing_arg())
 ```
 
 `censored::cond_inference_surv_cforest()` is a wrapper around [partykit::cforest()] (and other functions) that makes it easier to run this model. 

man/rmd/rule_fit_xrf.md

@@ -66,9 +66,10 @@ rule_fit(
 ## 
 ## Model fit template:
 ## rules::xrf_fit(formula = missing_arg(), data = missing_arg(), 
-##     colsample_bytree = numeric(1), nrounds = integer(1), min_child_weight = integer(1), 
-##     max_depth = integer(1), eta = numeric(1), gamma = numeric(1), 
-##     subsample = numeric(1), lambda = numeric(1))
+##     xgb_control = missing_arg(), colsample_bynode = numeric(1), 
+##     nrounds = integer(1), min_child_weight = integer(1), max_depth = integer(1), 
+##     eta = numeric(1), gamma = numeric(1), subsample = numeric(1), 
+##     lambda = numeric(1))
 ```
 
 ## Translation from parsnip to the underlying model call  (classification)
@@ -112,9 +113,10 @@ rule_fit(
 ## 
 ## Model fit template:
 ## rules::xrf_fit(formula = missing_arg(), data = missing_arg(), 
-##     colsample_bytree = numeric(1), nrounds = integer(1), min_child_weight = integer(1), 
-##     max_depth = integer(1), eta = numeric(1), gamma = numeric(1), 
-##     subsample = numeric(1), lambda = numeric(1))
+##     xgb_control = missing_arg(), colsample_bynode = numeric(1), 
+##     nrounds = integer(1), min_child_weight = integer(1), max_depth = integer(1), 
+##     eta = numeric(1), gamma = numeric(1), subsample = numeric(1), 
+##     lambda = numeric(1))
 ```
 
 ## Differences from the xrf package
@@ -134,6 +136,7 @@ These differences will create a disparity in the values of the `penalty` argumen
 
 ## Preprocessing requirements
 
+
 Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
 ## Other details

man/rmd/svm_linear_LiblineaR.md

@@ -29,7 +29,7 @@ svm_linear(
 ```
 
 ```
-## Linear Support Vector Machine Specification (regression)
+## Linear Support Vector Machine Model Specification (regression)
 ## 
 ## Main Arguments:
 ##   cost = double(1)
@@ -55,7 +55,7 @@ svm_linear(
 ```
 
 ```
-## Linear Support Vector Machine Specification (classification)
+## Linear Support Vector Machine Model Specification (classification)
 ## 
 ## Main Arguments:
 ##   cost = double(1)

man/rmd/svm_linear_kernlab.md

@@ -27,7 +27,7 @@ svm_linear(
 ```
 
 ```
-## Linear Support Vector Machine Specification (regression)
+## Linear Support Vector Machine Model Specification (regression)
 ## 
 ## Main Arguments:
 ##   cost = double(1)
@@ -53,7 +53,7 @@ svm_linear(
 ```
 
 ```
-## Linear Support Vector Machine Specification (classification)
+## Linear Support Vector Machine Model Specification (classification)
 ## 
 ## Main Arguments:
 ##   cost = double(1)

man/rmd/svm_poly_kernlab.md

@@ -33,7 +33,7 @@ svm_poly(
 ```
 
 ```
-## Polynomial Support Vector Machine Specification (regression)
+## Polynomial Support Vector Machine Model Specification (regression)
 ## 
 ## Main Arguments:
 ##   cost = double(1)
@@ -64,7 +64,7 @@ svm_poly(
 ```
 
 ```
-## Polynomial Support Vector Machine Specification (classification)
+## Polynomial Support Vector Machine Model Specification (classification)
 ## 
 ## Main Arguments:
 ##   cost = double(1)

man/rmd/svm_rbf_kernlab.md

@@ -32,7 +32,7 @@ svm_rbf(
 ```
 
 ```
-## Radial Basis Function Support Vector Machine Specification (regression)
+## Radial Basis Function Support Vector Machine Model Specification (regression)
 ## 
 ## Main Arguments:
 ##   cost = double(1)
@@ -60,7 +60,7 @@ svm_rbf(
 ```
 
 ```
-## Radial Basis Function Support Vector Machine Specification (classification)
+## Radial Basis Function Support Vector Machine Model Specification (classification)
 ## 
 ## Main Arguments:
 ##   cost = double(1)