corybrunson · corybrunson · Jul 17, 2026 · Jul 18, 2026 · Jul 18, 2026 · Jul 18, 2026
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: ordered
 Title: 'parsnip' Engines and Wrappers for Ordinal Classification Models
-Version: 0.1.0.9002
+Version: 0.1.0.9003
 Authors@R: c(
     person("Max", "Kuhn", , "max@posit.co",
            role = "aut",
@@ -22,13 +22,15 @@ Description: Bindings, methods, and tuners for using ordinal classification
     of Hornung (2020) <doi:10.1007/s00357-018-9302-x>
     in 'ordinalForest'.
 License: MIT + file LICENSE
-Depends: 
-    parsnip (> 1.6.0)
+Depends:
+    R (>= 4.1),
+    parsnip (>= 1.6.1)
 Imports: 
     cli,
-    dials (>= 1.4.3),
+    dials (> 1.4.4),
     purrr,
     rlang (>= 1.1.4),
+    scales (>= 1.3.0),
     tibble,
     tidyr
 Suggests: 
@@ -38,20 +40,20 @@ Suggests:
     ordinalNet,
     rms (>= 7.0.0),
     glmnetcr,
-    VGAM,
+    VGAM (>= 1.1-9),
     rpartScore,
     ordinalForest,
     orf,
     QSARdata,
     spelling,
     testthat (>= 3.0.0)
 Remotes:
-    tidymodels/parsnip
+    corybrunson/dials@parallel-reg
 Config/testthat/edition: 3
 Encoding: UTF-8
 Language: en-US
 Roxygen: list(markdown = TRUE)
 URL: https://corybrunson.github.io/ordered/
 BugReports: https://github.com/corybrunson/ordered/issues
 Config/Needs/website: rmarkdown
-Config/roxygen2/version: 8.0.0
+Config/roxygen2/version: 8.1.0
diff --git a/NAMESPACE b/NAMESPACE
@@ -11,8 +11,10 @@ S3method(predict_classprob,"_ordinalNet")
 S3method(predict_raw,"_ordinalNet")
 export(VGAM_vgam_wrapper)
 export(VGAM_vglm_wrapper)
+export(clm_wrapper)
 export(honesty)
 export(honesty_fraction)
+export(multi_predict_glmnetcr_wrapper)
 export(naive_scores)
 export(num_score_perms)
 export(num_score_trees)
@@ -22,21 +24,28 @@ export(ord_metric)
 export(ordinalForest_wrapper)
 export(ordinalNet_wrapper)
 export(orf_wrapper)
+export(parallel_penalty_factor)
 export(predict_glmnetcr_wrapper)
 export(predict_lrm_wrapper)
 export(predict_ordinalNet_wrapper)
 export(prune_func)
 export(rpartScore_wrapper)
 export(sample_fraction)
 export(split_func)
-export(threshold_structure)
 export(values_ord_metric)
 export(values_ordinal_link_VGAM)
 export(values_ordinal_link_clm)
+export(values_threshold_structure_VGAM)
 import(parsnip)
 import(rlang)
-importFrom(parsnip,eval_args)
-importFrom(parsnip,multi_predict)
-importFrom(parsnip,predict_raw)
-importFrom(stats,approx)
-importFrom(stats,predict)
+importFrom(parsnip,
+  eval_args,
+  multi_predict,
+  predict_raw
+)
+importFrom(stats,
+  approx,
+  as.formula,
+  coef,
+  predict
+)
diff --git a/NEWS.md b/NEWS.md
@@ -1,12 +1,31 @@
 # next version
 
-## additional engines
+## maintenance
 
-This version introduces source code and unit tests for new engines:
+### bug fix
+
+A bug in the prediction of `vglm` models, and associated tests, were patched.
+Previously, `predict()` was used, which triggers S3 dispatch when **VGAM** is not attached but S4 dispatched when it is.
+Now `predictvglm()` is used instead.
+
+### refactor
+
+`ordinal_reg()` argument value translation has been moved from the `translate()` method in parsnip to engine wrappers in ordered, with the exception of penalty path assembly for `ordinalNet` and `glmnetcr`, in coordination with [parsnip PR #1393](https://github.com/tidymodels/parsnip/pull/1393).
+
+### penalty paths (breaking change)
+
+Penalty path arguments in `ordinalNet` are no longer silently internally modified; for example, the user must specify `includeLambda0 = TRUE` if they want the path to include zero.
+Extrapolative predictions, which err in `ordinalNet`, are overridden to use path endpoint values for consistency with `glmnet` and `glmnetcr`.
+
+## new features
+
+### additional ordinal regression and random forest engines
+
+This version introduces source code and unit tests for new engines and dials:
 * `clm` from the **ordinal** package
   - cumulative link ordinal regression
+  - fit wrapper
   - additional `ordinal_link` dial values
-  - dial for `threshold` argument
 * `lrm` and `orm` from the **rms** package
   - regularized cumulative probability ordinal regression
   - shared prediction wrapper
@@ -18,19 +37,31 @@ This version introduces source code and unit tests for new engines:
   - conditional probability ordered random forests
   - fit wrapper
   - dials for `sample.fraction`, `honesty`, and `honesty.fraction` arguments
+* `ordinalNet` from the **ordinalNet** package (pre-existing engine)
+  - dial for the `parallelPenaltyFactor` argument
 
-Engine additions were coordinated with [parsnip PR #1384](https://github.com/tidymodels/parsnip/pull/1384).
+Coordinated with [parsnip PR #1384](https://github.com/tidymodels/parsnip/pull/1384).
 
-## linear predictions
+### linear prediction type
 
 Linear predictions are enabled for the `clm`, `lrm`, `orm`, `vglm`, and `ordinalNet` ordinal regression engines and for the `vgam` generalized additive model engine.
 They consistently return a single column of linear predictors (without threshold contributions).
 
-## bug fix
+Coordinated with [parsnip PR #1391](https://github.com/tidymodels/parsnip/pull/1391).
 
-A bug in the prediction of `vglm` models, and associated tests, were patched.
-Previously, `predict()` was used, which triggers S3 dispatch when **VGAM** is not attached but S4 dispatched when it is.
-Now `predictvglm()` is used instead.
+### threshold structure and parallel regression model arguments
+
+The `threshold_structure` model argument for `ordinal_reg()` controls what constraints, if any, are imposed on the ordered thresholds.
+It can be used by the `clm` and `vglm` engines, including with VGAM version 1.1-9.
+
+The `parallel_reg` model argument for `ordinal_reg()` controls the parallel regression assumption with a logical value applied to all predictors.
+It can be used by the `clm`, `vglm`, and `ordinalNet` engines.
+Note that the default is to defer to the engine, and the `vglm` engine defaults to non-parallel terms.
+
+The `gen_additive_mod()` `vgam` engine additionally registers the `Thresh` and `parallel` engine arguments.
+These may be tuned using the `threshold_structure` and `parallel_reg` dials.
+
+Coordinated with [parsnip PR #1393](https://github.com/tidymodels/parsnip/pull/1393) and [dials PR #462](https://github.com/tidymodels/dials/pull/462).
 
 # ordered 0.1.0
 

diff --git a/R/decision_tree-rpartScore.R b/R/decision_tree-rpartScore.R
@@ -1,4 +1,4 @@
-#' A wrapper for `rpartScore`
+#' Fit wrapper for `rpartScore`
 #'
 #' A wrapper is used because the model interface requires the response variable
 #' to be numeric rather than ordered or factor; the wrapper edits the input

diff --git a/R/gen_additive_mod-data.R b/R/gen_additive_mod-data.R
@@ -44,6 +44,22 @@ make_gen_additive_mod_vgam <- function() {
     func = list(pkg = "dials", fun = "odds_link"),
     has_submodel = FALSE
   )
+  parsnip::set_model_arg(
+    model = "gen_additive_mod",
+    eng = "vgam",
+    parsnip = "Thresh",
+    original = "Thresh",
+    func = list(pkg = "dials", fun = "threshold_structure"),
+    has_submodel = FALSE
+  )
+  parsnip::set_model_arg(
+    model = "gen_additive_mod",
+    eng = "vgam",
+    parsnip = "parallel",
+    original = "parallel",
+    func = list(pkg = "dials", fun = "parallel_reg"),
+    has_submodel = FALSE
+  )
 
   parsnip::set_fit(
     model = "gen_additive_mod",
@@ -53,9 +69,7 @@ make_gen_additive_mod_vgam <- function() {
       interface = "formula",
       protect = c("formula", "data", "weights"),
       func = c(pkg = "ordered", fun = "VGAM_vgam_wrapper"),
-      defaults = list(
-        parallel = TRUE
-      )
+      defaults = list()
     )
   )
 

diff --git a/R/ordered-package.R b/R/ordered-package.R
@@ -34,7 +34,7 @@
 #'
 #' fit_orf <- rand_forest(mode = "classification") %>%
 #'   set_engine("ordinalForest") %>%
-#'   set_args(nsets = 50, ntreefinal = 100, perffunction = "probability") %>%
+#'   set_args(nsets = 50, trees = 100, perffunction = "probability") %>%
 #'   fit(Sat ~ Infl + Type + Cont, data = house_train)
 #' predict(fit_orf, house_test, type = "prob")
 #'