1.0.3 (#845)

* version bumps

* use \doi{}

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: parsnip
 Title: A Common API to Modeling and Analysis Functions
-Version: 1.0.2.9005
+Version: 1.0.3
 Authors@R: c(
     person("Max", "Kuhn", , "[email protected]", role = c("aut", "cre")),
     person("Davis", "Vaughan", , "[email protected]", role = "aut"),
@@ -11,7 +11,7 @@ Maintainer: Max Kuhn <[email protected]>
 Description: A common interface is provided to allow users to specify a
     model without having to remember the different argument names across
     different functions or computational engines (e.g. 'R', 'Spark',
-    'Stan', etc).
+    'Stan', 'H2O', etc).
 License: MIT + file LICENSE
 URL: https://github.com/tidymodels/parsnip,
     https://parsnip.tidymodels.org/
@@ -71,8 +71,8 @@ Config/Needs/website: C50, dbarts, earth, glmnet, keras, kernlab, kknn,
     tidymodels/tidymodels, tidyverse/tidytemplate, rstudio/reticulate,
     xgboost
 Config/rcmdcheck/ignore-inconsequential-notes: true
+Config/testthat/edition: 3
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)
-Config/testthat/edition: 3
 RoxygenNote: 7.2.1.9000

NEWS.md

@@ -1,11 +1,12 @@
-# parsnip (development version)
+# parsnip 1.0.3
 
 * Adds documentation and tuning infrastructure for the new `flexsurvspline` engine for the `survival_reg()` model specification from the `censored` package (@mattwarkentin, #831).
 
 * The matrix interface for fitting `fit_xy()` now works for the `"censored regression"` mode (#829).
 
 * The `num_leaves` argument of `boost_tree()`s `lightgbm` engine (via the bonsai package) is now tunable.
 
+* A change in our data checking code resulted in about a 3-fold speed-up in parsnip (#835)
 
 # parsnip 1.0.2
 

man/rmd/rand_forest_aorsf.Rmd

@@ -60,6 +60,6 @@ Predictions of survival probability at a time exceeding the maximum observed eve
 
 - Jaeger BC, Long DL, Long DM, Sims M, Szychowski JM, Min YI, Mcclure LA, Howard G, Simon N. Oblique random survival forests. Annals of applied statistics 2019 Sep; 13(3):1847-83. DOI: 10.1214/19-AOAS1261
 
-- Jaeger BC, Welden S, Lenoir K, Pajewski NM. aorsf: An R package for supervised learning using the oblique random survival forest. Journal of Open Source Software 2022, 7(77), 1 4705. https://doi.org/10.21105/joss.04705.
+- Jaeger BC, Welden S, Lenoir K, Pajewski NM. aorsf: An R package for supervised learning using the oblique random survival forest. Journal of Open Source Software 2022, 7(77), 1 4705. \doi{10.21105/joss.04705}.
 
 - Jaeger BC, Welden S, Lenoir K, Speiser JL, Segar MW, Pandey A, Pajewski NM. Accelerated and interpretable oblique random survival forests. arXiv e-prints 2022 Aug; arXiv-2208. URL: https://arxiv.org/abs/2208.01129

man/rmd/rand_forest_aorsf.md

@@ -63,6 +63,6 @@ Predictions of survival probability at a time exceeding the maximum observed eve
 
 - Jaeger BC, Long DL, Long DM, Sims M, Szychowski JM, Min YI, Mcclure LA, Howard G, Simon N. Oblique random survival forests. Annals of applied statistics 2019 Sep; 13(3):1847-83. DOI: 10.1214/19-AOAS1261
 
-- Jaeger BC, Welden S, Lenoir K, Pajewski NM. aorsf: An R package for supervised learning using the oblique random survival forest. Journal of Open Source Software 2022, 7(77), 1 4705. https://doi.org/10.21105/joss.04705.
+- Jaeger BC, Welden S, Lenoir K, Pajewski NM. aorsf: An R package for supervised learning using the oblique random survival forest. Journal of Open Source Software 2022, 7(77), 1 4705. \doi{10.21105/joss.04705}.
 
 - Jaeger BC, Welden S, Lenoir K, Speiser JL, Segar MW, Pandey A, Pajewski NM. Accelerated and interpretable oblique random survival forests. arXiv e-prints 2022 Aug; arXiv-2208. URL: https://arxiv.org/abs/2208.01129