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Nov 17, 2022
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1.0.3 #845

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version bumps
topepo Nov 10, 2022
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use \doi{}
topepo Nov 10, 2022
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6 changes: 3 additions & 3 deletions DESCRIPTION
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@@ -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"),
Expand All @@ -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/
Expand Down Expand Up @@ -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
3 changes: 2 additions & 1 deletion NEWS.md
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# 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

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3 changes: 1 addition & 2 deletions man/details_rand_forest_aorsf.Rd
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2 changes: 1 addition & 1 deletion man/rmd/rand_forest_aorsf.Rmd
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Expand Up @@ -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
2 changes: 1 addition & 1 deletion man/rmd/rand_forest_aorsf.md
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Expand Up @@ -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