From 65cf27efc6b936f9a599c679fd833a142c6d6a4d Mon Sep 17 00:00:00 2001 From: bjaeger Date: Wed, 11 Oct 2023 13:43:29 -0400 Subject: [PATCH] fixing refs/dois for cran --- DESCRIPTION | 2 +- R/roxy.R | 4 ++-- inst/CITATION | 35 ++++++++++++++++++----------------- man/aorsf-package.Rd | 2 +- man/orsf.Rd | 2 +- man/orsf_control_custom.Rd | 7 +------ man/orsf_vi.Rd | 2 +- 7 files changed, 25 insertions(+), 29 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index a5a662a6..6186479b 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -25,7 +25,7 @@ Authors@R: c( family = "Burk", role = "rev") ) -Description: Fit, interpret, and make predictions with oblique random survival forests. Oblique decision trees are notoriously slow compared to their axis based counterparts, but 'aorsf' runs as fast or faster than axis-based decision tree algorithms for right-censored time-to-event outcomes. Methods to accelerate and interpret the oblique random survival forest are described in Jaeger et al., (2023) . +Description: Fit, interpret, and make predictions with oblique random survival forests. Oblique decision trees are notoriously slow compared to their axis based counterparts, but 'aorsf' runs as fast or faster than axis-based decision tree algorithms for right-censored time-to-event outcomes. Methods to accelerate and interpret the oblique random survival forest are described in Jaeger et al., (2023) . License: MIT + file LICENSE Encoding: UTF-8 LazyData: true diff --git a/R/roxy.R b/R/roxy.R index afaa26e2..40844894 100644 --- a/R/roxy.R +++ b/R/roxy.R @@ -199,8 +199,8 @@ roxy_cite_jaeger_2023 <- function(){ journal = "Journal of Computational and Graphical Statistics", date = "Published online 08 Aug 2023", number = NULL, - # doi = "10.1080/10618600.2023.2231048", - url = "https://doi.org/10.1080/10618600.2023.2231048" + doi = "10.1080/10618600.2023.2231048" + # url = "https://doi.org/10.1080/10618600.2023.2231048" ) } diff --git a/inst/CITATION b/inst/CITATION index dbdb9abc..ca5f2143 100644 --- a/inst/CITATION +++ b/inst/CITATION @@ -2,13 +2,13 @@ citHeader("To cite aorsf in publications use:") -citEntry( - entry = "Article", +bibentry( + bibtype = "Article", title = "aorsf: An R package for supervised learning using the oblique random survival forest", - author = personList(as.person("Byron C. Jaeger"), - as.person("Sawyer Welden"), - as.person("Kristin Lenoir"), - as.person("Nicholas M. Pajewski")), + author = c(as.person("Byron C. Jaeger"), + as.person("Sawyer Welden"), + as.person("Kristin Lenoir"), + as.person("Nicholas M. Pajewski")), journal = "Journal of Open Source Software", year = "2022", volume = "7", @@ -22,16 +22,16 @@ citEntry( ) ) -citEntry( - entry = "Article", +bibentry( + bibtype = "Article", title = "Accelerated and interpretable oblique random survival forests", - author = personList(as.person("Byron C. Jaeger"), - as.person("Sawyer Welden"), - as.person("Kristin Lenoir"), - as.person("Jaime L. Speiser"), - as.person("Matthew W. Segar"), - as.person("Ambarish Pandey"), - as.person("Nicholas M. Pajewski")), + author = c(as.person("Byron C. Jaeger"), + as.person("Sawyer Welden"), + as.person("Kristin Lenoir"), + as.person("Jaime L. Speiser"), + as.person("Matthew W. Segar"), + as.person("Ambarish Pandey"), + as.person("Nicholas M. Pajewski")), journal = "Journal of Computational and Graphical Statistics", year = "2023", url = "https://doi.org/10.1080/10618600.2023.2231048", @@ -42,9 +42,10 @@ citEntry( ) ) -citEntry(entry = "Article", +bibentry( + bibtype = "Article", title = "Oblique Random Survival Forests", - author = personList( + author = c( as.person("Byron C. Jaeger"), as.person("D. Leann Long"), as.person("Dustin M. Long"), diff --git a/man/aorsf-package.Rd b/man/aorsf-package.Rd index 606da2a8..0c0aa177 100644 --- a/man/aorsf-package.Rd +++ b/man/aorsf-package.Rd @@ -8,7 +8,7 @@ \description{ \if{html}{\figure{logo.png}{options: style='float: right' alt='logo' width='120'}} -Fit, interpret, and make predictions with oblique random survival forests. Oblique decision trees are notoriously slow compared to their axis based counterparts, but 'aorsf' runs as fast or faster than axis-based decision tree algorithms for right-censored time-to-event outcomes. Methods to accelerate and interpret the oblique random survival forest are described in Jaeger et al., (2023) \doi{ 10.1080/10618600.2023.2231048}. +Fit, interpret, and make predictions with oblique random survival forests. Oblique decision trees are notoriously slow compared to their axis based counterparts, but 'aorsf' runs as fast or faster than axis-based decision tree algorithms for right-censored time-to-event outcomes. Methods to accelerate and interpret the oblique random survival forest are described in Jaeger et al., (2023) \doi{10.1080/10618600.2023.2231048}. } \seealso{ Useful links: diff --git a/man/orsf.Rd b/man/orsf.Rd index 60d1de57..0991479e 100644 --- a/man/orsf.Rd +++ b/man/orsf.Rd @@ -919,5 +919,5 @@ Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests. \emph{ Jaeger BC, Long DL, Long DM, Sims M, Szychowski JM, Min YI, Mcclure LA, Howard G, Simon N. Oblique random survival forests. \emph{Annals of applied statistics} 2019 Sep; 13(3):1847-83. DOI: 10.1214/19-AOAS1261 -Jaeger BC, Welden S, Lenoir K, Speiser JL, Segar MW, Pandey A, Pajewski NM. Accelerated and interpretable oblique random survival forests. \emph{Journal of Computational and Graphical Statistics} Published online 08 Aug 2023. URL: https://doi.org/10.1080/10618600.2023.2231048 +Jaeger BC, Welden S, Lenoir K, Speiser JL, Segar MW, Pandey A, Pajewski NM. Accelerated and interpretable oblique random survival forests. \emph{Journal of Computational and Graphical Statistics} Published online 08 Aug 2023. DOI: 10.1080/10618600.2023.2231048 } diff --git a/man/orsf_control_custom.Rd b/man/orsf_control_custom.Rd index d2b4b1d7..4aa57cf8 100644 --- a/man/orsf_control_custom.Rd +++ b/man/orsf_control_custom.Rd @@ -108,12 +108,7 @@ How well do our two customized ORSFs do? Let’s compute their indices of prediction accuracy based on out-of-bag predictions: \if{html}{\out{
}}\preformatted{library(riskRegression) -}\if{html}{\out{
}} - -\if{html}{\out{
}}\preformatted{## riskRegression version 2023.03.22 -}\if{html}{\out{
}} - -\if{html}{\out{
}}\preformatted{library(survival) +library(survival) risk_preds <- list(rando = 1 - fit_rando$pred_oobag, pca = 1 - fit_pca$pred_oobag) diff --git a/man/orsf_vi.Rd b/man/orsf_vi.Rd index 1f952a6c..9d27d46c 100644 --- a/man/orsf_vi.Rd +++ b/man/orsf_vi.Rd @@ -286,5 +286,5 @@ Breiman L. Random forests. \emph{Machine learning} 2001 Oct; 45(1):5-32. DOI: 10 Menze BH, Kelm BM, Splitthoff DN, Koethe U, Hamprecht FA. On oblique random forests. \emph{Joint European Conference on Machine Learning and Knowledge Discovery in Databases} 2011 Sep 4; pp. 453-469. DOI: 10.1007/978-3-642-23783-6_29 -Jaeger BC, Welden S, Lenoir K, Speiser JL, Segar MW, Pandey A, Pajewski NM. Accelerated and interpretable oblique random survival forests. \emph{Journal of Computational and Graphical Statistics} Published online 08 Aug 2023. URL: https://doi.org/10.1080/10618600.2023.2231048 +Jaeger BC, Welden S, Lenoir K, Speiser JL, Segar MW, Pandey A, Pajewski NM. Accelerated and interpretable oblique random survival forests. \emph{Journal of Computational and Graphical Statistics} Published online 08 Aug 2023. DOI: 10.1080/10618600.2023.2231048 }