Package: aorsf
Title: Accelerated Oblique Random Survival Forests
Version: 0.0.7
Authors@R: c(
    person(given = "Byron",
           family = "Jaeger",
           role = c("aut", "cre"),
           email = "bjaeger@wakehealth.edu",
           comment = c(ORCID = "0000-0001-7399-2299")),
    person(given = "Nicholas",  
           family = "Pajewski", 
           role = "ctb"),
    person(given = "Sawyer",
           family = "Welden", 
           role = "ctb", 
           email = "swelden@wakehealth.edu"),
    person(given = "Christopher", 
           family = "Jackson",
           email = "chris.jackson@mrc-bsu.cam.ac.uk",
           role = "rev"),
    person(given = "Marvin", 
           family = "Wright",
           role = "rev"),
    person(given = "Lukas",
           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., (2022) <arXiv:2208.01129>.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.2
LinkingTo: Rcpp, RcppArmadillo
Imports: Rcpp, data.table, utils, stats, collapse
URL: https://github.com/ropensci/aorsf,
        https://docs.ropensci.org/aorsf/
BugReports: https://github.com/ropensci/aorsf/issues/
Depends: R (>= 3.6)
Suggests: survival, SurvMetrics, ggplot2, testthat (>= 3.0.0), knitr,
        rmarkdown, glmnet, covr, units, tibble
Config/testthat/edition: 3
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2023-01-11 17:14:44 UTC; bjaeger
Author: Byron Jaeger [aut, cre] (<https://orcid.org/0000-0001-7399-2299>),
  Nicholas Pajewski [ctb],
  Sawyer Welden [ctb],
  Christopher Jackson [rev],
  Marvin Wright [rev],
  Lukas Burk [rev]
Maintainer: Byron Jaeger <bjaeger@wakehealth.edu>
Repository: CRAN
Date/Publication: 2023-01-12 11:50:02 UTC
Built: R 4.1.3; x86_64-w64-mingw32; 2023-04-17 14:35:38 UTC; windows
Archs: i386, x64
