Package: aorsf
Title: Accelerated Oblique Random Survival Forests
Version: 0.1.1
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., (2023) <DOI:10.1080/10618600.2023.2231048>.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE, roclets = c ("namespace", "rd", "srr::srr_stats_roclet"))
RoxygenNote: 7.2.3
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
