Package: scoringutils
Title: Utilities for Scoring and Assessing Predictions
Version: 1.2.2
Language: en-GB
Authors@R: c(
    person(given = "Nikos",
           family = "Bosse",
           role = c("aut", "cre"),
           email = "nikosbosse@gmail.com",
           comment = c(ORCID = "https://orcid.org/0000-0002-7750-5280")),
    person(given = "Sam Abbott",
           role = c("aut"),
           email = "contact@samabbott.co.uk",
           comment = c(ORCID = "0000-0001-8057-8037")), 
    person(given = "Hugo",
           family = "Gruson",
           role = c("aut"),
           email = "hugo.gruson@lshtm.ac.uk",
           comment = c(ORCID = "https://orcid.org/0000-0002-4094-1476")),
    person(given = "Johannes Bracher",
           role = c("ctb"),
           email = "johannes.bracher@kit.edu",
           comment = c(ORCID = "0000-0002-3777-1410")), 
    person("Sebastian", "Funk", 
            email = "sebastian.funk@lshtm.ac.uk", 
            role = c("aut")))
Description: 
    Provides a collection of metrics and proper scoring rules 
    (Tilmann Gneiting & Adrian E Raftery (2007) 
    <doi:10.1198/016214506000001437>, Jordan, A., Krüger, F., & Lerch, S. (2019)
    <doi:10.18637/jss.v090.i12>) within a consistent framework for 
    evaluation, comparison and visualisation of forecasts. 
    In addition to proper scoring rules, functions are provided to assess 
    bias, sharpness and calibration 
    (Sebastian Funk, Anton Camacho, Adam J. Kucharski, Rachel Lowe, Rosalind
    M. Eggo, W. John Edmunds (2019) <doi:10.1371/journal.pcbi.1006785>) of 
    forecasts. 
    Several types of predictions (e.g. binary, discrete, continuous) which may 
    come in different formats (e.g. forecasts represented by predictive samples 
    or by quantiles of the predictive distribution) can be evaluated. 
    Scoring metrics can be used either through a convenient data.frame format, 
    or can be applied as individual functions in a vector / matrix format. 
    All functionality has been implemented with a focus on performance and is 
    robustly tested. Find more information about the package in the 
    accompanying paper (<doi:10.48550/arXiv.2205.07090>).
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: data.table, ggdist (>= 3.2.0), ggplot2 (>= 3.4.0), lifecycle,
        methods, rlang, scoringRules, stats
Suggests: kableExtra, knitr, magrittr, rmarkdown, testthat, vdiffr
Config/Needs/website: r-lib/pkgdown, amirmasoudabdol/preferably
Config/testthat/edition: 3
RoxygenNote: 7.2.3
URL: https://doi.org/10.48550/arXiv.2205.07090,
        https://epiforecasts.io/scoringutils/,
        https://github.com/epiforecasts/scoringutils
BugReports: https://github.com/epiforecasts/scoringutils/issues
VignetteBuilder: knitr
Depends: R (>= 3.6)
NeedsCompilation: no
Packaged: 2023-11-29 14:54:39 UTC; nikos
Author: Nikos Bosse [aut, cre] (<https://orcid.org/0000-0002-7750-5280>),
  Sam Abbott [aut] (<https://orcid.org/0000-0001-8057-8037>),
  Hugo Gruson [aut] (<https://orcid.org/0000-0002-4094-1476>),
  Johannes Bracher [ctb] (<https://orcid.org/0000-0002-3777-1410>),
  Sebastian Funk [aut]
Maintainer: Nikos Bosse <nikosbosse@gmail.com>
Repository: CRAN
Date/Publication: 2023-11-29 15:50:10 UTC
Built: R 4.2.3; ; 2024-04-24 02:24:23 UTC; windows
