DESCRIPTION

Package: scoringutils
Title: Utilities for Scoring and Assessing Predictions
Version: 0.1.7
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 = "Johannes Bracher",
           role = c("ctb"),
           email = "johannes.bracher@kit.edu",
           comment = c(ORCID = "0000-0002-3777-1410")), 
    person("Joel", "Hellewell",
           email = "joel.hellewell@lshtm.ac.uk", 
           role = c("ctb"),
           comment = c(ORCID = "0000-0003-2683-0849")),
    person(given = "Sophie Meakins",
           role = c("ctb"),
           email = "sophie.meakins@lshtm.ac.uk"),  
    person("James", "Munday", 
           email = "james.munday@lshtm.ac.uk", 
           role = c("ctb")),
    person("Katharine", "Sherratt", 
           email = "katharine.sherratt@lshtm.ac.uk", 
           role = c("ctb")),
    person("Sebastian", "Funk", 
            email = "sebastian.funk@lshtm.ac.uk", 
            role = c("aut")))
Description: 
    Combines a collection of metrics and proper scoring rules 
    (Tilmann Gneiting & Adrian E Raftery (2007) 
    <doi:10.1198/016214506000001437>) with an easy to
    use wrapper that can be used to automatically evaluate predictions. 
    Apart from 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 can be evaluated: 
    probabilistic forecasts (generally 
    predictive samples generated by Markov Chain Monte Carlo procedures), 
    quantile forecasts or point forecasts. Observed values and predictions
    can be either continuous, integer, or binary. Users can either choose 
    to apply these rules separately in a vector / matrix format that can 
    be flexibly used within other packages, or they can choose to do an 
    automatic evaluation of their forecasts. This is implemented with
    'data.table' and provides a consistent and very efficient framework for 
    evaluating various types of predictions. 
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: 
    data.table,
    forcats,
    ggplot2,
    goftest,
    graphics,
    scoringRules,
    stats,
    methods
Suggests: 
    testthat,
    knitr,
    rmarkdown
RoxygenNote: 7.1.1
URL: https://github.com/epiforecasts/scoringutils
BugReports: https://github.com/epiforecasts/scoringutils/issues
VignetteBuilder: knitr
Depends: 
    R (>= 2.10)