DESCRIPTION

Package: stackr
Title: Create Mixture Models From Predictive Samples
Version: 0.1.0
Authors@R: 
    c(person(given = "Nikos",
             family = "Bosse",
             role = c("aut", "cre"),
             email = "nikosbosse@gmail.com",
             comment = c(ORCID = "0000-0002-7750-5280")),
      person(given = "Yuling",
             family = "Yao",
             role = c("aut"),
             email = "yy2619@columbia.edu"),
      person(given = "Sam", 
            family = "Abbott",
            role = c("aut"),
            email = "contact@samabbott.co.uk",
            comment = c(ORCID = "0000-0001-8057-8037")), 
      person(given = "Sebastian",
             family = "Funk",
             role = c("aut"),
             email = "sebastian.funk@lshtm.ac.uk",
             comment = c(ORCID = "0000-0002-2842-3406")))
Description: 
      The `stackr` package provides an easy way to combine predictions
      from individual time series or panel data models to an 
      ensemble. `stackr` stacks (Yuling Yao, Aki Vehtari, Daniel Simpson, 
      and Andrew Gelman (2018) <doi:10.1214/17-BA1091>)
      Models according to the Continuous Ranked 
      Probability Score (CRPS) (Tilmann Gneiting & Adrian E Raftery (2007) 
      <doi:10.1198/016214506000001437>) over k-step ahead predictions. It is therefore 
      especially suited for timeseries and panel data. A function for 
      leave-one-out CRPS may be added in the future. Predictions need to be 
      predictive distributions represented by predictive samples. Usually, these will 
      be sets of posterior predictive simulation draws generated by an MCMC 
      algorithm. 
      Given some training data with true observed values as well as predictive samples
      generated from different models, `crps_weights` finds the optimal (in the sense of 
      minimizing expected cross-validation predictive error) weights 
      to form an ensemble from these models. Using these weights, 
      `mixture_from_samples` can then provide samples from the optimal 
      model mixture by drawing from the predictice samples
      of the individual models in the correct proportion. This gives a mixture model
      solely based on predictive samples and is in this regard superior to other 
      ensembling techniques like Bayesian Model Averaging. 
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Biarch: true
Depends: 
    R (>= 3.5.0)
Imports: 
    data.table,
    methods,
    Rcpp (>= 0.12.0),
    RcppParallel (>= 5.0.1),
    rstan (>= 2.18.1),
    rstantools (>= 2.4.0)
LinkingTo: 
    BH (>= 1.66.0),
    Rcpp (>= 0.12.0),
    RcppEigen (>= 0.3.3.3.0),
    RcppParallel (>= 5.0.1),
    rstan (>= 2.18.1),
    StanHeaders (>= 2.18.0)
SystemRequirements: GNU make
RoxygenNote: 7.3.2
Suggests: 
    knitr,
    rmarkdown,
    testthat (>= 3.0.0),
    scoringutils
VignetteBuilder: knitr
Config/testthat/edition: 3