Package: mipred
Type: Package
Title: Prediction using Multiple Imputation
Version: 0.0.1
Authors@R: person("Bart J. A.", "Mertens", email = "b.mertens@lumc.nl",
                  role = c("aut", "cre"), comment = c(ORCID = "0000-0002-5019-0354"))
Maintainer: Bart J. A. Mertens <b.mertens@lumc.nl>
Description: Calibration of generalized linear models and Cox regression models for prediction using 
    multiple imputation to account for missing values in the predictors as described in the paper by 
    "Mertens, Banzato and de Wreede" (2018) <arXiv:1810.05099>. The methodology and calculations 
    described in this paper are fully implemented in this package. The vignette describes all data analytic steps 
    which allow users to replicate results using the package functions on the data analyzed in the paper or 
    on their own data. 
    Imputations are generated using the package 'mice' without using the outcomes of observations for which the
    predictions are generated. Two options are provided to generate predictions. The first is prediction-averaging of 
    predictions calibrated from single models fitted on single imputed 
    datasets within a set of multiple imputations. The second is application of the Rubin's rules pooled model.
    For both implementations, unobserved values in the predictor data of new observations for 
    which the predictions are derived are automatically imputed. The package contains two basic functions.  
    The first, mipred() generates predictions of outcome on new observations. The second, mipred.cv() generates 
    cross-validated predictions with the methodology on existing data for which outcomes
    have already been observed. The present version is still in development and should support continuous, 
    binary and counting outcomes, but we have only thoroughly checked performance for binary outcome 
    logistic regression modeling. We will include the Cox regression extension later.
URL: https://github.com/BartJAMertens/mipred,
        https://arxiv.org/abs/1810.05099,
        https://www.researchgate.net/project/Prediction-calibration-using-multiple-imputations-to-account-for-missing-predictor-values
BugReports: https://github.com/BartJAMertens/mipred/issues
Depends: R (>= 3.5.0)
License: GPL-3
Imports: mice (>= 3.4.0)
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Suggests: testthat, knitr, rmarkdown, pROC
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-07-05 12:37:55 UTC; bartm
Author: Bart J. A. Mertens [aut, cre] (<https://orcid.org/0000-0002-5019-0354>)
Repository: CRAN
Date/Publication: 2019-07-12 15:50:03 UTC
