Package: glmBfp
Imports: survival, rms, statmod, methods, coda, Runuran (>= 0.12), Rcpp
        (>= 0.11.6)
License: GPL (>= 2)
Title: Bayesian Fractional Polynomials for GLMs
LinkingTo: Rcpp, RcppArmadillo
LazyLoad: yes
Authors@R: c(person("Isaac","Gravestock", role=c("aut","cre"), email="isaac.gravestock@uzh.ch"),
            person("Daniel","Sabanes Bove", role="aut"),
            person("David","Maisonave", role="ctb"),
            person("Terry","Therneau", role="ctb"),
            person("R Core Team", role="ctb")
            )
Description: Implements the Bayesian paradigm
    for fractional polynomials in generalized linear
    models. See package 'bfp' for the treatment of normal
    models.
Version: 0.0-48
Date: 2016-07-06
Depends: R (>= 2.12.0)
Encoding: UTF-8
Suggests: MASS
Collate: 'GPrior-classes.R' 'posteriors.R' 'GlmBayesMfp-methods.R'
        'GlmBayesMfpSamples-class.R' 'GlmBayesMfpSamples-methods.R'
        'McmcOptions-class.R' 'McmcOptions-methods.R' 'RcppExports.R'
        'helpers.R' 'computeModels.R' 'constructNewdataMatrix.R'
        'getFpTransforms.R' 'getDesignMatrix.R' 'coxTBF.R'
        'evalZdensity.R' 'formula.R' 'fpScale.R' 'fpTrans.R'
        'getFamily.R' 'getLogGPrior.R' 'getLogMargLikEstimate.R'
        'getMarginalZ.R' 'sampleGlm.R' 'sampleBma.R' 'getModelCoefs.R'
        'glmBayesMfp.R' 'glmBfp-package.R' 'hpds.R' 'inclusionProbs.R'
        'optimize.R' 'plotCurveEstimate.R' 'predictCoxTBF.R'
        'testCox.R' 'uncenteredDesignMatrix.R' 'writeFormula.R'
RoxygenNote: 5.0.1
Author: Isaac Gravestock [aut, cre],
  Daniel Sabanes Bove [aut],
  David Maisonave [ctb],
  Terry Therneau [ctb],
  R Core Team [ctb]
Maintainer: Isaac Gravestock <isaac.gravestock@uzh.ch>
Repository: CRAN
Repository/R-Forge/Project: bfp
Repository/R-Forge/Revision: 112
Repository/R-Forge/DateTimeStamp: 2016-07-06 12:41:04
Date/Publication: 2016-07-07 13:37:21
NeedsCompilation: yes
Packaged: 2016-07-06 12:48:14 UTC; rforge
Built: R 3.2.5; x86_64-w64-mingw32; 2017-04-20 08:54:56 UTC; windows
Archs: i386, x64
