##=============================================================================

##

## Copyright (c) 2018-2019 Marco Colombo and Paul McKeigue

##

## This program is free software: you can redistribute it and/or modify

## it under the terms of the GNU General Public License as published by

## the Free Software Foundation, either version 3 of the License, or

## (at your option) any later version.

##

## This program is distributed in the hope that it will be useful,

## but WITHOUT ANY WARRANTY; without even the implied warranty of

## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the

## GNU General Public License for more details.

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## You should have received a copy of the GNU General Public License

## along with this program.  If not, see <http://www.gnu.org/licenses/>.

##

##=============================================================================

#' Hierarchical shrinkage Stan models for biomarker selection

#'

#' The **hsstan** package provides linear and logistic regression models

#' penalized with hierarchical shrinkage priors for selection of biomarkers.

#' Models are fitted with Stan (Carpenter et al. (2017)), which allows to

#' perform full Bayesian inference.

#'

#' The package implements the horseshoe and regularized horseshoe priors

#' (Piironen and Vehtari (2017)), and the projection predictive selection

#' approach to recover a sparse set of predictive biomarkers (Piironen,

#' Paasiniemi and Vehtari (2020)).

#'

#' The approach is particularly suited to selection from high-dimensional

#' panels of biomarkers, such as those that can be measured by MSMS or similar

#' technologies (Colombo, Valo, McGurnaghan et al. (2019), Colombo, McGurnaghan,

#' Blackbourn et al. (2020)).

#'

#' @docType package

#' @name hsstan-package

#' @import Rcpp

#' @import methods

#' @useDynLib hsstan, .registration = TRUE

#'

#' @references

#' B. Carpenter et al. (2017),

#' Stan: a probabilistic programming language,

#' _Journal of Statistical Software_, 76 (1).

#' \doi{10.18637/jss.v076.i01}

#'

#' J. Piironen and A. Vehtari (2017),

#' Sparsity information and regularization in the horseshoe and other shrinkage

#' priors, _Electronic Journal of Statistics_, 11 (2), 5018-5051.

#' \doi{10.1214/17-EJS1337SI}

#'

#' J. Piironen, M. Paasiniemi and A. Vehtari (2020),

#' Projective inference in high-dimensional problems: prediction and feature

#' selection, _Electronic Journal of Statistics_, 14 (1), 2155-2197.

#' \doi{10.1214/20-EJS1711}

#'

#' M. Colombo, E. Valo, S.J. McGurnaghan et al. (2019),

#' Biomarkers associated with progression of renal disease in type 1 diabetes,

#' _Diabetologia_, 62 (9), 1616-1627.

#' \doi{10.1007/s00125-019-4915-0}

#'

#' M. Colombo, S.J. McGurnaghan, L.A.K. Blackbourn et al. (2020),

#' Comparison of serum and urinary biomarker panels with albumin creatinin

#' ratio in the prediction of renal function decline in type 1 diabetes,

#' _Diabetologia_, 63 (4), 788-798.

#' \doi{10.1007/s00125-019-05081-8}

#'

#' M. Colombo, A. Asadi Shehni, I. Thoma et al. (2021),

#' Quantitative levels of serum N-glycans in type 1 diabetes and their

#' association with kidney disease,

#' _Glycobiology_, 31 (5), 613-623.

#' \doi{10.1093/glycob/cwaa106}

NULL

.onLoad <- function(libname, pkgname) { # nocov start

  modules <- paste0("stan_fit4", names(stanmodels), "_mod")

  for (m in modules) loadModule(m, what = TRUE)

} # nocov end

.onAttach <- function(libname, pkgname) {

    ## number of cores used by default for sampling from the chains

    if (is.null(options()$mc.cores))

        options(mc.cores=min(ceiling(parallel::detectCores() / 2), 4)) # nocov

    packageStartupMessage("hsstan ", utils::packageVersion("hsstan"),

                          ": using ", options("mc.cores"),

                          " cores, set 'options(mc.cores)' to change.")

}