Cpp source usage

library(bvhar)

For Package Developers

This package provides public C++ headers. Some might be implement these useful.

• bvharsim.h: Rcpp random generation functions that are widely used in Bayesian statistics.
• ols.h: OLS classes and structs
• minnesota.h: Minnesota prior classes and structs
• mcmcsv.h: Classes and structs for stochastic volatility models. It includes
  • Minnesota prior
  • SSVS prior
  • Horseshoe prior
• mcmchs.h: Horseshoe prior classes
• mcmcssvs.h: SSVS prior classes
• olsforecaster.h: Forecasting class for each VAR and VHAR
• minnforecaster.h: Minnesota prior forecaster classes
• svforecaster.h: SV models forecaster classes
• olsspillover.h: FEVD and spillover class for each VAR and VHAR
• minnspillover.h: FEVD and spillover class for Minnesota prior
• svspillover.h: FEVD and spillover class for SV models
• bvharprogress.h: Simple progress bar classes
• bvharinterrupt.h: Interruption handler classes

You can use these by writing in your R package DESCRIPTION:

LinkingTo: 
    BH,
    Rcpp,
    RcppEigen,
    bvhar

Also, you can use in your single C++ source:

// [[Rcpp::depends(BH, RcppEigen, bvhar)]]
// [[Rcpp::plugins(bvhar)]]

You need to add plugins attribute because the header in this package should define USE_RCPP macro. Or you can use instead:

Sys.setenv("PKG_CPPFLAGS" = "-DUSE_RCPP")

without using plugins attribute.

MCMC headers

mcmc*.h has classes that can conduct MCMC. Since it is designed thread-safe, you can OpenMP for parallel multiple chain loop.

1. Initialize using smart pointer (in this package: std::unique_ptr)
   • Since each class requires other structure as its parameter, you first initialize it.
   • Each struct is inside the same header.
2. doPosteriorDraws() updates MCMC draws, so use this inside loop.
3. returnRecords(burn, thin) returns Rcpp::List of every MCMC record.

In case of SV model, you can define your own prior by defining a derived class.