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.

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.
doPosteriorDraws() updates MCMC draws, so use this inside loop.
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.