R/old/msde.R

In mlysy/msde: Bayesian Inference for Multivariate Stochastic Differential Equations

#' Simulation and inference for multivariate stochastic differential equations.
#'
#' @docType package
#' @name msde
#' @useDynLib msde, .registration = TRUE
#' @useDynLib msde user_unif_rand
#' @useDynLib msde user_norm_rand
#' @details See package vignettes; `vignette("msde-quicktut")` for a tutorial and `vignette("msde-exmodels")` for several example models.
#' @examples
#' \donttest{
#' # Posterior inference for Heston's model
#' hex <- example.models("hest")
#' hmod <- sde.make.model(ModelFile = hex$ModelFile,
#'                        param.names = hex$param.names,
#'                        data.names = hex$data.names)
#'
#' # Simulate data
#' X0 <- c(X = log(1000), Z = 0.1)
#' theta <- c(alpha = 0.1, gamma = 1, beta = 0.8, sigma = 0.6, rho = -0.8)
#' dT <- 1/252
#' nobs <- 1000
#' hest.sim <- sde.sim(model = hmod, x0 = X0, theta = theta,
#'                     dt = dT, dt.sim = dT/10, nobs = nobs)
#'
#' # initialize MCMC sampler
#' # both components observed, no missing data between observations
#' init <- sde.init(model = hmod, x = hest.sim$data,
#'                  dt = hest.sim$dt, theta = theta)
#'
#' # Initialize posterior sampling argument
#' nsamples <- 1e4
#' burn <- 1e3
#' hyper <- NULL # flat prior
#' hest.post <- sde.post(model = hmod, init = init, hyper = hyper,
#'                       nsamples = nsamples, burn = burn)
#'
#' # plot the histogram for the sampled parameters
#' par(mfrow = c(2,3))
#' for(ii in 1:length(hmod$param.names)) {
#'   hist(hest.post$params[,ii],breaks=100, freq = FALSE,
#'        main = parse(text = hmod$param.names[ii]), xlab = "")
#' }
#' }
NULL