OU.aux <- function(
    drift.expr = expression(kappa*(theta - X)),
    diffuse.expr = expression(sigma),
    rho.names = c("kappa", "theta", "sigma"),
    N = 1,
    M = 1,
    t.per.sim = 1,
    ndt = 100,
    z = NULL,
    seed = 0,
    X0 = rep(0.1, length = N),
    returnc = rep(T, length = N),
    lbound = -100,
    ubound = 100)
{
    # aux components:
    #   drift.expr    splus drift fn.
    #   diffuse.expr  splus diffusion fn.
    #   rho.names    character vector naming elements of rho vector
    #   N            dimension of the problem; size of X
    #   M            dimension of the Wiener process for the problem
    #   t.per.sim   is the time length of each simulation step
    #   ndt         is the number of discretization steps per simulation step
    #   z           random normals, optional, of length M*ndt*(n.burn+n.sim)
    #               (will otherwise be generated internally)
    #   seed        seed used if z is not given.
    #   X0          is the initial condition (of length N)
    #   returnc     logical vector of length N indicating which elements to return
    #   lbound,     simulation will be truncated to lie in [lbound, ubound]
    #   ubound
    aux <- list(drift.expr = drift.expr, diffuse.expr = diffuse.expr, rho.names = rho.names, N = N, M = M, t.per.sim = t.per.sim, ndt = 
        ndt, z = z, seed = seed, X0 = X0, returnc = returnc, lbound = lbound, ubound = ubound)
    aux$drift.pcode <- gensim.pcode(drift.expr, aux)
    aux$diffuse.pcode <- gensim.pcode(diffuse.expr, aux)
    aux
}


OU.gensim <- function(rho,n.sim,n.var=1,n.burn=0,aux=OU.aux())
{
    euler1d.pcode.gensim(rho=rho,
        n.sim = n.sim, n.burn = n.burn,
        aux = aux)
}




CIR.aux <- function(
    drift.expr = expression(kappa*(theta-X)),
    diffuse.expr = expression(sigma*sqrt(X)),
    rho.names = c("kappa", "theta", "sigma"),
    N = 1,
    M = 1,
    t.per.sim = 1,
    ndt = 100,
    z = NULL,
    seed = 0,
    X0 = rep(0.1, length = N),
    returnc = rep(T, length = N),
    lbound = 0,
    ubound = 100)
{
    # aux components:
    #   drift.expr    splus drift fn.
    #   diffuse.expr  splus diffusion fn.
    #   rho.names    character vector naming elements of rho vector
    #   N            dimension of the problem; size of X
    #   M            dimension of the Wiener process for the problem
    #   t.per.sim   is the time length of each simulation step
    #   ndt         is the number of discretization steps per simulation step
    #   z           random normals, optional, of length M*ndt*(n.burn+n.sim)
    #               (will otherwise be generated internally)
    #   seed        seed used if z is not given.
    #   X0          is the initial condition (of length N)
    #   returnc     logical vector of length N indicating which elements to return
    #   lbound,     simulation will be truncated to lie in [lbound, ubound]
    #   ubound
    aux <- list(drift.expr = drift.expr, diffuse.expr = diffuse.expr, rho.names = rho.names, N = N, M = M, t.per.sim = t.per.sim, ndt = 
        ndt, z = z, seed = seed, X0 = X0, returnc = returnc, lbound = lbound, ubound = ubound)
    aux$drift.pcode <- gensim.pcode(drift.expr, aux)
    aux$diffuse.pcode <- gensim.pcode(diffuse.expr, aux)
    aux
}


CIR.gensim <- function(rho,n.sim,n.var=1,n.burn=0,aux=CIR.aux())
{
    euler1d.pcode.gensim(rho=rho,
        n.sim = n.sim, n.burn = n.burn,
        aux = aux)
}