Nothing
gm.default <-
function (tm, te, method, gmguess = NULL, prior = NULL, burnin = NULL,
eps = 1e-06, conv_pvalue = 0.05, conv_freq = 10, niter = 10000,
sampl_func = NULL, combmat = NULL, sampl_method = "Unif",
logmethod = "Eigen", expmethod = "PadeRBS", verbose = FALSE,
...)
{
tm = as.matrix(tm)
te = as.numeric(te)
est = list()
if (method == "DA") {
est$par = gmDA(tmrel = tm, te, logmethod = logmethod)
est$method = "Diagonal Adjustment"
}
else if (method == "WA") {
est$par = gmWA(tmrel = tm, te, logmethod = logmethod)
est$method = "Weighted Adjustment"
}
else if (method == "QO") {
est$par = gmQO(tmrel = tm, te, logmethod = logmethod)
est$method = "Quasi Optimization"
}
else if (method == "EM") {
est = gmEM(tmabs = tm, te, gmguess, eps, niter, expmethod,
verbose)
est$method = "Expectation-Maximization Algorithm"
}
else if (method == "GS") {
est = gmGS(tmabs = tm, te, prior, burnin, conv_pvalue,
conv_freq, niter, sampl_method, expmethod, verbose,
combmat, sampl_func)
est$method = "Gibbs Sampler"
}
est$call = match.call()
est$tm = tm
est$te = te
class(est) = "gm"
est
}
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