Nothing
Mstep.nn.MSAR <-
function(data,theta,FB,covar.trans=covar.trans,covar.emis=covar.emis,method=NULL) {
order=attributes(theta)$order
d=dim(data)[3]
if(is.na(d) | is.null(d)){d=1}
M=attributes(theta)$NbRegimes
if (length(covar.trans)==1) {
Lag = covar.trans
covar.trans = array(data[(1):(T-Lag+1),,],c(T-Lag+1,N.samples,d))
data = array(data[Lag:T,,],c(T-Lag+1,N.samples,d))
}
T = dim(data)[1]
N.samples = dim(data)[2]
ncov.trans = dim(covar.trans)[3]
# Lcov <- dim(covar.trans)[1]
# if(is.null(Lcov) || is.na(Lcov)){Lcov <- length(covar.trans)}
# lcov <- dim(covar)[2]
# if(is.null(lcov) || is.na(lcov)){lcov <- 1}
#covar <- matrix(covar,Lcov,lcov)
par.hn = Mstep.hn.MSAR(data,theta,FB,covar.emis)
theta$transmat[which(theta$transmat<1e-15)] = 1e-15
theta$transmat = mk_stochastic(theta$transmat)
trans = para_trans(theta$transmat)
par.trans = theta$par.trans
nh_transition = attributes(theta)$nh.transitions
par.init = plie2(trans,par.trans)
lxi = dim(FB$probSS)[3]
if (order>0) {deb = order+1}
else {deb = 1}
resopt = ucminf(par.init,fn=loglik_nh_inp,gr=NULL,covar=array(covar.trans[deb+(1:(lxi)),,],c(lxi,N.samples,ncov.trans)),xi=FB$probSS,nh_transition=nh_transition,hessian=0,control = list(trace=FALSE,xtol=1e-4))
res = deplie2(resopt$par,M);
trans = res$trans ;
par.trans = res$par ;
transmat=para_trans_inv(trans);
if (order>0) {
list(A=par.hn$A,sigma=par.hn$sigma,A0=par.hn$A0,prior=par.hn$prior,par.emis=par.hn$par_emis,transmat=transmat,par.trans=par.trans)
} else {
list(sigma=par.hn$sigma,A0=par.hn$A0,prior=par.hn$prior,par.emis=par.hn$par_emis,transmat=transmat,par.trans=par.trans)
}
}
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