hmm.discnp-internal: Internal hmm.discnp functions.

In hmm.discnp: Hidden Markov Models with Discrete Non-Parametric Observation Distributions

Internal hmm.discnp functions.

Description

Internal hmm.discnp functions.

Usage

## S3 method for class 'multipleHmmDataSets'
x[i]
check.yval(yval, Rho, type, warn=TRUE)
checkConstColumns(y,prednames)
checkStartVal(par0,K,indep,yval,rand.start,mixture,prednames)
checkyXoK(y, X)
derivf(theta, K)
derivp(theta, K)
derivpi(ispd, tpm, npar, dp)
ell(phi, G)
expForm2p(x)
ffun(Dat, Rho, type)
forgethgl(fy, y, ymiss, tpm, ispd, d1pi, d2pi, npar, d1p,
          d2p, m, d1f, d2f)
orgethgl(fy, y, ymiss, tpm, xispd, d1pi, d2pi, npar, d1p,
          d2p, m, d1f, d2f)
rgethgl(fy, y, ymiss, tpm, xispd, d1pi, d2pi, npar, d1p,
          d2p, m, d1f, d2f)
get.gl(theta, K, y)
get.hgl(theta, K, y, hglmethod)
get.l(theta, K, y)
getIspd(pars, K)
getRho(pars, K, rhovals, stationary, prednames)
getTpm(pars, K, stationary)
hmmBD(y, par0, K, stationary,
      mixture, cis, tolerance, digits, verbose,
      itmax, crit, bicm)
hmmBI(y, par0, K, stationary,
      mixture, cis, tolerance, digits, verbose,
      itmax, crit, bicm)
hmmNumOpt(Dat, par0, stationary, verbose, itmax, bicm, rhovals, npar, optimiser,
          optimMethod, hessian=FALSE, ...)
hmmLM(y, par0, itmax=200, crit, lmc=10, tolerance,
      bicm, rhovals, hglmethod, digits=NULL, verbose=FALSE) 
hmmSD(y, par0, itmax=200, crit, tolerance,
      bicm, rhovals, hglmethod, digits=NULL, verbose=FALSE) 
hmmUV(y, par0, K, method,
      hglmethod, optimiser, optimMethod, stationary,
      mixture, cis, tolerance, digits, verbose,
      itmax, crit, bicm, X, addIntercept, lmc, hessian, ...)
init.all(K, rand.start, mixture, indep, yval, prednames)
lmstep(theta, K, y, lmc, hglmethod)
lse(z)
msRho(Rho0, G)
makeDat(y, X, addIntercept)
ordinal(k)
ordinalsuffix(k)
paramExtract(Rho)
p2expForm(x)
phi2rho(phi, ijk)
predictEngineHmmDiscnp(stateProbs,Rho,numb,drop)
recurse(fy, tpm, ispd, lns)
reparam(object, expForm=TRUE, stationary=NULL)
revise.ispd(tpm=NULL, gamma=NULL, lns=NULL, cis=TRUE)
revise.rho(Dat, gamma, type)
revise.tpm(xi, mixture)
rho2phi(Rho)
simference(object, expForm=TRUE, seed=NULL,nsim=100,verbose=TRUE)
steepest(K, y, theta)
tidyList(y, rp=c("response","predictor"),addIntercept=NULL, yval=NULL)

Details

These functions are auxiliary and are not intended to be called by the user.

hmm.discnp documentation built on Sept. 26, 2022, 5:05 p.m.