TRUE for hidden Markov models, FALSE otherwise.

Number of states, the same as qmodel$nstates.

TRUE if the parameter values in pars are the maximum likelihood estimates, FALSE if they are the initial values.

The outcome distribution for each hidden state. A vector of length nstates whose rth entry is the index of the state r outcome distributions in the vector of supported distributions. The vector of supported distributions is given in full by msm:::.msm.HMODELS: the first few are 1 for categorical outcome, 2 for identity, 3 for uniform and 4 for normal.

String identifying each distribution in models.

Vector of length nstates giving the number of parameters in each outcome distribution, excluding covariate effects.

Number of initial state occupancy probabilities being estimated. This is zero if est.initprobs=FALSE, otherwise equal to the number of states.

Total number of parameters, equal to sum(npars).

A vector of length totpars, made from concatenating a list of length nstates whose rth component is vector of the parameters for the state r outcome distribution.

List with the names of the parameters in pars.

A vector of length totpars, whose ith element is the state corresponding to the ith parameter.

A vector of length nstates giving the index in pars of the first parameter for each state.

Index in pars of parameters which can have covariates on them.

Initial state occupancy probabilities, as supplied to msm (initial values before estimation, if est.initprobs=TRUE.)

Are initial state occupancy probabilities estimated (TRUE or FALSE), as supplied in the est.initprobs argument of msm.

Number of covariate effects per parameter in pars, with, e.g. factor contrasts expanded.

Vector of covariate effects, of length sum(ncovs).

Labels of these effects.

Vector indicating state corresponding to each element of coveffect.

Number of covariate effects on HMM outcomes, equal to sum(ncovs).

Vector of length nstates-1 giving the number of covariate effects on each initial state occupancy probability (log relative to the baseline probability).

Vector of length sum(nicovs) giving covariate effects on initial state occupancy probabilities.

Number of covariate effects on initial state occupancy probabilities, equal to sum(nicovs).

Constraints on (baseline) hidden Markov model outcome parameters, as supplied in the hconstraint argument of msm, excluding covariate effects, converted to a vector and mapped to the set 1,2,3,... if necessary.

Vector of constraints on covariate effects in hidden Markov outcome models, as supplied in the hconstraint argument of msm, excluding baseline parameters, converted to a vector and mapped to the set 1,2,3,... if necessary.

Matrix of range restrictions for HMM parameters, including those given to the hranges argument to msm.

TRUE if standard errors are available for the estimates.

Matrix of initial state occupancy probabilities with one row for each subject (estimated if est.initprobs=TRUE).

Confidence intervals for baseline HMM outcome parameters.

Confidence intervals for covariate effects in HMM outcome models.