CAT                     Pseudo activity counts (per minute) data for
                        cats
convolution             Convolution of two real vectors of the same
                        length.
dist_learn              Distributed learning for a longitudinal
                        continuous-time zero-inflated Poisson hidden
                        Markov model, where zero-inflation only happens
                        in State 1. Assume that priors, transition
                        rates and state-dependent parameters can be
                        subject-specific, clustered by group, or
                        common. But at least one set of the parameters
                        have to be common across all subjects.
dist_learn2             Distributed learning for a longitudinal
                        continuous-time zero-inflated Poisson hidden
                        Markov model, where zero-inflation only happens
                        in State 1 and covariates are for
                        state-dependent zero proportion and means.
                        Assume that priors, transition rates,
                        state-dependent intercepts and slopes can be
                        subject-specific, clustered by group, or
                        common. But at least one set of the parameters
                        have to be common across all subjects.
dist_learn3             Distributed learning for a longitudinal
                        continuous-time zero-inflated Poisson hidden
                        Markov model, where zero-inflation only happens
                        in State 1 with covariates in the
                        state-dependent parameters and transition
                        rates.
dzip                    pmf for zero-inflated poisson
fasthmmfit              Fast gradient descent / stochastic gradient
                        descent algorithm to learn the parameters in a
                        specialized zero-inflated hidden Markov model,
                        where zero-inflation only happens in State 1.
                        And if there were covariates, they could only
                        be the same ones for the state-dependent log
                        Poisson means and the logit structural zero
                        proportion.
fasthmmfit.cont         Fast gradient descent algorithm to learn the
                        parameters in a specialized continuous-time
                        zero-inflated hidden Markov model, where
                        zero-inflation only happens in State 1. And if
                        there were covariates, they could only be the
                        same ones for the state-dependent log Poisson
                        means and the logit structural zero proportion.
fasthmmfit.cont3        Fast gradient descent algorithm to learn the
                        parameters in a specialized continuous-time
                        zero-inflated hidden Markov model, where
                        zero-inflation only happens in State 1 with
                        covariates in the state-dependent parameters
                        and transition rates.
fasthsmmfit             Fast gradient descent / stochastic gradient
                        descent algorithm to learn the parameters in a
                        specialized zero-inflated hidden semi-Markov
                        model, where zero-inflation only happens in
                        State 1. And if there were covariates, they
                        could only be the same ones for the
                        state-dependent log Poisson means and the logit
                        structural zero proportion. In addition, the
                        dwell time distributions are nonparametric for
                        all hidden states.
grad_zipnegloglik_cov_cont
                        gradient for negative log likelihood function
                        in zero-inflated Poisson hidden Markov model
                        with covariates, where zero-inflation only
                        happens in state 1
grad_zipnegloglik_nocov_cont
                        gradient for negative log likelihood function
                        from zero-inflated Poisson hidden Markov model
                        without covariates, where zero-inflation only
                        happens in state 1
hmmfit                  Estimate the parameters of a general
                        zero-inflated Poisson hidden Markov model by
                        directly minimizing of the negative
                        log-likelihood function using the gradient
                        descent algorithm.
hmmsim                  Simulate a hidden Markov series and its
                        underlying states with zero-inflated emission
                        distributions
hmmsim.cont             Simulate a hidden Markov series and its
                        underlying states with zero-inflated emission
                        distributions
hmmsim2                 Simulate a hidden Markov series and its
                        underlying states with covariates
hmmsim2.cont            Simulate a continuous-time hidden Markov series
                        and its underlying states with covariates
hmmsim3.cont            Simulate a continuous-time hidden Markov series
                        and its underlying states with covariates in
                        state-dependent parameters and transition
                        rates.
hmmsmooth.cont          Compute the posterior state probabilities for
                        continuous-time hidden Markov models without
                        covariates where zero-inflation only happens in
                        state 1
hmmsmooth.cont2         Compute the posterior state probabilities for
                        continuous-time hidden Markov models where
                        zero-inflation only happens in state 1 and
                        covariates can only be included in the
                        state-dependent parameters
hmmsmooth.cont3         Compute the posterior state probabilities for
                        continuous-time hidden Markov models with
                        covariates in the state-dependent parameters
                        and transition rates
hmmviterbi              Viterbi algorithm to decode the latent states
                        for hidden Markov models
hmmviterbi.cont         Viterbi algorithm to decode the latent states
                        for continuous-time hidden Markov models
                        without covariates
hmmviterbi2             Viterbi algorithm to decode the latent states
                        in hidden Markov models with covariate values
hmmviterbi2.cont        Viterbi algorithm to decode the latent states
                        in continuous-time hidden Markov models with
                        covariates
hsmmfit                 Estimate the parameters of a general
                        zero-inflated Poisson hidden semi-Markov model
                        by directly minimizing of the negative
                        log-likelihood function using the gradient
                        descent algorithm.
hsmmfit_exp             Simulate a hidden semi-Markov series and its
                        underlying states with covariates where the
                        latent state distributions have accelerated
                        failure time structure whose base densities are
                        exponential
hsmmsim                 Simulate a hidden semi-Markov series and its
                        corresponding states according to the specified
                        parameters
hsmmsim2                Simulate a hidden semi-Markov series and its
                        underlying states with covariates
hsmmsim2_exp            Simulate a hidden semi-Markov series and its
                        underlying states with covariates
hsmmviterbi             Viterbi algorithm to decode the latent states
                        for hidden semi-Markov models
hsmmviterbi2            Viterbi algorithm to decode the latent states
                        in hidden semi-Markov models with covariates
hsmmviterbi_exp         Viterbi algorithm to decode the latent states
                        in hidden semi-Markov models with covariates
                        where the latent state durations have
                        accelerated failure time structure
package-ziphsmm         zero-inflated poisson hidden (semi-)Markov
                        models
retrieve_cov_cont       retrieve the natural parameters from the
                        working parameters in zero-inflated Poisson
                        hidden Markov model with covariates, where
                        zero-inflation only happens in state 1
retrieve_cov_cont3      retrieve the natural parameters from the
                        working parameters in zero-inflated Poisson
                        hidden Markov model with covariates in
                        state-dependent parameters and transition rates
retrieve_nocov_cont     retrieve the natural parameters from working
                        parameters for a continuous-time zero-inflated
                        Poisson hidden Markov model where
                        zero-inflation only happens in state 1
rzip                    generate zero-inflated poisson random variables
zipnegloglik_cov_cont   negative log likelihood function for
                        zero-inflated Poisson hidden Markov model with
                        covariates, where zero-inflation only happens
                        in state 1
zipnegloglik_cov_cont3
                        negative log likelihood function for
                        zero-inflated Poisson hidden Markov model with
                        covariates in state-dependent parameters and
                        transition rates
zipnegloglik_nocov_cont
                        negative log likelihood function for
                        zero-inflated Poisson hidden Markov model
                        without covariates, where zero-inflation only
                        happens in state 1
