# Zero-Inflated Poisson Hidden (Semi-)Markov Models

## Documentation for package ‘ziphsmm’ version 2.0.6

- DESCRIPTION file.

## Help Pages

package-ziphsmm-package | zero-inflated poisson hidden (semi-)Markov models
ziphsmm-package | zero-inflated poisson hidden (semi-)Markov models
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