ziphsmm package - RDocumentation

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.

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

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

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

zipnegloglik_cov_cont3

negative log likelihood function for zero-inflated Poisson hidden Markov model with covariates in state-dependent parameters and transition rates

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.

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_nocov_cont

negative log likelihood function for zero-inflated Poisson hidden Markov model without covariates, where zero-inflation only happens in state 1

hsmmsim

Simulate a hidden semi-Markov series and its corresponding states according to the specified parameters

hmmviterbi2

Viterbi algorithm to decode the latent states in hidden Markov models with covariate values

package-ziphsmm

zero-inflated poisson hidden (semi-)Markov models

rzip

generate zero-inflated poisson random variables

hmmviterbi.cont

Viterbi algorithm to decode the latent states for continuous-time hidden Markov models without covariates

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

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

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

hsmmviterbi

Viterbi algorithm to decode the latent states for hidden semi-Markov models

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.

hsmmviterbi2

Viterbi algorithm to decode the latent states in hidden semi-Markov models with covariates

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

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.

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.

convolution

Convolution of two real vectors of the same length.

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.

CAT

Pseudo activity counts (per minute) data for cats

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.

dzip

pmf for zero-inflated poisson

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

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.

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.cont

Compute the posterior state probabilities for continuous-time hidden Markov models without covariates where zero-inflation only happens in state 1

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.

hmmsim2

Simulate a hidden Markov series and its underlying states with covariates

hmmsim.cont

Simulate a hidden Markov series and its underlying states with zero-inflated emission distributions

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.

hmmviterbi2.cont

Viterbi algorithm to decode the latent states in continuous-time hidden Markov models with covariates

hmmsim

Simulate a hidden Markov series and its underlying states with zero-inflated emission distributions

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

ziphsmm (version 2.0.6)

Zero-Inflated Poisson Hidden (Semi-)Markov Models

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Functions in ziphsmm (2.0.6)