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| 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 |