| package-rarhsmm-package | Regularized Autoregressive Hidden Semi Markov Models |
| rarhsmm-package | Regularized Autoregressive Hidden Semi Markov Models |
| em.hmm | EM algorithm to compute maximum likelihood estimate of Gaussian hidden Markov models with / without autoregressive structures and with / without regularization on the covariance matrices and/or autoregressive structures. |
| em.semi | EM algorithm to compute maximum likelihood estimate of Gaussian hidden semi-Markov models with / without autoregressive structures and with / without regularization on the covariance matrices and/or autoregressive structures. |
| finance | NYSE stock closing price data |
| hmm.predict | 1-step forward prediction for (autoregressive) Gaussian hidden Markov model |
| hmm.sim | Simulate a Gaussian hidden Markov series with / without autoregressive structures |
| hsmm.predict | 1-step forward prediction for (autoregressive) Gaussian hidden semi-Markov model |
| hsmm.sim | Simulate a Gaussian hidden semi-Markov series with / without autoregressive structures |
| mvdnorm | multivariate normal density |
| mvrnorm | multivariate normal random number generator |
| package-rarhsmm | Regularized Autoregressive Hidden Semi Markov Models |
| rmultinomial | multinomial random variable generator |
| smooth.hmm | Calculate the probability of being in a particular state for each observation. |
| smooth.semi | Calculate the probability of being in a particular state for each observation. |
| viterbi.hmm | Viterbi algorithm to decode the latent states for Gaussian hidden Markov model with / without autoregressive structures |
| viterbi.semi | Viterbi algorithm to decode the latent states for Gaussian hidden semi-Markov model with / without autoregressive structures |