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 |