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| asymptoticCovMat | Asymptotic covariance matrix of the HMM parameters |
| data_mixture | Simulated univariate mixture of 3 gausssian distributions |
| distributionClass | Set the parameters for the distributions of observations |
| distributionSet | Set the parameters for the distributions of observations |
| forwardBackward | forward-backward procedure |
| forwardbackward | forward-backward procedure |
| HMMClass | Set the parameters for the hidden Markov models |
| HMMFit | Fit an Hidden Markov Model |
| HMMFitClass | Fit an Hidden Markov Model |
| HMMGraphicDiag | Graphic diagnostic of the HMM estimation |
| HMMPlotSerie | Plot univariates series in each estimated states |
| HMMSet | Set the parameters for the hidden Markov models |
| HMMSim | Simulation of an Hidden Markov Model |
| n1d_3s | A 3 states HMM with univariate gaussian distribution data set |
| n3d_2s | A 2 states HMM with 3D gaussian distribution data set |
| obs_n1d_3s | A 3 states HMM with univariate gaussian distribution data set |
| obs_n3d_2s | A 2 states HMM with 3D gaussian distribution data set |
| print.summary.HMMFitClass | Fit an Hidden Markov Model |
| summary.HMMFitClass | Fit an Hidden Markov Model |
| viterbi | Viterbi algorithm |
| weather | Simulated discrete HMM |