Packaged: Wed May 31 08:52:55 2006; hornik

Packaged: Thu Aug 24 16:44:27 2006; rolf

Author: Rolf Turner <r.turner@auckland.ac.nz> and Limin Liu.
Packaged: Thu Jul  3 10:58:24 2008; rturner

Packaged: Thu Feb 26 08:40:57 2009; hornik

Title: Hidden Markov models with discrete non-parametric observation
observation distributions to data sets.  Simulates data from
such models.  Finds most probable underlying hidden states and

such models.  Finds most probable underlying hidden states, the
most probable sequences of such states, and the log likelihood
of a collection of observations given the parameters of the

from such models.  Finds most probable underlying hidden
states, the most probable sequences of such states, and the
log likelihood of a collection of observations given the

Title: Hidden Markov Models with Discrete Non-Parametric Observation

observation distributions to data sets.  The observations may
be univariate or bivariate. Simulates data from such models.
Finds most probable underlying hidden states, the most
probable sequences of such states, and the log likelihood
of a collection of observations given the parameters of
the model.  Auxiliary predictors are accommodated in the

Title: Constructs Asymmetric Hidden Markov Models
person("Robert Gentleman and Ross Ihaka",
Description: Asymmetric hidden Markov model (HMM-A) learning.
HMM-As are similar to regular HMMs, but use Bayesian Networks (BNs) in
their emission distribution. The HMM-As can therefore offer more problem
Imports: bnlearn, mhsmm, MCMCpack, Rgraphviz, graph, methods
Packaged: 2020-04-08 10:12:52 UTC; joopthibaudier

person(given = "Robert Gentleman and Ross Ihaka",
person(given = "The R Development Core Team",
Packaged: 2020-06-26 13:05:07 UTC; joopthibaudier

Title: Hidden Markov Models for High Dimensional Data
person("Giuseppina", "Guatteri", role = "aut",
Description: Some algorithms for the study of Hidden Markov Models for two different types of data. For the study of univariate and multivariate data in a finite framework, we provide some methods based on the definition of a Gaussian copula function to define the dependence between data (for further details, see Martino A., Guatteri, G. and Paganoni A. M. (2018) <https://mox.polimi.it/publication-results/?id=776&tipo=add_qmox>). For the study of functional data, we define an objective function based on distances between random curves to define the emission functions of the HMM (for further details, see Martino A., Guatteri, G. and Paganoni A. M. (2019) <https://mox.polimi.it/publication-results/?id=805&tipo=add_qmox>).
Imports: graphics, stats, gmfd, mvtnorm, roahd