Title: Hidden Markov Model for Continuous Observations Processes
Maintainer: Mikhail A. Beketov <mikhail.beketov@gmx.de>
Description: The package includes the functions designed to analyse continuous observations processes with the Hidden Markov Model approach. They include Baum-Welch and Viterbi algorithms and additional visualisation functions. The observations are assumed to have Gaussian distribution and to be weakly stationary processes. The package was created for analyses of financial time series, but can also be applied to any continuous observations processes.
Packaged: 2014-02-11 15:19:07 UTC; mikhailbeketov

Title: A Fast Solver for Henderson Mixed Model Equation via Row
Description: Consider the linear mixed model with normal random effects. A typical method to solve Henderson's Mixed Model Equations (HMME) is recursive estimation of the fixed effects and random effects. We provide a fast, stable, and scalable solver to the HMME without computing matrix inverse. See Kim (2017) <arXiv:1710.09663> for more details.

Title: Markov Regime Switching Copula Models Estimation and Goodness of
Author: Mamadou Yamar Thioub <mamadou-yamar.thioub@hec.ca>,
Maintainer: Mamadou Yamar Thioub <mamadou-yamar.thioub@hec.ca>
Description: R functions to estimate and perform goodness of fit test for several
Markov regime switching and mixture bivariate copula models.
The goodness of fit test is based on a Cramer von Mises statistic and
uses the Rosenblatt transform and parametric bootstrap to estimate the p-value.
The estimation of the copula parameters are based on the pseudo-maximum likelihood
method using pseudo-observations defined as normalized ranks.
Depends: matrixcalc, mvtnorm, foreach, doParallel, copula
Packaged: 2018-10-19 17:13:00 UTC; abdoulaye

Packaged: 2018-12-01 11:12:25 UTC; abdoulaye

Title: Hidden Markov Models with Extra Zeros
Author: Ting Wang - I am grateful to Jiancang Zhuang for some helpful suggestions and contributions
Description: Contains functions for hidden Markov models with observations having extra zeros as defined in the following two publications, Wang, T., Zhuang, J., Obara, K. and Tsuruoka, H. (2016) <doi:10.1111/rssc.12194>; Wang, T., Zhuang, J., Buckby, J., Obara, K. and Tsuruoka, H. (2018) <doi:10.1029/2017JB015360>. The observed response variable is either univariate or bivariate Gaussian conditioning on presence of events, and extra zeros mean that the response variable takes on the value zero if nothing is happening. Hence the response is modelled as a mixture distribution of a Bernoulli variable and a continuous variable. That is, if the Bernoulli variable takes on the value 1, then the response variable is Gaussian, and if the Bernoulli variable takes on the value 0, then the response is zero too. This package includes functions for simulation, parameter estimation, goodness-of-fit, the Viterbi algorithm, and plotting the classified 2-D data. Some of the functions in the package are based on those of the R package 'HiddenMarkov' by David Harte.