HMMextra0s: Hidden Markov Models with Extra Zeros

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.

Package details

AuthorTing Wang - I am grateful to Jiancang Zhuang for some helpful suggestions and contributions
MaintainerTing Wang <[email protected]>
LicenseGPL (>= 2)
Version1.0.0
URL http://www.stats.otago.ac.nz/?people=ting_wang
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("HMMextra0s")

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HMMextra0s documentation built on May 2, 2019, 9:11 a.m.