mixvlmc-package | R Documentation |
Estimates Variable Length Markov Chains (VLMC) models and VLMC with covariates models from discrete sequences. Supports model selection via information criteria and simulation of new sequences from an estimated model. See Bühlmann, P. and Wyner, A. J. (1999) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/aos/1018031204")} for VLMC and Zanin Zambom, A., Kim, S. and Lopes Garcia, N. (2022) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/jtsa.12615")} for VLMC with covariates.
Mixvlmc uses the following options()
:
mixvlmc.maxit
: maximum number of iterations in model fitting for covlmc()
mixvlmc.predictive
: specifies the computing engine used for model fitting
for covlmc()
. Two values are supported:
"glm"
(default value): covlmc()
uses stats::glm()
with a binomial
link (stats::binomial()
) for a two values state space, and VGAM::vglm()
with a multinomial link (VGAM::multinomial()
) for a state space with
three or more values;
"multinom"
: covlmc()
uses nnet::multinom()
in all cases.
The first option "glm"
is recommended as both stats::glm()
and VGAM::vglm()
are able to detect and deal with degeneracy in the data set.
mixvlmc.backend
: specifies the implementation used for the context tree
construction in ctx_tree()
, vlmc()
and tune_vlmc()
. Two values are
supported:
"R"
(default value): this corresponds to the original almost pure R
implementation.
"C++"
: this corresponds to the experimental C++ implementation. This
version is significantly faster than the R version, but is still
considered experimental.
Maintainer: Fabrice Rossi Fabrice.Rossi@apiacoa.org (ORCID) [copyright holder]
Other contributors:
Hugo Le Picard lepicardhugo@gmail.com (ORCID) [contributor]
Guénolé Joubioux guenole.joubioux@gmail.com [contributor]
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