This is a bug fix release (see issue #79 for the main bugs):
loglikelihood()
and related functions for the
multinom
predictive enginecovlmc
)VGAM
package in corner cases with
degenerate modelsThis release has no visible changes and fixes internal issues:
mode
parameter of cutoff.vlmc()
has been renamed to scale
type
parameter has been removed from contexts()
variants and the
default result format has been modified significantlycounts
parameter from contexts.vlmc()
and contexts.covlmc()
has
been replaced by a logical parameter named local
contexts()
sequences are now reported by default in temporal orderThe main major new feature of this version is the inclusion of a C++
implementation of context tree and VLMC construction. A new option
mixvlmc.backend
can be used to switch globally from the original "R"
implementation to the new "C++"
back end. A new backend
parameter has
been added to ctx_tree()
, vlmc()
and tune_vlmc()
to enable local back end
selection.
The C++ implementation is significantly faster than the R implementation, at
least by a factor 10. While it has been thoroughly tested, it is still
considered experimental notably because it does not apply to COVLMC (setting the
global option to "C++"
has not effect on COVLMC model construction). For
context trees and VLMC, results should not depend on the back end, at least
within numerical precision. The only notable difference is the ordering of the
contexts which differs between back ends: in a call to contexts()
, the first
context for the R back end will generally not be the first context for the C++
back end.
contexts()
can now report the positions of each context in the original time
seriesctx_node
objects
using the find_sequence()
function. A collection of new functions can be
used to manipulate the nodes and gain fine grain information on the
corresponding sequences (issue #50). contexts()
reports now contexts as a list of ctx_node
objectspredict.vlmc()
and predict.covlmc()
can be used to make one
step ahead predictions of a time series based on a (CO)VLMC model (issue #46).
Those function are documented in a new vignette (vignette("prediction")
)logLik.vlmc()
, logLik.covlmc()
loglikelihood()
and loglikelihood.covlmc()
have been revised, expanded to
include three possible definitions of the likelihood function, and documented
in a new vignette (vignette("likelihood")
)tune_vlmc()
and tune_covlmc()
can be used with the different likelihood
function definitionstune_vlmc()
and tune_covlmc()
can be plotted using base R
graphics or ggplot2 (issue #36)tune_covlmc()
can trim the best model (and the initial one) if asked to trim.covlmc()
implements simple trimming for VGAM based objects (issue #48)cutoff()
uses a new tolerance
parameter to avoid reporting cut off values
that are almost identical due to numerical imprecision simulate.vlmc()
implements a user specified burn in period (issue #40)simulate.vlmc()
and simulate.covlmc()
now handle the random generator
state as does stats::simulate()
(issue #56)simulate.covlmc()
unreliable for state spaces with three or more statessimulate.covlmc()
that occurred in contexts with a longer
self memory compared to their covariate memorymetrics.vlmc()
and align the results with the ones
obtained by using direct calculation on the results of predict.vlmc()
contexts()
resultsctx_tree()
documentation and its default valuecontexts()
documentation and its default valuetrim.covlmc()
simulate.vlmc()
and
simulate.covlmc()
NEWS.md
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