The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
This 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has been replaced by a logical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 totrim.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 imprecisionsimulate.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
file to track changes to the
package.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.