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.
gg_season() not working with daily data showing
seasonality > 1 week.gg_irf() for plotting impulse responses
(typically obtained from using IRF() with fable
models).cointegration_johansen() and
cointegration_phillips_ouliaris() from
urca.gg_season() not wrapping across
facet_period argument correctly.Minor patch to resolve CRAN check issues with ggplot2 v3.5.0 breaking changes.
gg_season() breaks issue with
ggplot2 v3.5.0Minor patch to resolve CRAN check issues with S3 method consistency.
tapered argument to ACF() and
PACF() for producing banded and tapered estimates of
autocovariance (#1).gg_season() now allows seasonal period identifying
labels to be nudged and repelled with the labels_repel,
labels_left_nudge, and labels_right_nudge
arguments.gg_season() behaviour of max_col has been
restored, where colours aren’t used if the number of subseries to be
coloured exceeds this value. The default has changed to Inf
since this function now supports continuous colour guides. A new
argument max_col_discrete has been added to control the
threshold for showing discrete and continuous colour guides (#150).guerrero() method to maintain a consistent
subseries length by removing the first few observations of needed. This
more closely matches the described method, and the implementation in the
forecast package.grid.draw() method for ensemble graphics
(gg_tsdisplay() and gg_tsresiduals()). This
allows use of ggsave() with these plots (#149).generate(<STL>) returning
$.sim as a num [1:n(1d)] instead of
num [1:72] (fable/#336).gg_season() incorrectly grouping some
seasonal subseries.CCF() now matches stats::ccf()
x and y arguments (#144).Minor release for compatibility with an upcoming ggplot2 release. This release contains a few bug fixes and improvements to existing functionality.
gg_tsresiduals() function now allows the type of
plotted residual to be controlled via the type
argument.STL()
decompositions. For data with a single seasonal pattern, the window has
changed from 13 to 11. This change is based on results from simulation
experiments.seasonal::seas() defaults were not
being used in X_13ARIMA_SEATS() when
defaults = "seasonal" (#130).gg_subseries() on data with spaces in
the index column name (#136).... in ACF(),
PACF(), and CCF() with y (and
x for CCF()) arguments. This change should not
affect the code for most users, but is important for the eventual
passing of ... to acf(), pacf()
and ccf() in a future version (#124).Small patch to fix check issues on Solaris, and to resolve
components() for automatically selected transformations in
X_13ARIMA_SEATS().
X_13ARIMA_SEATS() decomposition method. This is a
complete wrapper of the X-13ARIMA-SEATS developed by the U.S. Census
Bureau, implemented via the seasonal::seas() function. The
defaults match what is used in the seasonal pacakge, however these
defaults can be removed (giving an empty default model) by setting
defaults="none".X_13ARIMA_SEATS() method officially deprecates
(supersedes) the X11() and SEATS() models
which were previously not exported (#66).generate() method for STL()
decompositions. The method uses a block bootstrap method to sample from
the residuals.fitted() and residuals() methods for
STL() decompositions.guerrero() default lower bound for Box-Cox
lambda selection to from -1 to -0.9. A transformation parameter of -1
typically results from data which should not be transformed with a
Box-Cox transformation, and can result in very inaccurate forecasts if
such a strong and inappropriate transformation is used.A minor release to fix check issues introduced by changes in an upstream dependency.
gg_season() labels are low aligned outward (#115).gg_season() and gg_subseries() (#117).gg_season()gg_lag() facets are now displayed with a 1:1 aspect
ratio.n_flat_spots() function has been renamed to
longest_flat_spot() to more accurately describe the
feature.gg_season() and ggsubseries() date
structure improvements.n_flat_spots() return name is now
“longest_flat_spot” to better describe the feature.gg_tsdisplay() erroring
when the spec.ar order is chosen to be 0.CCF() lag being spaced by multiples of the data’s
frequency.gg_season() and
gg_subseries() (#107).View() not working on ACF(),
PACF() and CCF() outputs.Minor patch to resolve upstream check issues introduced by dplyr v1.0.0 and tsibble v0.9.0.
polar = TRUE in gg_season().ACF().feat_spectral() to use
stats::spec.ar() instead of
ForeCA::spectral_entropy(). Note that the feature value
will be slightly different due to use of a different spectral estimator,
and the fix of a bug in ForeCA.feat_stl().gg_lag() have been reversed for
consistency with stats::lag.plot().feat_intermittent()gg_tsdisplay() not
working with plotting expressions of data.gg_subseries() erroring when certain
column names are used (#95).STL()
specials.var_tiled_var() no longer includes partial tile windows
in the computation.feat_stl().components(). For example,
tourism %>% STL(Trips) is now
tourism %>% model(STL(Trips)) %>% components(). This
change allows for more flexible decomposition specifications, and better
interfaces for decomposition modelling.feat_spectral() not showing
results.ACF(), PACF() and
CCF() for tidyr change.gg_tsdisplay() will no longer fail on non-seasonal data
with missing values. The last plot will instead show a PACF in this case
(#76)stat_arch_lm()
(#85)gg_season, gg_subseries,
gg_lag, gg_tsdisplay,
gg_tsresiduals, gg_arma.ACF, PACF, CCF, and
autoplot.tbl_cffabletools::features(): feat_stl,
feat_acf, feat_pacf, guerrero,
unitroot_kpss, unitroot_pp,
unitroot_ndiffs, unitroot_nsdiffs,
box_pierce, ljung_box,
var_tiled_var, var_tiled_mean,
shift_level_max, shift_var_max,
shift_kl_max, feat_spectral,
n_crossing_points, n_flat_spots,
coef_hurst, stat_arch_lmclassical_decomposition, STLThese 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.