plot.nppi() function now returns an additional plot
for JAB Pseudo Value Stability.nppi() there was an inconsistency in dimensionality
between the jab_point_values and the
jab_pseudo_values due to how NA values were handled. This
is fixed such that both vectors will always have the same length.nppi() function now returns an additional item in
the nppi object: jab_pseudo_values which
include the ith MBJ pseudo-value used in the JAB variance
estimator.nppi() there was an incorrect computation of the JAB
variance estimator, which did use the ith MBJ pseudo-value in
place of the the ith MBJ point value. This is now fixed.A new function nppi() has been added to the package.
This function estimates the optimal block-length using the
non-parametric plug-in (NPPI) method of Lahiri, Furukawa, Lee,
(2007).
A new S3 plot method plot.nppi() has been added to
the package. This function outputs a diagnostic plot for JAB point
values calculated from the above nppi() function.
In pwsd(), m_hat was indexing the first
insignificant lag of the correlation structure rather than the first
significant lag prior to the consecutive run of insignificant
lags.
pwsd() and plot.pwsd() now output
significance bands that match rho_k_critical exactly. Prior
to this, the significance bands on the correlogram output were generated
using the plot.acf(ci = ) argument which led to misleading
graphical representations of the implied hypothesis test’s critical
value in the PWSD method.
In hhj(), sub_block_length has been
changed to sub_sample to avoid confusion with the other
tuning parameter, pilot_block_length
A new vignette has been included on tuning and diagnosing problematic output from the selection functions!
pwsd() now includes a new argument to override the
implied hypothesis test by setting m_hat directly.
In pwsd(), rho.k.critical has been
changed to snake case rho_k_critical in the
$parameters matrix from output of class ‘pwsd’ objects from
pwsd().
In hhj(), if subsample = is set
directly it is now rounded to the nearest whole number.
plot.pwsd() now includes “darkmagenta” significance
lines to match pwsd().
plot.pwsd() now explicitly includes an option to
customize title with main = .
hhj() now includes a warning message if the supplied
iteration limit n_iter is reached but still outputs an
object of class ‘hhj.’