Maintenance release to accommodate breaking changes in dplyr 1.1.0.
This first major release accompanies the publication of an article in the Journal of Statistical Software:
Vasilopoulos, K., Pavlidis, E., & Martínez-García, E. (2022). exuber: Recursive Right-Tailed Unit Root Testing with R. Journal of Statistical Software, 103(1), 1–26. https://doi.org/10.18637/jss.v103.i10
augment
method for radf_obj and radf_cvNew arg trunc
Fixed inconsistencies among functions.
Now radf stores the data that are later can be accessed with
mat+
Advanced features on datestamping: New columns that indicate:
New datestamping procedure rev_radf etc.
New bootstrap procedure radf_wb_cv2 and
radf_wb_distr2
New coloring convention for plotting ds and
obj classes
radf_obj and
radf_cv.progress package for progress_bar.Maintenance release for compatibility with dplyr v1.0.0.
We have the following design in mind for future scalability. If you
want make inference about radf models, then the estimation
can be achieved with radf() function and return an object
of class radf_obj, and the critical values can be achieved
with radf_*_cv() and return an object of class
radf_cv.
autoplot() for radf models has been
refactored and new features have been added for more flexibility and
conformity with the {ggplot} mindset.autoplot,
ggarrange() is now defunct.fortify() methods have been replaced by
tidy(), augment(), tidy_join()
and glance_join() methods. fortify() methods
are now defunct.glance() is now defunct. The user can use
tidy() with panel=TRUE instead.mc_cv() to radf_mc_cv().
mc_cv() is now deprecated.mc_distr() to radf_mc_distr().
mc_distr() is now deprecated.wb_cv() to radf_wb_cv().
wb_cv() is now deprecated.wb_distr() to radf_wb_distr().
wb_distr() is now deprecated.sb_cv() to radf_sb_cv().
sb_cv() is now deprecated.sb_distr() to radf_sb_distr().
sb_distr() is now deprecated.crit dataset to radf_crit.col_names() to series_names().
col_names() is now deprecated.exuberdata that
accommodates critical values for up to 2000 observations. Critical
values can be examined with exuberdata::radf_crit2. The
package is created through drat R archive Template, and can
be easily installed with
install.packages('exuberdata', repos = 'https://kvasilopoulos.github.io/drat/', type = 'source')
or through install_exuberdata wrapper function that is
provided in exuber.zoo has been used as a dependency to import
the method index(). We made the decision to remove
zoo and create a new method index()
internally.opt_bsadf = conservative for the simulated
critical values (crit), also reduced the size of the
crit from 700 to 600 due to package size restrictions.sim_dgp1() and sim_dgp2() have been
renamed to sim_psy1() and sim_psy2() to better
describe the origination of the dgp.sim_dgp1() and sim_dgp2() have been
soft-deprecated.autoplot_radf() arranges automatically multiple graphs,
to return to previous behavior we included the optional argument
arrange which is set to TRUE by default.Three new functions have been added to simulate empirical distributions for:
mc_dist(): Monte Carlowb_dist(): Wild Bootstrapsb_dist(): Sieve Bootstrapand a function that can calculate the p-values
calc_pvalue() given the above distributions as
argument.
Also methods tidy() and autoplot() have
been added to turn the object into a tidy tibble and draw a particular
plot with ggplot2, respectively.
tidy() methods for objects of class radf,
cv.augment() methods for objects of class
radf and cv.augment_join() to combine object radf and
cv into a single data.frame.glance() method for objects of class
radf.summary(),
diagnostics() and datestamp().wb_cv()seed argument to functions that are using rng. Also the
option to declare a global seed for reproducibility with the
option(exuber.global_seed = ###)sb_cv() and wb_cv()now can parse data that
contain a date-column. Similarly, to what radf() is
doing.sb_cv reference.datestamp and
diagnostics.datestamp dummy is now an attribute.Some of the arguments in the functions were included as options, you
can set the package options with
e.g. options(exuber.show_progress = TRUE).
parallel option boolean, allows for parallel in
critical values computation.ncores option numeric, sets the number of cores,
defaults to max - 1.show_progress option boolean, allows you to disable the
progress bar, defaults to TRUE.radf()sb_cv() function: Panel Sieve Bootstrapped
critical valuessummary(), diagnostics,
datestamp() and autoplot(), without having to
specify argument cv. The critical values have been simulated from
mc_cv() function and stored as data. Custom critical values
should be provided by the user with the option cv.ggarrange() function, that can arrange a list of
ggplot objects into a single grob.fortify to arrange a data.frame from
radf() function.radf() can parse date from ts
objects.report() has been renamed into
summary().plot() has been renamed into
autoplot().plot() and report() are soft
deprecated.