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
New 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more flexibility and conformity with the {ggplot} mindset.autoplot, ggarrange() is now defunct.fortify() methods have been replaced by tidy(), augment(), tidy_join() and glance_join() methods.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.