NEWS.md
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_cv
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.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methods have been replaced by tidy()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
.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
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.