This first major release accompanies the publication of an article in the Journal of Statistical Software:
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.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methods have been replaced by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.