Version 1.5.1.1
------------------------------
MINOR CHANGES
- Minimal changes to comply with CRAN requirements, specially dealing with
M1mac architecture.
- Remove suggested packages: rstan and nimble.
- Vignette minimal changes to comply with CRAN.
Version 1.5.0
------------------------------
MAJOR CHANGES
- New function ggs_grb() to plot the shrinkage of Potential Scale Reduction
Factor (Rhat) over batches of parameters.
- New argument "keep_original_order" to ggs() that forces to keep the
parameters in the order they are originally provided.
MINOR CHANGES
- Ready for dplyr-1.0.
- New simplified way to draw histograms manually in ggs_histogram(),
ggs_ppmean() and ggs_ppsd().
Version 1.4.1
------------------------------
MINOR CHANGES
- Improvements in the documentation of datasets provided by the package.
Version 1.4
------------------------------
MAJOR CHANGES
- ggs_caterpillar() makes comparisons easier. In includes a new argument
"comparison" that allows to specify the variable that sets the dodged
parameters.
- ggs_geweke(), ggs_Rhat() and ggs_effective() have a new argument "plot" that
allows to return the plot (as always, and the default) but also the tidy
dataframe containing the calculations of the diagnostics.
- New function ggs_measures() that returns a tidy dataframe with the formal
diagnostics of ggs_geweke(), ggs_Rhat() and ggs_effective().
- Improvements to ggs_effective(): A new version to calculate the number of
effective draws is introduced, based on the steps followed by the coda package.
Also the function now allows to report either the number of effective
samples or its proportion.
- ggmcmc() was not correctly handling parameter names when plotting
caterpillar plots in functions that employ par_labels.
- Several improvements in the documentation, including the datasets.
MINOR CHANGES
- Concrete "by" options in left_joins to avoid messages.
- Minor bug in ggs_rocplot() needed to prepare for dplyr >= 1.0.
- Include 'nimble' as a suggestion.
Version 1.3
------------------------------
MAJOR CHANGES
- A new function ggs_pcp() displays the percent of correctly predicted outcomes
in a binary model.
- ggs_Rhat() now allows to select the Rhat specification. The "BG98" is the
one used by the coda package, but the default is still the "BDA2". Check the
documentation for more details.
- A new function ggs_effective() displays the number of effective independent
draws.
- A new function plab() is included to help creating matching data frames for
parameters and labels.
- Following "Bayesian Data Analysis", v3 ggs() introduces the possibility of
splitting the sequences into two chains. The new argument "splitting" is
however FALSE by default.
- ggs_caterpillar() now includes a new argument "label" that allows to specify
the variable to be displayed in the labels, hence facilitating the combination
with facet_*().
MINOR CHANGES
- Adjust vertical size of facetted figure.
- Improved way to calculate the Geweke diagnostic.
- More informative error messages.
- Updated list of contributors.
- Improved documentation in the main github page.
- Bug solving the management of families using stan.
Version 1.2
------------------------------
MAJOR CHANGES
- Substantial performance improvement involving the treatment of families.
- More control of aesthetics for ggs_caterpillar(). Thanks to Jonathan
Gilligan.
- Add HDP bands in ggs_density() and ggs_traceplot() with the argument (hdp =
TRUE).
MINOR CHANGES
- Fixed a bug in sde0f when density is 0 or very close. Thanks to Brian Stock.
- Vignettes (JSS and using ggmcmc) have been adapted to new requirements of
R-development.
- Minor bibliography issues solved in the vignettes.
- Minor documentation issue with original s object being refered as D.
- Using ggmcmc vignette improved with examples of how to combine ggs() objects
with ggplot2 functions. Specifically, with facet_wrap() to accommodate several
figures in a single display.
Version 1.1
------------------------------
MAJOR CHANGES
- New argument ('sort') in ggs() that allows to control whether the custom
sort of variables in family names first and numerical values later is passed
(default) or the original order of parameter names is kept.
MINOR CHANGES
- New stan/rstan versions treat warmup differently and this is solved.
- Solved a bug in ggs_caterpillar() when a list is passed.
- par_labels argument in ggs() now allows also a tbl_df (in addition to a
data frame).
Version 1.0
------------------------------
MAJOR CHANGES
- Version 1.0 comes with the publication of the article at the Journal of
Statistical Software.
- ggmcmc() can produce reports in HTML format, when a file in HTML extension
is given as argument. The figures can be in PNG or SVG formats.
- 'stanreg' and 'brmsfit' objects from packages 'rstanarm' and 'brms',
respectively, are supported by ggs().
- Improved documentation (HTML vignette) with the latest improvements (greek
letters, rstararm/brms packages supported by ggs(), HTML report, acknowledgments).
MINOR CHANGES
- Normalize plural for all figure titles.
- Warning messages from joins are not shows to the user.
- stanfit objects correctly treat Parameter names.
- Internal improvement of the function that does the custom sort of the
parameter names, as well as their labels.
- References to the JSS article have been incorporated in all functions that
generate plots.
- Improved documentation (JSS vignette) to accommodate last editorial changes.
Version 0.8
------------------------------
MAJOR CHANGES
- Introduce the possibility to get Greek letters in the plots, as parameter
names.
- Vignettes are added, improving the package documentation.
- ggs_separation() incorporates new arguments: uncertainty_band (to avoid the
uncertainty band of the predicted values), show_labels (to add the
Parameter as the label of the case in the x-axis.
MINOR CHANGES
- Solved a bug that passed thick_ci and thin_ci incorrectly in
ggs_caterpillar().
- Parameters are sorted by their natural order following the numerical order
inside the family. This helps ggs_separation() and ggs_rocplot(), as they
expect observed values in numerical order.
- Solved a bug with the calculation of the expected number of events in
ggs_separation().
- Added some supressWarnings() to avoid annoying messages when doing some
left_join() operations.
- ggs_separation now plots the observed cases as proportional to the
horizontal space, not as simple lines. This makes visualization clearer and
more accurate to reality.
Version 0.7.3
------------------------------
MINOR CHANGES
- Updated examples for ggs_pairs() to work with newer version of GGally.
- GGally is imported instead of depends.
- Specify a certain version of tidyr (0.3.1 needed)
- Add a check that "Parameter" is a factor, which is not granted with the
development version of tidyr as of 04/01/2016.
Version 0.7.2
------------------------------
MINOR CHANGES
- Solve revdep issue with tidyr-0.3 due to unqualified imports of "%>%, and
use explicit calls to dplyr and tidyr in all functions taken from those packages.
- Improve citation.
- Improve the text of the package description.
- Add BugReports fields and include another URL.
Version 0.7.1
------------------------------
MINOR CHANGES
- Minor changes to pass CRAN checks.
Version 0.7
------------------------------
MAJOR CHANGES
- Added new function ggs_pairs() (thanks to Max Joseph) that produces
scatterplot matrices of parameters, helping with posterior correlation.
Library GGally is now a dependency.
- ggs() is now able to process warmup (burnin) samples from stan, and so the
argument "inc_warmup" now does something useful.
MINOR CHANGES
- ci() does not allow lists of data frames, so change it accordingly in the
help file (thanks to Alex Zvoleff).
- Solve minor issues with par_labels.
- Solve minor issue with ggs_crosscorrelation not showing correctly par_labels
(thanks to Juste).
- Add a small example to the documentation about the use of the family
argument in ggs().
- Better specify the origin of dplyr::* functions, to avoid collisions with
other packages.
Version 0.6
------------------------------
MAJOR CHANGES
- Include data and samples from models in order to facilitate examples
linear: MCMC output from a linear regression model with dummy data.
logit: MCMC output from a logit regression model with dummy data.
radon: MCMC output and other description variables for a hierarchical /
multilevel model based on radon example at Gelman & Hill.
- Major revision of the code moving plyr to dplyr and reshape2 to tidyr
The main object from ggs() is no more a data frame, but a tbl_df.
Deleted the possibility of parallelization. It is not (yet) implemented in
dplyr, but in any case, dplyr is clearly faster than plyr.
- New function ci() that calculates credible intervals for a ggs() object.
- When using par_labels, not only the columns "Parameter" and "Label" are
considered, but also the resulting ggs() object contains other columns present
in par_labels and the old Parameter name. This allows mainly ggs_caterpillar()
(but also other functions) to use facets or colors, fills, etc.
- New argument "simplify" in ggs_traceplot() or "simplify_traceplot" in ggmcmc()
that allows to keep only a percentage of random chains, so that the size of the
plot (and the time taken) is reduced considerably. But use it with care, because
then the chain is no more complete.
- Homogenize function names, using underscores "_" everywhere, instead of dots
".", in calc_bin(), gl_unq() and roc_calc().
MINOR CHANGES
- When selecting a family of parameters, maintain parameter ordering for
multipage outputs.
- par_labels had a bug that produced wrong reordering of labels, and levels of
parameters were incorrectly sorted. Although it is a minor change, the mistake
was severe.
- Labels in x-axis in crosscorrelation matrix are better aligned and more
legible.
- Solve scaling issues in Rhat. Introduce a meaningful default.
- Better examples for ggs_separation() and ggs_rocplot().
- Documentation improved for internal functions.
Version 0.5.1
------------------------------
MINOR CHANGES
- When the chain is stucked in the same value, the computation of the ar() for
the spectral density needed by ggs_geweke() failed. It has been solved
- When the range of a parameter is huge the calculation of the bins is
approximate, and gave different number of bins. Now ggs_histogram takes care of
different number of bins by each variable due to rounding.
Version 0.5
------------------------------
MAJOR CHANGES
- ggmcmc() now allows the argument "plot" to select only some plots.
- ggs_Rhat() now has scaling between 1 and 1.5 by default, so that perspective
on real convergence is present.
- ggs_geweke() includes anargument shadow_limit to highlight the -2+2 range
- rugs in ggs_density() and ggs_compare_partial() are FALSE by default: rug it
is only a visual improvement that really does not add too much information to
the plot, but it takes a lot of resources.
MINOR CHANGES
- Old arguments "param.page" from ggmcmc() and "fully.bayesian" from
ggs_rocplot() now use underscore to be systematic with the rest of the package.
- Change default label for ggs_histogram().
- Rhat label now has a real hat.
Version 0.4.2
------------------------------
MINOR CHANGES
- Bug in ggmcmc() related to ggs_histogram() and the division in pages relying
on nParameters.
Version 0.4.1
------------------------------
MINOR CHANGES
- Bug in export on ggs_rocplot.
- Stan samples are imported without relying on coda.
Version 0.4
------------------------------
MAJOR CHANGES
- Add the possibility to use different parameter names than the default ones
provided by the MCMC software, using the argument "par_labels" in ggs().
- Move from reshape() to reshape2().
- geom_histogram() allows to specify a desired number of bins.
- ROC plot (ggs_rocplot()) with code originally from Zachary M. Jones.
- Separation plot (ggs_separation()) with code originally from Zachary M. Jones.
- New plots ggs_ppmean() and ggs_ppsd() for posterior predictive checks.
- Make parallel=FALSE the default in ggs().
- Consistency in names of the arguments achieved by moving all "." into "_".
MINOR CHANGES
- rstan is no more a suggested package, because it is not in CRAN, and it is
not expected to be there in the near future.
- Fix bugs in ggs_crosscorrelation when the number of parameters was 1 or 2.
- In ggmcmc, do not plot Potential Scale Reduction Factor when there is a
single chain.
- Allow NULL as filename for the pdf file in ggmcmc.
- argument "labels" in ggs_caterpillar() is renamed to "model_labols" avoid
confusion with the parameter names
- Adjust the defaults for the caterpillar plots, to remark the thick and thin
segments
Version 0.3
------------------------------
MAJOR CHANGES
- ggs() can import MCMCpack objects, stanfit objects and csv files from Stan
running from the command line, in addition to the previous import of samples
from mcmc.list() objects (from the coda package).
- New functions ggs_Rhat() and ggs_geweke() that show graphically the results
of the Rhat (potential scale reduction factor, by Gelman & Rubin), and the
Geweke diagnostic (z-score).
- ggs_caterpillar is able to plot against a continuous variable due to the
addition of the 'X' argument
- ggs_caterpillar() has the ability to plot two models, so that model
comparison is easier. Thanks to Zachary M. Jones.
- New parameter "family" that allows to select only certain parameters from
the ggs object based on a regular expression (i.e., select all "beta"
parameters, or all "theta", ones, or "alpha\\[1,.\\]", etc...)
MINOR CHANGES
- Traceplot now shows by default the burnin period and takes care of the
thinning, used by ggs_traceplot() and ggs_running().
- New argument to have absolute colour scale in crosscorrelations.
- ggs_caterpillar() is horizontal by default.
- Documentation has been improved.
Version 0.2
------------------------------
MINOR CHANGES
- opts() is replaced by theme() as it is deprecated in ggplot-0.9.2.
- theme_text() is replaced by element_text() as it is deprecated in
ggplot-0.9.2.
- opts(title="") is replaced by labs(title="") as it is deprecated in
ggplot-0.9.2.
Version 0.1
------------------------------
Initial version