Set the default number of discretizations in gdverse to range from 3 to 8 (#15).
Optimize the Python integration setup in gdverse (#14).
Now opgd() returns optimal discretization parameters
(#13).
Force data to tibble format in
gdverse GDMs model function (#12).
Align the RID model and algorithm with the original framework presented in paper (#9).
Beautify the narrative and other writing details in the vignettes, without making any changes at the user level.
Clear the WORDLIST to ensure the source code remains
clean and organized.
Migrate the source code from ausgis/gdverse to
stscl/gdverse on GitHub.
The general variable discretization in gdverse
now utilizes sdsfun::discretize_vector() (#6).
Algorithm functions are migrated to sdsfun
(#8).
Update the RGD Model API Settings (#2).
Fix bug caused by changes in default parameters of
opgd in sesu_opgd (#4).
Maintain the same results for st_unidisc and
ClassInt::classify_intervals (#5).
The parameter overlaymethod in rid and
idsa has been renamed to overlay.
Add readr as a dependence of type
Suggests.
Recompile vignettes due to internal function changes.
When the discvar input for the opgd,
rgd, rid, spade functions is
NULL, it is assumed that all independent variables in the
formula need to be discretized.
Updating the S3 method for plotting various factor detectors to better conform to academic publication requirements.
Using new example data in the vignettes for spade
and idsa.
Adding the esp function to the package.
Unify all vignettes filenames to lowercase.
Support for using the sf object as input in all
models.