mildsvm 0.4.1
- Fix documentation to address CRAN NOTEs
 
- Minor updates to functions, snapshot tests, and Github actions to
accomodate newer versions of other packages (in particular, dplyr,
tibble, testthat)
 
v0.4.0
Add ordinal methods to the
package
- Add 
omisvm() for ordinal multiple instance support
vector machine 
- Add 
mior() for multiple instance ordinal
regression 
- Add 
misvm_orova() for MI-SVM reducing ordinal to binary
one-vs-all classification 
- Add 
svor_exc() for support vector ordinal regression
with explicit constraints 
Other changes
- Breaking: change 
generate_mild_df() to a new
interface 
- Breaking: change 
mildsvm() to mismm() 
- Breaking: fix S3 method issue, affects 
mi_df and
mild_df methods parameter 
- Add 
mi_df() class and methods, including
as_mi_df() 
- Add method for 
mi_df objects for misvm(),
cv_misvm() and all new ordinal methods 
- Add 
ordmvnorm data for examples 
- Add print methods for 
kfm_exact,
kfm_nystrom, mild_df, mior,
misvm, mismm, misvm_orova,
omisvm, smm, svor_exc 
- Package now depends on R > 3.5.0, new imports of pillar,
utils
 
- fix warning when 
misvm() has matrix passed 
- fix 
.reorder() ambiguity 
- pass lintr checks
 
- re-work internals for easier testing
 
v0.3.1
- Fix bug where NaN columns passed to mildsvm() would fail
 
- Fix bug where columns with identical values passed to mildsvm()
would fail
 
v0.3.0
- Add new method to mildsvm(): method = ‘qp-heuristic’. This works
similar to the method of the same name in misvm(), but uses the SMM
kernel from kme() in the underlying calculations.
 
- Fix bug in classify_bags() when using factors
 
v0.2.0
- The main modeling functions (misvm(), mildsvm(), and smm()) now have
three methods:
- Formula method (i.e. misvm(mi(y, bags) ~ x1 + x2, data = df,
…))
 
- Data-frame method (i.e. misvm(x, y, bags, …))
 
- Method for the mild_df class (I.e. misvm(mil_data, …)). This method
often performs non-trivial aggregation or transformation since misvm()
and smm() work naturally on MIL data and supervised data,
respectively.
 
 
- Prediction on main modeling functions always returns a tibble with a
single column depending on the type argument
 
- Kernel feature maps functions are now organized as kfm_nystrom(),
kfm_exact() with a build_fm() method.
 
- Update MilData class to mild_df class, and improve the class methods
and constructors.
 
- Many internal methods removed and restructured.
 
v0.1.0
- Initial release. This release has several known bugs and an early
input/output scheme that has since been revised. This represents a
mostly working starting point.