(development version)

v0.4.0

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

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

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

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.

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.