NEWS

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mildsvm 0.4.1.9000

mildsvm 0.4.1 (2025-08-19)

• 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)

mildsvm 0.4.0 (2022-07-14)

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

mildsvm 0.3.1

• Fix bug where NaN columns passed to mildsvm() would fail
• Fix bug where columns with identical values passed to mildsvm() would fail

mildsvm 0.3.0

• 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

mildsvm 0.2.0

• 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.

mildsvm 0.1.0

• 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.