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Changelog

Source: NEWS.md

midr 0.6.0.900 (development version)

midr 0.6.0

CRAN release: 2026-03-08

  • Implemented collection objects such as “midlist” and “midrib”, along with manipulation functions like midlist(), and corresponding visualization functions. This enables the comparison of multiple interpretation models, as well as the interpretation of survival models and multi-class classification models.
  • Renamed long class names to shorter abbreviations (“mid.importance” to “midimp”, “mid.breakdown” to “midbrk”, and “mid.conditional” to “midcon”).

midr 0.5.3.904

  • Fixed memory issues in get.link() and added support for parametric link functions (including Box-Cox, Yeo-Johnson, Robit, and Scobit).

midr 0.5.3.903

  • Multivariate Response Support: interpret() now accepts a matrix for the response variable y, allowing for simultaneous modeling of multiple responses.
  • Introduction of “midlist”: Implemented the “midlist” class to handle multiple MID models efficiently. Methods such as predict() and mid.effect() have been vectorized to handle “midlist” objects using optimized matrix operations.
  • Performance Optimization: Significant reduction in compilation time and binary size by removing the heavy dependency on RcppEigen headers for the core Laplacian smoothing algorithm.

midr 0.5.3.902

  • The digits argument of interpret() now defaults to NULL.
  • get.yhat() has new method for workflow objects.

midr 0.5.3.901

  • Updated interpret() and encoder generators to enhance the variable encoding functionality, introducing new arguments: split for numeric variables and lump for factor variables.
  • The catchall and encoding.digits arguments are renamed to others and digits; the use.catchall argument is deprecated.

midr 0.5.3.900

  • Fixed incorrect calculation in interpret() when singular.ok = TRUE (or, more directly, weighted.norm = TRUE) and lambda > 0.
  • Modified weights calculation to enhance the numerical stability.

midr 0.5.3

CRAN release: 2026-01-16

Fourth release on CRAN. This version introduces significant memory efficiency improvements for large-scale data analysis.

Major Improvements

  • Optimized fitting process: Enhanced the space (and time) complexity when constructing large design matrices, especially for datasets with many observations.
  • Memory-efficient estimation: Introduced the save.memory option in interpret().
  • Improved design matrix constraints: Replaced the max.ncol argument with max.nelements in interpret() to provide more intuitive control over the memory consumption of the design matrix.

Other Enhancements

  • Memory-free prediction engine: Re-implemented the prediction logic to avoid storing massive term effect matrices, significantly reducing the memory usage of interpret() and predict.mid().
  • Flexible formatting: plot.mid.breakdown() and ggmid.mid.breakdown() now support format.args and enhanced label.format for better visualization control.
  • CRAN compatibility: Updated various internal functions to ensure consistent behavior with the ‘stats’ package (especially with stats::terms()) and improved documentation clarity.

midr 0.5.2.907

  • Updated interpret() to improve space and time complexity of constructing the design matrix.

midr 0.5.2.906

  • The format argument in mid.breakdown() is deprecated.
  • plot.mid.breakdown() and ggmid.mid.breakdown() now have a new argument format.args, which is passed to base::format() to format the predictor values stored in “mid.breakdown” objects.
  • The format argument in plot.mid.breakdown() and ggmid.mid.breakdown() is renamed to label.format. The formatting strings now support more flexible formats, such as “%t=%v, %t=%v” for interactions.

midr 0.5.2.905

  • Updated ggmid.mid.importance() and plot.mid.importance() to modify appearance of the plots when color themes are applied.

midr 0.5.2.904

  • Memory-efficient interpret(): The model object no longer stores the massive fitted.matrix (the term-wise decomposition of the fitted values).
  • Optimized predict() engine: Re-implemented the prediction logic using a matrix-free approach.
  • On-demand Decomposition: Functions like mid.importance() now perform term-wise decomposition on-the-fly using the new optimized prediction engine.
  • mid.importance() introduced a new argument max.nkeeps (default: 10,000). While importance scores are calculated using the full dataset for maximum accuracy, the function now optionally retains only a weighted random sample of the term-wise predictions.
  • Standardized predict outputs: For type = "terms", the intercept is now stored in the constant attribute of the returned matrix, aligning with standard R conventions (e.g., predict.lm).
  • Removed the redundant fitted.matrix reference in interpret.default to prevent memory leaks during the estimation process.

midr 0.5.2.903

midr 0.5.2.902

midr 0.5.2.901

  • Enhanced plotting functions for “mid.importance” objects, allowing users to restrict which terms are displayed.
  • Fixed a bug that occurred when a link function is used with a response variable containing NA values.

midr 0.5.2.900

  • Updated functions to enhance consistency with the ‘stats’ package, especially with regard to the return value for the stats::terms() function.

midr 0.5.2

CRAN release: 2025-09-07

Third release on CRAN.

midr 0.5.1.901

  • Corrected typos and improved clarity in the documentation.

midr 0.5.1.900

  • Changed some argument names for consistency (max.nterms, max.nplots, max.nrow).

midr 0.5.1

CRAN release: 2025-08-27

Second release on CRAN.

midr 0.5.0.909

  • Vectorized the design matrix encoding process in factor.encoder() and numeric.encoder() for improved performance.
  • Unified weighted.rmse() and its related functions into a single, more versatile weighted.loss() function.
  • Updated and improved various help documents for clarity and completeness.
  • Changed some argument names for consistency (e.g., changed max.bars to max.terms, max.nrow to max.rows, etc.).
  • Deprecated weighted() and its family functions.
  • Deprecated mid.extract() and mid.frames().

midr 0.5.0.908

  • Rewrote color.theme() to significantly enhance its functionality and flexibility.

midr 0.5.0.906

midr 0.5.0.905

  • Rewrote interpret.formula() and factor.encoder() to correctly support subset and drop.unused.levels arguments.
  • Updated get.yhat() methods to ensure prediction outputs always have the same length as the number of input observations.

midr 0.5.0.904

  • Improved the computational efficiency of the parameter interpolation step in interpret.default().

midr 0.5.0.903

  • Fixed a bug in interpret.default() that caused inconsistency between “fitted.values” and “residuals”.
  • Fixed an issue in mid.f() (mid.effect()) to correctly handle vector recycling when an input’s length is 1.
  • Fixed autoplot.mid.conditional() to avoid redundant evaluation of the “mid” object.
  • Updated the automatic determination method for the number of knots (k) in interpret().
  • Added support for type suffixes in color.theme() for easier theme specification.

midr 0.5.0.902

  • Rewrote interpret.formula() to resolve environment issues related to stats::model.frame()
  • Added new tests to cover this improved functionality.
  • Refined the default diverging color theme “midr”.

midr 0.5.0.901

  • Corrected the “HCL” color theme to be properly categorized as a “qualitative” theme.
  • Changed the rounding method used in numeric “encoder” objects.
  • Updated the article and examples related to color.theme().

midr 0.5.0.900

  • Default color themes can now be set globally via R options: midr.diverging, midr.qualitative and midr.sequential.
  • Added the “HCL” color theme and set it as the new default for “qualitative” theme.
  • Fixed an issue in interpret.formula() to ensure the evaluated formula is correctly stored in the function call.

midr 0.5.0

CRAN release: 2025-06-23

First release on CRAN.

midr 0.4.9.909

  • The accompanying working paper is now available on arXiv.