midr 0.5.3

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

• interpret.formula() now supports unevaluated column names for the weights argument.

midr 0.5.2.902

• weighted.loss() supports the R-squared metrics by passing method = "r2".

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

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

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

• Fixed a memory leak issue where numeric.encoder() and factor.encoder() held an unnecessary reference to the execution environment of interpret.default().
• Updated the hex logo and favicons.

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

First release on CRAN.

midr 0.4.9.909

• The accompanying working paper is now available on arXiv.