ggmid() for better compatibility with ggplot2 ecosystem.get.yhat() for models created with packages including "model_fit", "workflow" and "rpf".theme_midr() now accepts optional arguments ... for new parameters introduced from ggplot2 4.0.0; ink, paper and accent.get.yhat() for models created with packages including aorsf, flexsurv and mboost.interpret.default() to support formulas with dot (.) notation in the terms argument.interpret() to directly use the default solver (RcppEigen::fastLmPure) when an integer is provided for the method argument.method are no longer converted to lowercase and do not undergo partial matching with default method names.interpret.formula() to prevent double evaluation of arguments under NSE.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.get.link() and added support for parametric link functions (including Box-Cox, Yeo-Johnson, Robit, and Scobit).interpret() now accepts a matrix for the response variable y, allowing for simultaneous modeling of multiple responses.predict() and mid.effect() have been vectorized to handle "midlist" objects using optimized matrix operations.digits argument of interpret() now defaults to NULL.get.yhat() has new method for workflow objects.interpret() and encoder generators to enhance the variable encoding functionality, introducing new arguments: split for numeric variables and lump for factor variables.catchall and encoding.digits arguments are renamed to others and digits; the use.catchall argument is deprecated.interpret() when singular.ok = TRUE (or, more directly, weighted.norm = TRUE) and lambda > 0.Fourth release on CRAN. This version introduces significant memory efficiency improvements for large-scale data analysis.
save.memory option in interpret().max.ncol argument with max.nelements in interpret() to provide more intuitive control over the memory consumption of the design matrix.interpret() and predict.mid().plot.mid.breakdown() and ggmid.mid.breakdown() now support format.args and enhanced label.format for better visualization control.stats::terms()) and improved documentation clarity.interpret() to improve space and time complexity of constructing the design matrix.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.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.ggmid.mid.importance() and plot.mid.importance() to modify appearance of the plots when color themes are applied.interpret(): The model object no longer stores the massive fitted.matrix (the term-wise decomposition of the fitted values).predict() engine: Re-implemented the prediction logic using a matrix-free approach.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.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).fitted.matrix reference in interpret.default to prevent memory leaks during the estimation process.interpret.formula() now supports unevaluated column names for the weights argument.weighted.loss() supports the R-squared metrics by passing method = "r2".NA values.stats::terms() function.Third release on CRAN.
max.nterms, max.nplots, max.nrow).Second release on CRAN.
factor.encoder() and numeric.encoder() for improved performance.weighted.rmse() and its related functions into a single, more versatile weighted.loss() function.max.bars to max.terms, max.nrow to max.rows, etc.).weighted() and its family functions.mid.extract() and mid.frames().color.theme() to significantly enhance its functionality and flexibility.numeric.encoder() and factor.encoder() held an unnecessary reference to the execution environment of interpret.default().interpret.formula() and factor.encoder() to correctly support subset and drop.unused.levels arguments.get.yhat() methods to ensure prediction outputs always have the same length as the number of input observations.interpret.default().interpret.default() that caused inconsistency between "fitted.values" and "residuals".mid.f() (mid.effect()) to correctly handle vector recycling when an input's length is 1.autoplot.mid.conditional() to avoid redundant evaluation of the "mid" object.k) in interpret().color.theme() for easier theme specification.interpret.formula() to resolve environment issues related to stats::model.frame()color.theme().midr.diverging, midr.qualitative and midr.sequential.interpret.formula() to ensure the evaluated formula is correctly stored in the function call.First release on CRAN.