Skip to contents

Changelog

Source: NEWS.md

figsr (development version)

New features

  • ggfigs() draws a fitted model with ‘ggplot2’, in five views: "tree" (node-link diagram of every tree), "rules" (every tree as a horizontal rule list, with + between trees), "waterfall" (where the prediction for one observation comes from), "cuts" (the numeric cutpoints drawn over the data) and "profile" (exact partial dependence). It returns a ggplot; with interactive = TRUE it returns a ‘ggiraph’ widget whose tooltips show the rule behind each node, leaf or cut. ‘ggiraph’ is only suggested. autoplot() gives the same ggplots.

Bug fixes

  • plot() no longer draws boxes on top of each other. Nodes used to sit on a fixed grid, so in deeper trees neighboring leaves and branch conditions overlapped and their labels were cut off. Each split now places its subtrees from the width of their labels, every tree gets a panel width proportional to the room it needs, and all trees share one vertical scale, so their roots line up. A drawing too large for its panel is scaled down, text included, instead of clipped.

Performance

  • The split search was rewritten and fitting is about 25 to 60 times faster (for example, 12.6 s to 0.33 s with 32,000 rows, 10 numeric predictors and max_splits = 10), allocating about 30 times less memory. Everything that depends on the predictors alone (row order, cutpoints, level groups) is computed once per node instead of once per iteration, every cutpoint of a predictor is scored from a single cumulative sum of the residuals instead of one pass per cutpoint, and a leaf whose residuals did not change since the last iteration keeps its previous best split. The fitted model is unchanged: the same splits, cutpoints, missing-value directions and sample indices as 0.2.0, which a new regression test checks on fourteen data sets.

figsr 0.2.0

Documentation

  • A new vignette, vignette("missing-values"), works through na_method = "mia" on the real Pima Indians Diabetes Database, where missingness is informative but modest, and shows plot()’s new main, tree_names and sign-colored leaves on it.

New data

  • pima_diabetes: the Pima Indians Diabetes Database (Smith et al. 1988), 768 patients, ships lazily loaded like iris. Its five physiologically impossible zeros (Glucose, BloodPressure, SkinThickness, Insulin, BMI) are already recoded to NA, so it is ready to demonstrate na_method = "mia" without an internet connection or preprocessing.

New features

  • plot() gained main, an overall title drawn once above every tree panel, and tree_names, overriding the automatic per-tree panel titles (a single string applies to every tree; a vector is indexed by each tree’s position in the fit, so it stays correct when tree_idx only plots some of them; NA in a position keeps that tree’s default title). In the "scientific" and "modern" styles, a leaf’s background, border and text now depend on the sign of its contribution – green for positive, red for negative; "classic" stays monochromatic by design.

  • figs(), fit_figs() and bagging_figs() gained na_method. The default "omit" is the previous behavior. "mia" (missingness incorporated in attributes, Twala et al. 2008) keeps every row: at each split the search also tries sending missing values left or right of the cutpoint, and splitting on missingness itself, and uses the learned direction at prediction time. summary() prints these as IS MISSING clauses, plot() labels the edge missing values take, and figsr_importance() reports a split on missingness on its own missing(<predictor>) row.

Bug fixes

  • The parsnip engine now fits through the formula interface. The previous data.frame interface ran model.frame() with na.omit before the engine, so fit() silently dropped rows with a missing predictor.

figsr 0.1.1

CRAN release: 2026-08-31

Bug fixes

  • figs() now carries the parent leaf’s value into the two leaves created by splitting it. Predictions from any tree deeper than one split were wrong before this fix.

  • Classification probabilities are no longer passed through a logistic function. The engine fits squared error on the 0/1 encoding of the outcome, so the sum of the leaf values already estimates the probability of the second level; it is now clamped to the unit interval instead. fitted_values is clamped the same way, so it agrees with predict().

  • predict() now rebuilds the model frame from the fitted terms, so a formula with a transformed term such as y ~ log(x) can be predicted from. It previously failed with a missing-predictor error.

  • A matrix-valued term such as poly(x, 2) raised a cryptic “missing value where TRUE/FALSE needed” during fitting; it is now rejected with a clear message.

  • figs() gained subset and na.action arguments, which were previously swallowed by ... and silently ignored. Passing weights now raises an error rather than fitting an unweighted model in silence.

  • A model that accepts no split at all predicts the outcome mean instead of zero. In classification this was a probability of zero for every observation.

  • Integer predictors with large values no longer overflow when the candidate cutpoints are computed.

  • Numeric cutpoints above the 30-candidate cap are now quantiles of the sample rather than of its distinct values, so they follow the density of the data.

  • Factor predictors are judged by the levels present in a node rather than by the declared level set, so a factor carrying many unused levels is no longer skipped as if it had too many levels.

  • A missing value in a predictor at prediction time raises a clear error, and a factor level unseen in training is rejected the way other stats model functions reject it, instead of failing deep inside the tree traversal.

  • predict() rejects an unsupported type instead of quietly returning numeric predictions.

  • bagging_figs() records the mode that was actually fitted. A factor outcome with the default mode = "regression" produced an ensemble that returned numeric scores instead of class predictions. Bagged classification also averages member probabilities rather than hard labels, and supports type = "prob". A character outcome is converted to a factor once, so a bootstrap resample that misses the minority class no longer aborts the fit.

  • fit_figs() no longer drops a predictor genuinely named .outcome.

  • plot() validates style and tree_idx instead of rendering the wrong style silently or failing with an internal error on NA. The "classic" style labels leaves dy =, like the other styles, because a leaf holds one tree’s contribution rather than the prediction.

  • Registering the parsnip model is now idempotent, so reloading the package in a live session no longer errors.

New features

  • figs_tree() gained an engine argument defaulting to "figsr", and an update() method, so specifications behave like the rest of parsnip.

  • print() reports which predictors actually carry a split, and summary() states that classification leaf values are contributions to the probability of the second class.

Documentation

  • The Description field no longer quotes acronyms, following the request from CRAN.
  • Help pages are rendered from markdown, so inline code and cross-references appear as such.
  • The introductory vignette covers prediction, importance, plotting, classification, parsnip integration and bagging, and its example data no longer draws the outcome independently of the predictors.

figsr 0.1.0

CRAN release: 2026-08-31

  • First release.

  • figs() fits Fast Interpretable Greedy-Tree Sums for regression and two-class classification, growing a sum of shallow trees by greedily taking the split that most reduces residual impurity, whether that split opens a new tree or deepens an existing one.

  • figs_tree() registers the model with parsnip, so FIGS can be used inside tidymodels workflows. max_splits() and max_trees() provide dials parameter objects for tuning; min_n reuses dials::min_n().

  • summary() prints the tree sum as IF-THEN decision rules and plot() draws the trees, in a "scientific", "modern" or "classic" style.

  • figsr_importance() ranks predictors by the total residual sum-of-squares reduction attributable to each of them.

  • bagging_figs() fits a bootstrap ensemble of FIGS models.