Package: ggRandomForests Version: 3.5.0

ggRandomForests v3.5.0

• plot.gg_varpro() no longer draws a phantom "NA" category when nvar is smaller than the number of variables the fit reports. $imp/$stats are truncated to nvar, but the per-tree overlay ($imp.tree) and the class-conditional data ($conditional) still carry every variable; re-levelling those to the truncated $imp levels orphaned the extras to NA, which rendered as an empty box/bar. Those rows are now dropped, so only the displayed variables appear.
• The vignettes now render their figures with ragg and quantise them to a 256-color palette, cutting the source tarball from 4.7 MB to 2.3 MB. The vignettes had never chosen a graphics device, so they fell through to the default png(), which writes RGBA truecolor: an alpha channel these opaque plots never use, over tens of thousands of anti-aliased colors that PNG cannot compress. Figures are visually unchanged (mean pixel difference 1.55 on a 0-255 scale). Both steps are build-time only and degrade to no-ops when ragg or magick is absent, so a vignette rebuild without them still succeeds -- at the old file size.
• The varPro vignette now documents which variables a varpro fit actually makes available. A fit narrows the predictors twice -- object$xvar.names holds what varPro::partialpro() can reach, varPro::get.topvars() only the reported ranking -- and partialpro() silently drops any requested name outside the first set. The new section covers naming xvar.names to get past the reported ranking, split.weight = FALSE to widen the candidate set itself, and the two arguments (nvar, sparse) that look like they should help and don't.
• gg_partial_varpro() now warns when a name passed in xvar.names is one the varpro fit cannot reach, instead of letting it disappear. partialpro() keeps only the names it finds in object$xvar.names and says nothing about the rest, so a request for twelve variables could come back with ten. The check runs before partialpro() does, so the warning arrives ahead of the computation rather than after it; it names every dropped variable and points at split.weight = FALSE. Supplying part_dta yourself is unchanged -- the variables are already gone by then. The function's examples now cover the object-driven path, which had none.
• gg_partial_varpro(scale = "chf") now computes the variables you name in xvar.names instead of every variable the fit can reach. The chf path routes through gg_partial_rfsrc() rather than partialpro(), and it had never been given the variable list -- so asking for one variable quietly did the work for all fourteen. This is the mirror image of the partialpro() bug above: that one returns fewer variables than you asked for, this one returned all of them. Naming a variable the forest does not carry has always been an error and still is. The partialpro-only arguments (cut, nsmp) mean nothing on this path and are now ignored with a warning rather than in silence.
• Fix: gg_vimp() on a randomForest fit grown with importance = TRUE now reports the permutation importance you asked for. It was reporting node purity instead, and silently: randomForest stores %IncMSE and IncNodePurity side by side, and gg_vimp() stacked both into one vimp column and ranked them together. The two are not commensurable -- node purity runs in the thousands where %IncMSE runs in the tens -- so every impurity row outranked every permutation row, and the truncation to nvar cut the permutation values away entirely. On randomForest(medv ~ ., Boston, importance = TRUE) the plot showed lstat = 12576.7 (node purity) where the permutation value is lstat = 62.4. Node purity is now left out of the ranking; read randomForest::importance(object) if you want both. Fits grown without importance = TRUE are unaffected -- they only ever stored node purity, and that is still what you get.
• Fix: gg_vimp() on a randomForest classification fit grown with importance = TRUE now reports permutation importance as well. That matrix mixes the same two scales -- a permutation column per class plus MeanDecreaseAccuracy, alongside MeanDecreaseGini -- but it is wider than the single-outcome branch that picks one measure, so it skipped that branch and every column was ranked together. MeanDecreaseGini came out the sole survivor: on randomForest(Species ~ ., iris, importance = TRUE), gg_vimp() returned 4 rows of node purity where 16 rows of permutation importance were there to report. The per-class columns and MeanDecreaseAccuracy are all permutation measures on one scale, so they are now kept together and named in the set column, the way an rfsrc fit's all/<class> columns already were; only MeanDecreaseGini is dropped.
• Fix: which.outcome now selects the column you asked for on a randomForest classification fit. which.outcome = 0 documented itself as overall importance and took column 1 to get it, and which.outcome = k took column k + 1 for class k. Both are right for an rfsrc fit, whose $importance leads with an all column, and neither is right here: a randomForest matrix opens on the classes and keeps the overall permutation measure in MeanDecreaseAccuracy, near the end. So 0 returned the first class labeled as overall -- on randomForest(Species ~ ., iris, importance = TRUE) it handed back setosa's values, ranking Petal.Width above Petal.Length where the overall measure has them the other way round -- and every class index was shifted by one, 1 giving versicolor. The columns are now resolved by name: 0 reaches MeanDecreaseAccuracy, k reaches class k, and which.outcome = 1 agrees with which.outcome = "setosa". Fits grown with importance = FALSE keep no MeanDecreaseAccuracy column and their single measure answers to 0 as before.
• which.outcome now names the measure it selected in the set column, for both rfsrc and randomForest fits. Asking for one measure reported set as the literal "vimp" -- the pivot takes set from the source column name, and the selected column was named after the vimp column it was about to be written into rather than after the measure it held. So the one path where you have to say which measure you want was the one path that would not tell you which measure you got. gg_vimp(rfsrc_iris, which.outcome = 0) now reports set == "all", gg_vimp(rf_iris, which.outcome = 0) reports set == "MeanDecreaseAccuracy", and both agree with the names the unfiltered pivot has always used. Values and ordering are unchanged, and plots are unaffected: plot.gg_vimp() only facets on set when there is more than one of them, and selecting a measure leaves exactly one.
• nvar counts variables again for randomForest fits, not rows. It was applied after the multiclass pivot, where a frame holds one row per variable per measure, so it lopped whole measures off the end of the ranking instead of trimming the ranking itself.
• gg_vimp() now says in ?gg_vimp that a randomForest fit without importance = TRUE stores only IncNodePurity, so the ranking is node purity rather than permutation VIMP, and nothing in the plot marks the difference. The example now passes importance = TRUE.
• gg_error() now explains that the error trajectory is randomForestSRC's to record, not ours: rfsrc()'s block.size defaults to NULL unless you request importance, which stores the error at the final tree only, so a default fit gives gg_error() a single point rather than a curve -- tree.err = TRUE alone does not change that. Grow with block.size = 1 for an error at every tree. The examples do this now; they had all been plotting one dot.
• gg_beta_varpro(): the imp column is documented as the absolute coefficient. varPro::beta.varpro() wraps every coefficient it returns in abs(), so the sign is discarded upstream and never reaches us -- the docs had said "Sign is real (direction of local association)", which cannot be read off this output. Use gg_ivarpro() for a signed local estimator.
• gg_isopro(): the "What's in the output" section now says the polarity flip is ours. varPro::isopro()'s howbad is lower = more anomalous; we return 1 - howbad so that higher = more anomalous. The section had credited that to the fit, contradicting this function's own @return.
• Added gg_shap() and plot.gg_shap() (with shap_importance(), shap_beeswarm(), shap_dependence()) for SHAP explanations of regression and classification forests, wrapping kernelshap (Suggests).
• gg_shap() now enforces the integer contract on bg_n and which.class instead of silently coercing them. Both are documented as integers, but were only loosely checked: bg_n = 1.9 was truncated to 1 and bg_n = Inf (or any value above .Machine$integer.max) became NA, while which.class = 2.9 passed the range check and then indexed column 2 -- returning SHAP values for a class the caller never asked for. Non-whole, non-finite, out-of-range and non-scalar values now raise a clear error. Valid input is unaffected.
• Added print.gg_shap() and summary.gg_shap(). gg_shap was the only gg_* class without them, so it dumped every row at the REPL instead of showing a header. print() now gives the standard one-line header (with the variable and observation counts) and summary() returns a summary.gg object reporting the baseline, background-sample size, the explained class for classification fits, and the top variables by mean |SHAP|.
• The package help page (?ggRandomForests) now describes the whole current surface -- the SHAP, Brier, varPro and unsupervised-varPro families were missing -- and no longer claims that plot() methods may return a list of ggplot2 objects; each returns a single plottable object (a ggplot, or a patchwork composite for the multi-panel methods).
• gg_partial() no longer lets survival partial dependence be mistaken for a probability. randomForestSRC::plot.variable() defaults to surv.type = "mort", so yhat is mortality -- an expected event count, not a value on [0, 1] -- and it only superficially resembles a percentage. yhat is passed through unscaled (rescaling it would corrupt the quantity); instead the label describing what was plotted is carried on the object as attr(x, "ylabel") and used as the y-axis title by plot.gg_partial(). Note that gg_partial_rfsrc() defaults to partial.type = "surv" and so does report survival probabilities: the two entry points report different quantities by default. (#15)

ggRandomForests v3.4.1

• The remaining rfsrc/randomForest wrappers -- gg_error(), gg_vimp(), gg_variable(), gg_rfsrc(), and gg_brier() -- now have default S3 methods, so a wrong-class input gives a clear "expected an 'rfsrc' or 'randomForest' object" error (naming the class it got) instead of R's generic "no applicable method". This finishes the dispatch-consistency pass started for the varPro family in 3.4.0. (gg_roc() keeps its existing gg_roc.rfsrc default, which accepts rfsrc-shaped objects.)

ggRandomForests v3.4.0

• gg_isopro(), gg_beta_varpro(), and gg_ivarpro() now have default S3 methods, so a wrong-class input gives a clear "expected a '' object" error (naming the class it got) instead of R's generic "no applicable method". This makes the varPro-family wrappers consistent with gg_beta_uvarpro() / gg_sdependent(); the previously-unreachable inner class checks were removed.
• Fix: gg_partial_rfsrc() now computes partial dependence correctly for factor predictors. It was passing factor labels as partial.values to randomForestSRC::partial.rfsrc(), which imposes a level by its integer code (internally as.numeric(partial.values)). Character labels ("No"/"Yes") became NA and numeric-looking labels ("4"/"6"/"8") became out-of-range codes, so every level collapsed to a single value (a flat categorical partial plot). The wrapper now passes the integer codes and relabels the output, matching plot.variable(partial = TRUE) and the ground-truth partial dependence. The categorical x is now returned as a factor in the model's level order, so the plot keeps that order instead of re-sorting alphabetically. Continuous and numeric low-cardinality predictors are unaffected.
• gg_beta_uvarpro() / plot.gg_beta_uvarpro(): tidy wrapper and bar chart for varPro::get.beta.entropy() -- the unsupervised analogue of gg_beta_varpro(). From a uvarpro() fit it aggregates the per-region lasso coefficients into beta_mean = colMeans(|beta|) per variable (most-important first), flags variables above a selection cutoff, and accepts a precomputed beta_fit matrix. print/summary/autoplot companions follow the gg_* conventions.
• gg_sdependent() / plot.gg_sdependent(): tidy wrapper and ranked lollipop for varPro::sdependent() signal-variable detection. Returns one row per candidate variable (imp_score, graph degree, signal flag) ranked by imp_score. Complements gg_udependent() (the dependency graph) with the "which variables are signal" ranking; shares the beta_fit entropy matrix. Follows the get.beta.entropy + sdependent workflow from the varPro::uvarpro() help (iowa-housing example).
• New uvarpro vignette: a short, focused walk-through of the unsupervised varPro wrappers (gg_udependent(), gg_beta_uvarpro(), gg_sdependent()) on a single uvarpro() fit, using the shared beta_fit matrix. The three unsupervised sections were lifted out of the varpro vignette, which now points to the new one and covers the five supervised wrappers.
• Fixed the main vignette's \VignetteIndexEntry, which still carried the template placeholder "Vignette's Title" -- it now reads "Exploring Random Forests with ggRandomForests" (the index entry CRAN lists, not the document title, was the stale one).

ggRandomForests v3.3.0

• gg_partial_varpro(): classification partial plots now default to probability. scale = "auto" on a classification fit resolves to "prob" (P(Y = target class)) instead of raw log-odds; "odds" and "logodds" are options. The back-transform is applied before averaging (mean predicted probability). The causal contrast is shown only on "logodds".
• gg_partial_varpro(): survival partial plots now default to survival probability. scale = "auto" on a survival fit resolves to "surv" (S(tau | x), bounded 0-1) via a new partialpro learner, instead of the unbounded ensemble-mortality score (still available via scale = "mortality"). "surv" and "rmst" default tau to the median follow-up time when time is omitted -- a units-safe, data-driven horizon (v3.2.0's rmst required time; this is a loosening). The resolved tau is reported in a message and the axis label.
• plot.gg_partial_varpro(): documents what the causal (virtual-twins) estimator is and when to use it, and explains why it is hidden on the bounded probability scales.
• Documentation: plot.gg_partial_varpro() gains a "Reading an RMST curve" section explaining how to interpret the scale = "rmst" y-axis -- RMST(tau) is the expected event-free time within the first tau time-units (area under S(t) out to tau), read in the model's own time units, bounded by tau, and higher-is-better (the opposite direction from ensemble mortality). It also notes that tau must be supplied in the fit's time units, since a tau beyond the largest event time truncates to the full restricted mean. No code change.

ggRandomForests v3.2.0

• Fix (#118): gg_varpro() no longer fails with the cryptic "arguments imply differing number of rows:

  , 0" when varPro::importance() returns a degenerate importance table (0 rows, or p variables with no usable z column) -- observed intermittently on survival fits where the release-rule step selects no variables. It now stops with a clear, specific message explaining the empty importance and suggesting a larger ntree. The guard is scoped to the degenerate case only; well-formed fits (survival included) are unaffected -- this is not a blanket survival-family block (cf. the reverted #116).

• Fix: gg_partial_varpro(scale = "rmst", time = tau) now drives the survival partial computation instead of only relabeling the y-axis. varPro::partialpro() has no time argument, so its default survival learner returns ensemble mortality at every horizon -- multi-horizon RMST plots built that way differed only by Monte-Carlo noise, not by tau. scale = "rmst" now passes partialpro() an RMST(tau) learner that integrates the survival curve (integral_0^tau S(t) dt) from object$rf, so the curve genuinely depends on tau. This path recomputes from object (a survival fit) with part_dta = NULL; a precomputed part_dta can only be relabeled, and the function now warns when you try. Also warns when tau exceeds the model's event-time range (RMST is truncated there) and when time is passed to a scale that ignores it. Imports now requires varPro (>= 3.1.0) (the version exposing the partialpro() learner argument this path relies on).
• Fix: gg_partial_varpro(scale = "surv"/"chf", model = ...) no longer errors when a variable yields an empty continuous or categorical frame (the survival path-C model-label assignment now guards against a 0-row data.frame).
• gg_partial_varpro() (and the gg_partialpro() alias) now forward ... to varPro::partialpro() on the object-driven path. This restores control over which variables are computed (xvar.names, nvar) and the UVT step (cut, nsmp, ...) for the RMST path, which must recompute from object and so cannot accept a precomputed part_dta. Without an explicit xvar.names, partialpro() falls back to varPro::get.topvars(object), which can return few or no variables for some fits.

ggRandomForests v3.1.2

• CRAN fix: skip only the single test grow that trips the upstream randomForestSRC gcc-UBSAN report at entry.c:184 — the unsupervised isolation forest in gg_isopro (varPro::isopro(method = "unsupv")). Only an unsupervised grow has a 0-length yvar.wt, the vector rfsrcGrow decrements to an out-of-bounds pointer; supervised grows are unaffected. We verified this under -fsanitize=undefined: of every varPro/rfsrc grow in the test suite, only isopro(method = "unsupv") fires entry.c:184. make_iso_fit() therefore calls skip_on_cran() only for method = "unsupv". ggRandomForests is pure R and unchanged.
• The broader skip_on_cran() guards added in v3.1.1 (the varpro, uvarpro, ivarpro, beta.varpro, and isopro(method = "rnd") test fixtures) are removed: those grows are supervised (or synthetic-supervised) and gcc-UBSAN-clean, so they run on CRAN again, restoring that test coverage. The upstream issue is fixed in randomForestSRC and pending a CRAN release.

ggRandomForests v3.1.1

• CRAN fix: the varPro tests now call skip_on_cran() so they do not run on CRAN's check machines, including the gcc-UBSAN additional check. They were triggering an upstream randomForestSRC sanitizer issue (a 0-length array access in rfsrcGrow, entry.c:184) that surfaces when any varPro grow (varpro(), beta.varpro(), uvarpro(), isopro(), ivarpro()) builds a forest. ggRandomForests is pure R and its code is unchanged; the varPro tests still run in our CI (the workflows set NOT_CRAN=true) and locally; they are skipped only on CRAN's check machines, including the gcc-UBSAN check. The upstream issue has been reported to the randomForestSRC maintainers.
• The varpro vignette now loads every varPro fit from a precomputed file (vignettes/varpro_precomputed.rds, built by vignettes/precompute_varpro.R), so the vignette performs no live varPro grow during R CMD check. This removes the same upstream sanitizer path from the vignette build and trims check time. Each chunk falls back to a live fit if the precomputed object is absent, so the vignette remains reproducible from source.

ggRandomForests v3.1.0

• Fix: gg_vimp() for single-outcome rfsrc forests now correctly flags variables with non-positive VIMP in the positive column (affecting plot coloring). The column was named VIMP (uppercase) in single-outcome fits but the flag check accessed $vimp (lowercase), leaving positive stuck at TRUE for all variables. Surfaced by the Copilot review on PR #109.
• Documentation pass. Deepened the varPro-family and rfsrc importance/partial/survival help pages against the upstream randomForestSRC and varPro documentation, and made the line between gg_vimp() (permutation, Breiman-Cutler importance) and gg_varpro() (varPro release-rule importance) explicit and cross-linked. Vignette prose deepened with the same framing; one-line code-comment fixes; fixed a stale @return in gg_roc() (documented a yvar column the function does not return). No user-facing behavior change.
• Vignettes: the regression and survival partial-dependence surfaces are now rendered as static ggplot2 heat maps instead of interactive plotly widgets, and figures render at 96 dpi. This cuts the installed size from ~17 MB to ~5 MB (the plotly library is no longer bundled into the vignette HTML). plotly is dropped from Suggests.
• Check time: reduced the R CMD check vignette-rebuild and test timings to bring the overall CRAN check comfortably under budget (CRAN flagged the overall check time on the 3.1.0 submission). The regression and survival vignettes use lighter forests (ntree 200 / 150, imputation ntree 100) and coarser partial-dependence grids. The varpro vignette's three gg_partial_varpro() calls and the Boston beta.varpro() fit (~34 s combined) are precomputed offline by vignettes/precompute_varpro.R and loaded from vignettes/varpro_precomputed.rds, with an automatic live-computation fallback if the file is absent. The gg_udependent() tests memoise the per-fit entropy matrix (varPro::get.beta.entropy(), ~1.5 s and a pure function of the fit) instead of recomputing it once per test. No user-facing behavior change.

ggRandomForests v3.0.0

• Version jump to 3.0.0. The varPro integration is a major scope expansion plus the gg_partialpro() soft-deprecation, which is major-version territory. Survival / multivariate varPro families, ROC confidence intervals, and hazard estimates are deferred to v3.1.0.
• CRAN-audit cleanup: the gg_brier() / plot.gg_brier() examples move from \dontrun to \donttest (so they execute under R CMD check --as-cran and on CRAN; library(survival) added so Surv() resolves), the per-variable message() in the deprecated surv_partial.rfsrc() is removed (its one behavior change: that function no longer prints a line per variable), and the README points to the new "varpro" vignette.
• Fix: importance plots now consistently put the most-important variable at the top. gg_varpro(), gg_beta_varpro(), and gg_ivarpro() previously built their variable factor with descending levels, so after coord_flip() the most-important variable landed at the bottom — inverted relative to gg_vimp(). All three now reverse the factor levels to match the gg_vimp convention (and the varImpPlot / vip standard). Row order and summary() output are unchanged (still most-important first). A new cross-function test pins the convention.
• New vignette: "Exploring variable importance with varPro." Walks the full gg_* varPro layer (gg_partial_varpro, gg_varpro, gg_udependent, gg_isopro, gg_beta_varpro, gg_ivarpro) on three worked examples — regression (Boston), classification (iris binary + multi-class), and survival (PBC). Includes a family-support matrix documenting which wrapper works for which forest family. Headline document for v3.0.0.
• gg_ivarpro() and plot.gg_ivarpro(): tidy wrapper and per-variable-distribution / per-observation-profile plots for varPro::ivarpro() (individual / local variable importance) across regression and classification (binary + multi-class) families. The long-format tidy frame is (obs, variable, local_imp, selected) for regression; classification adds a class column. NA cells are filtered out and sparsity is surfaced in provenance. which_obs (integer index) collapses to a single-observation profile; the plot switches from a jittered distribution view to a horizontal bar chart. which_class (response level name) collapses to a single class panel; binary fits default to the last factor level (positive class). cutoff accepts NULL (per-class mean), a scalar, or a named numeric vector — matching the gg_beta_varpro classification contract. Optional ivarpro_fit argument lets callers cache the expensive ivarpro() call. Last of four Phase 4 sub-projects.
• gg_beta_varpro() adds varPro classification support (binary + multi-class). Binary fits default to a single positive-class panel (last factor level); multi-class fits return a long-format frame with a class column and plot as facet_wrap(~ class). Optional which_class selects a single class; cutoff accepts a scalar or per-class named vector. Variables are stored as a factor whose levels are set by mean(|sum-of-class-beta|) descending so every facet shows rows in the same order. Motivating use case: 30-day mortality.
• Provenance shape change for gg_beta_varpro(): attr(*, "provenance")$cutoff is now always a named numeric vector — length 1 named "regr" for regression, length K named with the response factor levels for classification. Downstream tooling should read it as a vector and select by name; the prior scalar shape is gone.
• gg_beta_varpro() and plot.gg_beta_varpro(): tidy wrapper and default horizontal bar chart for varPro::beta.varpro() — the per-rule lasso-β refinement of variable importance. Aggregates per-rule β̂ by variable into beta_mean = mean(|β̂|) and flags variables above a selection cutoff (default mean(beta_mean)). Optional beta_fit argument lets callers compute the expensive beta.varpro() step once and reuse the result across multiple wrapper calls (different cutoffs, snapshot rebuilds, vignette knits). print / summary / autoplot S3 companions follow the existing gg_* conventions. Regression family only — classification, regr+, and survival are tracked under Phase 4d (see the spec for the endpoint map). Third of three Phase 4 sub-projects.
• gg_isopro() gains a newdata argument so a fitted varPro::isopro model can score new observations into the same tidy gg_isopro frame. Internally the wrapper calls predict.isopro() twice: with quantiles = FALSE to populate the case.depth column (varPro's native polarity, lower = more anomalous) and with quantiles = TRUE to compute howbad = 1 - quantile (the wrapper convention, higher = more anomalous). Both polarities are visible in the returned data frame, and the relationship is named in the roxygen. The plot / print / summary / autoplot S3 companions work unchanged on the new tidy frame; to overlay training and test scores, bind the two extractor calls with a method label column and pass the result to plot(). Second of three Phase 4 sub-projects.
• Fix (gg_isopro training-path polarity). Bug in the original gg_isopro (PR #94): varPro's $howbad on an isopro fit uses "lower = more anomalous" polarity (it is the quantile of case.depth), but the wrapper's plot method and documentation both assume "higher = more anomalous". Train scores and the new test-data scores were anti-correlated until this PR's training-path flip (howbad = 1 - object$howbad) brought them into agreement. The fix surfaced because the test-data sanity check (training-as-newdata top-5 overlap) failed at 0/5 instead of 5/5 before the flip. Note: the two vdiffr baselines recorded in PR #94 (gg-isopro-default and gg-isopro-threshold) were recorded under the inverted polarity; they are visually flipped relative to the new behavior but CI skips snapshots (VDIFFR_RUN_TESTS = false) so no failure surfaces. Re-record with VDIFFR_RUN_TESTS = true when convenient.
• Documentation: pedagogical pass over the varPro wrappers (gg_partial_varpro, gg_varpro, gg_udependent and their plot.* methods). Each help page now has explicit "What X is doing", "What's in the output", and "What you use this for" sections so a reader new to varPro can learn the underlying method (release rules, beta-entropy dependency, parametric / nonparametric / causal partial estimators) from the help page alone, not just the wrapper mechanics. No API or behavioral change.
• Documentation: enable roxygen2 markdown package-wide via Roxygen: list(markdown = TRUE) in DESCRIPTION. New roxygen blocks can use backticks and [fn()] link syntax; existing \code{} / \link{} markup keeps working. Two source-roxygen edits to keep R CMD check clean: randomForest[SRC] in R/help.R (markdown read it as an unfinished link) becomes plain randomForestSRC; the 95\% escape in R/gg_rfsrc.R::bootstrap_survival becomes a literal 95%. No API or rendered-doc behavioral change beyond the conventions switch.
• New gg_isopro() and plot.gg_isopro(): tidy wrapper and ranked-elbow + density visualization for varPro::isopro isolation-forest anomaly scores. plot.gg_isopro() takes panel = c("both", "elbow", "density") and optional threshold (score-space) or top_n_pct (quantile-space) to draw a reference line; if both are set, threshold wins with a message. A method column auto-triggers color grouping for multi-method comparisons (use dplyr::bind_rows() on three gg_isopro() calls). print / summary / autoplot S3 companions follow the existing gg_* conventions. First of three Phase 4 sub-projects.
• plot.gg_variable(): fix render error on the default multi-class classification plot. The default-xvar selection was treating yvar (the observed-class column) and outcome (the multi-class pivot facet) as predictors; pivoting them into var then dropped the column the downstream geom_jitter(aes(color = yvar)) referenced, and the patchwork errored when actually rendered. CI did not catch this because the existing test only asserted the patchwork class (lazy) and snapshots run with VDIFFR_RUN_TESTS = false. New test exercises a real build of every sub-plot.
• plot.gg_variable(): the same default-xvar selection used substring grep("time", ...) / grep("event", ...), which silently dropped any predictor whose name contained those substrings -- e.g. the documented veteran-data survival predictor diagtime. Switch to exact matching for event / time / yvar / outcome and an anchored prefix for yhat (yhat or yhat.<class>). New test exercises diagtime on the veteran survival forest.
• gg_roc(): per-class one-vs-rest ROC curves (#88, closes #72).
  • New per_class argument, default FALSE. With per_class = TRUE on a forest of more than two classes, gg_roc() returns a long-format gg_roc data frame with a class factor column, plus a named AUC vector attribute with one entry per class, ordered by descending AUC.
  • plot.gg_roc() gains panel = c("overlay", "facet"). When the object has a class column, "overlay" colors the curves by class and "facet" gives each class its own panel.
  • summary.gg_roc() prints the named per-class AUC values when a class column is present.
  • On a binary forest, per_class = TRUE does nothing, the usual single-curve result comes back unchanged.
  • ROC confidence intervals are still to come, in v3.1.0 (issue #7 / #72-CIs).
• New gg_udependent(): varPro cross-variable dependency (Phase 3).
  • gg_udependent() reads cross-variable dependency scores off a uvarpro fit, via varPro::get.beta.entropy() and varPro::sdependent(). It returns a tidy list: $edges (variable_from, variable_to, weight), $nodes (variable, degree, selected), and $graph, an igraph object.
  • plot.gg_udependent() draws the dependency network with ggraph. Edge width and opacity scale with dependency strength; node color marks the signal variables. The layout is configurable ("fr", "kk", "stress", and so on).
  • ggraph added to Suggests:.
• New gg_varpro(): varPro variable importance (#85).
  • gg_varpro() pulls per-tree importance scores from a fitted varpro object and draws a boxplot of the per-tree z-score distribution for each variable. The hinges sit at the 15th and 85th percentiles and the whiskers at the 5th and 95th, so the box is not the usual Tukey one — it reports the percentiles it actually shows. Variables with aggregate z above cutoff (default 0.79) are color-highlighted.
  • With faithful = TRUE, the individual per-tree z-scores are jittered over the box as semi-transparent points, with a white-outlined dot at the mean, the same view as varPro's internal bxp output.
  • With conditional = TRUE (classification forests only), gg_varpro() reads $conditional.z and draws class-conditional importance as a facet_wrap(~class, nrow=1) bar chart.
  • Set local.std = FALSE to allow plot(..., type = "raw"), which shows raw per-tree importance instead of the z-normalized values.
• gg_variable.randomForest: classification fix (#87).
  • For a classification forest, gg_variable.randomForest() now stores per-class OOB vote fractions as yhat.<classname> columns, read from object$votes, the same layout the rfsrc path produces. It used to store a single yhat factor column of class labels (from object$predicted), and that column shape stopped the multi-class pivot in plot.gg_variable from ever running. The vote fractions are row-normalized to [0, 1], even when the forest was fit with norm.votes = FALSE.
  • plot.gg_variable, binary classification: with smooth = TRUE the x and y aesthetics are now mapped onto the smooth layer correctly.
  • plot.gg_variable, multi-class numeric path: smooth = TRUE now adds the smooth layer instead of skipping it silently.
  • Closes stale issues #81 (fixed in PR #83) and #82.
• New gg_partial_varpro(): varPro partial dependence (#84).
  • gg_partial_varpro() takes over from gg_partialpro() as the entry point for varPro partial dependence plots. It accepts an optional object argument (the originating varpro fit) which it uses for provenance-aware axis labels, and a scale argument ("auto", "mortality", "rmst", "surv", "chf").
  • Ensemble mortality labeling (Ishwaran et al. 2008): with scale = "mortality", or scale = "auto" on a survival forest, the y-axis reads "Ensemble mortality (expected events)". That is an unbounded relative-risk score, not a survival probability, and the documentation says so plainly so it is not misread.
  • Survival path C: with scale = "surv" or scale = "chf", gg_partial_varpro() pulls the embedded rfsrc forest from object$rf and returns true S(t) or CHF partial curves through the existing gg_partial_rfsrc machinery.
  • varPro is now a hard dependency (Imports:).
  • gg_partialpro() is soft-deprecated: it warns, then hands off to gg_partial_varpro(). It will be removed in the release after v3.0.0.
• randomForest engine validation and repair (#82). Fixes #80, #81, and a plot.gg_error label wart, and adds full randomForest regression test coverage. Details below.
  • plot.gg_variable() now always returns a single ggplot (one variable) or a patchwork composite (several variables, or the default) — never a bare list. This matches the v2.7.3 plot.gg_partial* change. A list used to come back for multiple xvar, which broke patchwork / autoplot() / layer_data() composition (#80).
  • gg_roc() and calc_roc() for randomForest now build the ROC from class probabilities (OOB votes by default, honoring oob) rather than the degenerate three-point curve they produced before. With which_outcome = "all" (the default for gg_roc(rf)) the result is a macro-averaged one-vs-rest ROC, and no warning. The shared .validate_which_outcome helper and calc_roc.rfsrc are byte-for-byte unchanged, so rfsrc behavior is untouched (#81).
• Dependency modernization. This breaks scripts that relied on attachment. randomForestSRC and randomForest move from Depends: to Imports:; igraph, callr, and varPro are added to Suggests: (varPro later moves up to Imports:, with the first varPro-integration component). library(ggRandomForests) no longer puts randomForestSRC or randomForest on the search path. A script that called rfsrc() or randomForest() unqualified after only library(ggRandomForests) now needs its own library(randomForestSRC) / library(randomForest), or must qualify the calls. ggRandomForests itself is unaffected. It qualifies every call into its dependencies.

ggRandomForests v2.7.3

• plot.gg_partial(), plot.gg_partial_rfsrc(), and plot.gg_partialpro() now always return a single ggplot/patchwork object. Previously, when both continuous and categorical predictors were present, they returned a named list list(continuous=, categorical=), which surprised users and made autoplot() dispatch ambiguous. The two panels are now combined vertically via patchwork::wrap_plots() (patchwork moved from Suggests to Imports). Closes #77.
• autoplot() S3 methods for all 10 gg_* classes, delegating to the corresponding plot.gg_*() method so objects work in |> pipelines, patchwork, and cowplot compositions via ggplot2::autoplot().
• print() and summary() S3 methods for every gg_* data object (gg_error, gg_vimp, gg_rfsrc, gg_variable, gg_partial, gg_partial_rfsrc, gg_partialpro, gg_roc, gg_survival, gg_brier). print() is header-only — use head() for rows. summary() returns a printable summary.gg object with per-class diagnostics. Each gg_* constructor now attaches a "provenance" attribute (source, family, ntree, n, xvar.names) consumed by the new methods.
• New gg_brier() extractor and plot.gg_brier() method for time-resolved Brier scores and CRPS on survival forests (issue #9). Wraps randomForestSRC::get.brier.survival() and adds the mortality-quartile decomposition, a 15-85 percent per-subject envelope, and running CRPS via trapezoidal integration. Supports cens.model = c("km", "rfsrc"), type = c("brier", "crps"), and envelope (overall line + 15-85% ribbon). Multi-model comparison is left to dplyr::bind_rows() on multiple gg_brier outputs — see ?gg_brier for an example.
• Visual unification of ribbon overlays across plot methods. All ribbons now use a shared alpha (.gg_ribbon_alpha = 0.2) and a shared fill (.gg_ribbon_fill = "steelblue") for single-series cases (KM/NA CIs, bootstrap CIs, gg_brier envelope); group-stratified ribbons keep their group-colored fill. Statistical bounds unchanged — only styling. ggRandomForests v2.7.2 =====================
• Address CRAN reviewer (Benjamin Altmann) feedback on the v2.7.1 resubmission:
  • Add methods references to DESCRIPTION (Breiman 2001 and Ishwaran et al. 2008, with <doi:...> auto-links) per CRAN cookbook.
  • Drop the man/shift.Rd Rd file: shift() is an internal utility and the example used ggRandomForests:::shift(...). Marked the function @noRd so it no longer generates a help page.
  • Replace cat() in surv_partial.rfsrc() with message() so progress output is suppressible (suppressMessages()) and plays nicely inside notebooks / Shiny / quarto.
  • Restore the user's par() settings in the surv_partial.rfsrc() example via oldpar <- par(no.readonly = TRUE); on.exit(par(oldpar)).

ggRandomForests v2.7.1

• Fix gg_partial_rfsrc() for survival forests: partial.rfsrc() was being called without partial.type, causing a zero-length comparison (if (partial.type == "rel.freq") ...) inside the C-level prediction routine and aborting the call. Survival forests now pass partial.type = "surv" (default; configurable via the new partial.type argument accepting "surv", "chf", or "mort"). This unblocks the partial-dep chunk in the survival vignette.
• Fix gg_partial_rfsrc() for survival forests with multiple partial.time values: get.partial.plot.data() returns yhat as an [length(partial.values) x length(partial.time)] matrix, but the previous code assumed a vector and crashed on column-mismatch when assigning time. The result is now reshaped to long form so each (x, time) pair is a single row.
• Improve plot.gg_partial_rfsrc() survival layout: predictor value is now on the x-axis with one curve per (rounded) time point colored by Time, faceted by variable name. The previous default put time on the x-axis and one curve per predictor value, producing a saturated legend with dozens of nearly-identical lines.
• Add tests/testthat/test_plot_layer_data.R: regression suite that uses ggplot2::layer_data() to verify each plot.gg_*() method renders non-empty layers for every supported forest family. Catches the empty-figure class of bug (transform/plot column-name mismatch) without requiring visual inspection.
• ggrandomforests.news() now reads NEWS.md (the canonical change log R also surfaces via utils::news()). The legacy hand-maintained inst/NEWS has been removed — it had silently drifted to v2.4.0 (June 2025) across three releases, so users running the helper saw stale version info. One source of truth, no more drift window.
• Fix plot.gg_vimp() legend duplication: the bar geom mapped both fill and color to the positive column, but only the fill legend was titled "VIMP > 0", leaving a redundant second legend titled "positive". Both aesthetics now share the "VIMP > 0" title so ggplot merges them into a single legend by default.
• Fix plot.gg_vimp() for forests with all-positive VIMP: the bar geom previously mapped only color (no fill), producing hollow / outline- only bars and an "Ignoring unknown labels: fill" warning whenever labs(fill = ...) was applied. Both fill and color are now mapped unconditionally, so bars render filled in every case.
• Add @examples blocks to plot.gg_partial_rfsrc() and plot.gg_partialpro(). The latter uses a self-contained mock of the varpro::partialpro() output structure so the example runs without pulling in varpro as a dependency.

ggRandomForests v2.7.0

• S3 design overhaul: gg_partial(), gg_partialpro(), and gg_partial_rfsrc() now stamp their return values with S3 classes (gg_partial, gg_partialpro, gg_partial_rfsrc respectively), enabling plot() dispatch without any boilerplate.
• Add plot.gg_partial(), plot.gg_partial_rfsrc(), and plot.gg_partialpro() S3 methods; continuous predictors render as line plots, categorical as bar charts, faceted by variable name. Survival forests produce curves over time; two-variable surface plots group by xvar2.name.
• Convert gg_survival() to an S3 generic dispatching on the class of its first argument. New gg_survival.rfsrc() method extracts the survival response directly from the fitted forest (no separate data argument needed); gg_survival.default() preserves the existing interface.
• Fix plot.gg_survival() auto-coercion: previously called gg_survival(rfsrc_obj) treating the forest as the interval string argument, causing a latent crash; replaced with inherits() guard.
• Deprecate surv_partial.rfsrc() via .Deprecated() with a pointer to gg_partial_rfsrc(); all package tests updated to suppress the warning.
• Fix gg_partial_rfsrc() — make_eval_grid() used unlist(dplyr::select()) which coerced factor columns to integer codes; now uses newx[[xname]] to preserve column class. Categorical detection extended to cover is.factor() and is.character() in addition to the cardinality check.
• Add guards to gg_partial_rfsrc(): all-NA xval after NA removal now emits a warning and skips the variable; all-NA grouping variable (xvar2) calls stop(); n_eval and cat_limit are validated as single integers

  = 2 near function entry.

• Fix cyclomatic complexity across gg_partial_rfsrc.R: refactored into eight top-level unexported helpers (validate_scalar_int, validate_partial_args, snap_partial_time, make_eval_grid, call_partial_rfsrc, partial_one_var, partial_no_group, partial_with_group, split_partial_result); all functions now score below the cyclocomp_linter limit of 20.
• Fix @param partial.time documentation: "see the section above" corrected to "see the section below".
• Replace deprecated tidyr::gather() with tidyr::pivot_longer() in plot.gg_vimp() and plot.gg_partialpro().
• Add gg_survival.rfsrc, gg_survival.default, plot.gg_partial, plot.gg_partial_rfsrc, and plot.gg_partialpro to NAMESPACE; add corresponding @rdname / @export roxygen tags.
• Update tests: add expect_s3_class() checks for all new classes; add plot() smoke tests for gg_partial, gg_partial_rfsrc, gg_partialpro; add gg_survival.rfsrc tests for KM extraction, by stratification, and error on non-survival forest.
• Add plot.gg_partial, plot.gg_partial_rfsrc, and plot.gg_partialpro to _pkgdown.yml reference index.

ggRandomForests v2.7.0

• Fix critical visual bug in plot.gg_rfsrc: all aes() calls used bare string literals instead of .data[[col]], causing every aesthetic to map to a constant string rather than the underlying data column. All plot types (regression, classification, survival) were affected.
• Fix aes() bare-string literals in plot.gg_roc multi-class branch; remove unreachable if (crv < 2) dead-code branch.
• Fix bootstrap_survival CI-band indexing in gg_rfsrc: negative index computed via colnames() was a no-op on large datasets and a latent crash for data with ≤ 2 unique event times.
• Fix gg_rfsrc.rfsrc: is.null(df[, col]) does not detect missing columns; replaced with !col %in% colnames() guard.
• Fix gg_rfsrc.randomForest: method used non-existent object$xvar; now recovers the training frame via .rf_recover_model_frame().
• Fix legend suppression in plot.gg_error for single-outcome forests where the data frame has no variable column.
• Fix gg_vimp and plot.gg_vimp: 1:nvar replaced with seq_len(nvar) in both S3 methods; 1:0 silently returned c(1, 0) instead of integer(0) when nvar == 0.
• Migrate full test suite to testthat 3.x API: expect_is → expect_s3_class / expect_type / expect_true(is.*()); expect_equivalent → expect_equal(ignore_attr = TRUE); all context() calls removed; testthat 1.x expect_that / is_identical_to removed.
• Add .lintr package-level linter configuration; fix lintr spacing in gg_partial.
• Improve GitHub Actions: lint.yaml now fails CI on any lint issue; R-CMD-check.yaml treats warnings as errors and uses Rtools 44; test-coverage.yaml duplicate codecov upload removed.
• Add covr and vdiffr to Suggests.

ggRandomForests v2.6.1

• Fix model-label assignment in gg_partial for categorical variable data
• Refactor gg_partial and gg_partial_rfsrc to improve factor-level normalization and categorical data handling

ggRandomForests v2.6.0

• Add and export new plotting functions; update existing plot documentation
• Improve unit and integration tests; overall coverage raised to 83%
• Remove hvtiRutilities internal dependency; clean up associated imports
• Refactor gg_partial_rfsrc to use .data pronoun for all dplyr calls

ggRandomForests v2.5.0

• Initial gg_partial_rfsrc function: computes partial dependence data directly from an rfsrc model via randomForestSRC::partial.rfsrc, without requiring a separate plot.variable call
• Add support for a grouping variable (xvar2.name) in gg_partial_rfsrc
• Improved vignette formatting and namespace usage

ggRandomForests v2.4.0

• Updating to latest ggplot2 functions
• Utilize some namespace referencing
• Added pkgdown documentation
• Minor testing improvements

ggRandomForests v2.3.0

• Knocking the dust off this.
• Fix the ROC curves
• Fix the colors on VIMP plot

ggRandomForests v2.2.1

• Fix docs for HTML5/Roxygen update

ggRandomForests v2.2.0

• Bring back the regression vignette
• Improve package tests and code coverage
• Clean up code with lintr

ggRandomForests v2.1.0

To pull this out of archive on randomForestSRC 3.1 build release. Fixed a plot bug for gg_error to show the actual curve (issue 35)

ggRandomForests v2.0.1

• Correct a bug in survival plots when predicting on future data without a known outcome.
• All Vignettes are now at https://github.com/ehrlinger/ggRFVignette
• All tests are being moved to https://github.com/ehrlinger/ggRFVignette
• Begin work on rewriting all checks to not use cached data. This will require more runtime, and hence we will run fewer of them on CRAN release.
• Minor bug and documentation fixes.

ggRandomForests v2.0.0

• Added initial support for the randomForest package
• Updated cache files for randomForestSRC 2.2.0 release.
• Remove regression vignettes to meet CRAN size limits. These remain available at the package source https://github.com/ehrlinger/ggRandomForests
• Minor bug and documentation fixes.

ggRandomForests v1.2.1

• Update cached datasets for randomForestSRC 2.0.0 release.
• Correct some vignette formatting errors (thanks Joe Smith)

ggRandomForests v1.2.0

• Convert to semantic versioning http://semver.org/
• Updates for release of ggplot2 2.0.0
• Change from reshape2::melt dependence to tidyr::gather
• Optimize tests for CRAN to optimize R CMD CHECK times.

ggRandomForests v1.1.4

• combine.gg_partial bug when giving a single variable plot.variable object.

• Remove dplyr depends to transitions from "Imports" to "Suggests".

• Argument for single outcome gg_vimp plot for classification forests.

• Improvements to gg_vimp arguments for consistency.

• Add bootstrap confidence intervals to gg_rfsrc function.

• Initial partial.rfsrc function to replace the randomForestSRC::plot.variable function.

• Move cache data to randomForestSRC v1.6.1 to take advantage of rfsrc version checking between function calls.

• Vignette updates for JSS submission of "ggRandomForests: Exploring Random Forest Survival".

• Vignette updates for arXiv submission of ggRandomForests: Random Forests for Regression

• Some optimizations to reduce package size.

• Remove all tests from CRAN build to optimize R CMD CHECK times.

• Remove pdf vignette figure from CRAN build.

• Return S3method calls to NAMESPACE for "S3 methods exported but not registered" for R V3.2+.

• Misc Bug Fixes.

ggRandomForests v1.1.3

• Update "ggRandomForests: Visually Exploring a Random Forest for Regression" vignette.
• Further development of draft package vignette "Survival with Random Forests".
• Rename vignettes to align with randomForestSRC package usage.
• Add more tests and example functions.
• Refactor gg_ functions into S3 methods to allow future implementation for other random forest packages.
• Improved help files.
• Updated DESCRIPTION file to remove redundant parts.
• Misc Bug Fixes.

ggRandomForests v1.1.2

• Add package vignette "ggRandomForests: Visually Exploring a Random Forest for Regression"
• Add gg_partial_coplot, quantile_cuts and surface_matrix functions
• export the calc_roc and calc_auc functions.
• replace tidyr function dependency with reshape2 (melt instead of gather) due to lazy eval issues.
• reduce dplyr dependencies (remove select and %>% usage for base equivalents, I still use tbl_df for printing)
• Further development of package vignette "Survival with Random Forests"
• Refactor cached example datasets for better documentation, estimates and examples.
• Improved help files.
• Updated DESCRIPTION file to remove redundant parts.
• Misc Bug Fixes.

ggRandomForests v1.1.1

Maintenance release, mostly to fix gg_survival and gg_partial plots.

• Fix the gg_survival functions to plot kaplan-meier estimates.
• Fix the gg_partial functions for categorical variables.
• Add some more S3 print functions.
• Try to make gg_functions more consistent.
• Further development of package vignette "Survival with Random Forests"
• Modify the example cached datasets for better estimates and examples.
• Improve help files.
• Misc Bug Fixes.

ggRandomForests v1.1.0

• Add panel option for gg_variable and gg_partial
• Rework interactions plot
• add gg_coplot functions
• Imports instead of depends
• Add version dependencies for randomForestSRC
• Include package vignette "Random Forests for Survival"
• Misc Bug Fixes

ggRandomForests v1.0.0

• First CRAN release.

ggRandomForests v0.2

• Initial useR!2014 release.