R Under development (unstable) (2026-08-05 r90355) -- "Unsuffered Consequences" Copyright (C) 2026 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(testthat) > > test_check("ggRandomForests") Loading required package: ggRandomForests ggRandomForests 3.5.0 Type ggrandomforests.news() to see new features, changes, and bug fixes. randomForestSRC 3.6.2 Type rfsrc.news() to see new features, changes, and bug fixes. randomForest 4.7-1.2 Type rfNews() to see new features/changes/bug fixes. OMP: Warning #96: Cannot form a team with 28 threads, using 2 instead. OMP: Hint Consider unsetting KMP_DEVICE_THREAD_LIMIT (KMP_ALL_THREADS), KMP_TEAMS_THREAD_LIMIT, and OMP_THREAD_LIMIT (if any are set). from varPro::get.beta.entropy | family: unsupv | ntree: 50 | cutoff: 0.4125 (default) | precomputed: TRUE 2 of 4 variables selected over 3 released region(s) from varPro::beta.varpro | family: regr | cutoff: 2.84 (default) | precomputed: TRUE 1 of 4 variables selected; 182 / 250 rules with non-zero beta from varPro::ivarpro | family: regr | view: aggregate | precomputed: TRUE 727 (obs x variable) cells; 405 unique obs across 7 variables entry.c:184:55: runtime error: pointer index expression with base 0x000000000001 overflowed to 0xfffffffffffffff9 #0 0x7ff2e83d89d4 in rfsrcGrow /tmp/RtmpYknzRd/R.INSTALL20b35d7f36dacb/randomForestSRC/src/entry.c:184:55 #1 0x557dc6735e4b in R_doDotCall (/data/localhost/ripley/R/R-clang/bin/exec/R+0x90e4b) #2 0x557dc673673d in do_dotcall (/data/localhost/ripley/R/R-clang/bin/exec/R+0x9173d) #3 0x557dc6775273 in bcEval_loop eval.c #4 0x557dc676e83b in bcEval eval.c #5 0x557dc676dff4 in Rf_eval (/data/localhost/ripley/R/R-clang/bin/exec/R+0xc8ff4) #6 0x557dc6786158 in R_execClosure eval.c #7 0x557dc678564a in applyClosure_core eval.c #8 0x557dc676ef16 in Rf_applyClosure (/data/localhost/ripley/R/R-clang/bin/exec/R+0xc9f16) #9 0x557dc676e447 in Rf_eval (/data/localhost/ripley/R/R-clang/bin/exec/R+0xc9447) #10 0x557dc66faeff in do_docall (/data/localhost/ripley/R/R-clang/bin/exec/R+0x55eff) #11 0x557dc6775273 in bcEval_loop eval.c #12 0x557dc676e83b in bcEval eval.c #13 0x557dc676dff4 in Rf_eval (/data/localhost/ripley/R/R-clang/bin/exec/R+0xc8ff4) #14 0x557dc6786158 in R_execClosure eval.c #15 0x557dc678564a in applyClosure_core eval.c #16 0x557dc676ef16 in Rf_applyClosure (/data/localhost/ripley/R/R-clang/bin/exec/R+0xc9f16) #17 0x557dc676e447 in Rf_eval (/data/localhost/ripley/R/R-clang/bin/exec/R+0xc9447) #18 0x557dc6732adb in do_External (/data/localhost/ripley/R/R-clang/bin/exec/R+0x8dadb) #19 0x557dc6775273 in bcEval_loop eval.c #20 0x557dc676e83b in bcEval eval.c #21 0x557dc676dff4 in Rf_eval (/data/localhost/ripley/R/R-clang/bin/exec/R+0xc8ff4) #22 0x557dc6786158 in R_execClosure eval.c #23 0x557dc678564a in applyClosure_core eval.c #24 0x557dc676ef16 in Rf_applyClosure (/data/localhost/ripley/R/R-clang/bin/exec/R+0xc9f16) #25 0x557dc676e447 in Rf_eval (/data/localhost/ripley/R/R-clang/bin/exec/R+0xc9447) #26 0x557dc678a7aa in do_begin (/data/localhost/ripley/R/R-clang/bin/exec/R+0xe57aa) #27 0x557dc676e21f in Rf_eval (/data/localhost/ripley/R/R-clang/bin/exec/R+0xc921f) #28 0x557dc678c927 in do_eval (/data/localhost/ripley/R/R-clang/bin/exec/R+0xe7927) #29 0x557dc6775273 in bcEval_loop eval.c #30 0x557dc676e83b in bcEval eval.c #31 0x557dc676dff4 in Rf_eval (/data/localhost/ripley/R/R-clang/bin/exec/R+0xc8ff4) #32 0x557dc6786158 in R_execClosure eval.c #33 0x557dc678564a in applyClosure_core eval.c #34 0x557dc676ef16 in Rf_applyClosure (/data/localhost/ripley/R/R-clang/bin/exec/R+0xc9f16) #35 0x557dc676e447 in Rf_eval (/data/localhost/ripley/R/R-clang/bin/exec/R+0xc9447) #36 0x557dc678cd07 in do_eval (/data/localhost/ripley/R/R-clang/bin/exec/R+0xe7d07) #37 0x557dc6775273 in bcEval_loop eval.c #38 0x557dc676e83b in bcEval eval.c #39 0x557dc676dff4 in Rf_eval (/data/localhost/ripley/R/R-clang/bin/exec/R+0xc8ff4) #40 0x557dc6786158 in R_execClosure eval.c #41 0x557dc678564a in applyClosure_core eval.c #42 0x557dc6788af8 in R_forceAndCall (/data/localhost/ripley/R/R-clang/bin/exec/R+0xe3af8) #43 0x557dc66ba9db in do_lapply (/data/localhost/ripley/R/R-clang/bin/exec/R+0x159db) #44 0x557dc67d0807 in do_internal (/data/localhost/ripley/R/R-clang/bin/exec/R+0x12b807) #45 0x557dc67754d5 in bcEval_loop eval.c #46 0x557dc676e83b in bcEval eval.c #47 0x557dc676dff4 in Rf_eval (/data/localhost/ripley/R/R-clang/bin/exec/R+0xc8ff4) #48 0x557dc6786158 in R_execClosure eval.c #49 0x557dc678564a in applyClosure_core eval.c #50 0x557dc676ef16 in Rf_applyClosure (/data/localhost/ripley/R/R-clang/bin/exec/R+0xc9f16) #51 0x557dc676e447 in Rf_eval (/data/localhost/ripley/R/R-clang/bin/exec/R+0xc9447) #52 0x557dc67bb777 in Rf_ReplIteration (/data/localhost/ripley/R/R-clang/bin/exec/R+0x116777) #53 0x557dc67bd27e in run_Rmainloop (/data/localhost/ripley/R/R-clang/bin/exec/R+0x11827e) #54 0x557dc67bd2ea in Rf_mainloop (/data/localhost/ripley/R/R-clang/bin/exec/R+0x1182ea) #55 0x557dc66a6db7 in main (/data/localhost/ripley/R/R-clang/bin/exec/R+0x1db7) #56 0x7ff2f2e0a680 in __libc_start_call_main (/lib64/libc.so.6+0x3680) (BuildId: 17f2e1fd905f485786f6fd6e3bede4ad737137e7) #57 0x7ff2f2e0a797 in __libc_start_main@GLIBC_2.2.5 (/lib64/libc.so.6+0x3797) (BuildId: 17f2e1fd905f485786f6fd6e3bede4ad737137e7) #58 0x557dc66a6cd4 in _start (/data/localhost/ripley/R/R-clang/bin/exec/R+0x1cd4) SUMMARY: UndefinedBehaviorSanitizer: undefined-behavior entry.c:184:55 pbc: rfsrc 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`/`` 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.`). 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 `$conditional.z` and draws class-conditional importance as a `facet_wrap(~class, nrow=1)` bar chart. - Set `local.std = FALSE` to allow `plot(..., type = "raw")`, which shows raw per-tree importance instead of the z-normalized values. * `gg_variable.randomForest`: classification fix (#87). - For a classification forest, `gg_variable.randomForest()` now stores per-class OOB vote fractions as `yhat.` columns, read from `object$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 `` 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. [ FAIL 0 | WARN 24 | SKIP 30 | PASS 1336 ] ══ Skipped tests (30) ══════════════════════════════════════════════════════════ • On CRAN (25): 'test_gg_beta_uvarpro.R:149:3', 'test_gg_isopro.R:151:3', 'test_gg_ivarpro.R:291:3', 'test_gg_partial_varpro.R:404:3', 'test_gg_partial_varpro.R:507:3', 'test_gg_partial_varpro.R:533:3', 'test_gg_partial_varpro.R:703:3', 'test_gg_sdependent.R:101:3', 'test_gg_shap.R:3:3', 'test_gg_shap.R:36:3', 'test_gg_shap.R:49:3', 'test_gg_shap.R:62:3', 'test_gg_shap.R:75:3', 'test_gg_shap.R:88:3', 'test_gg_shap.R:101:3', 'test_gg_shap.R:119:3', 'test_gg_shap.R:133:3', 'test_gg_shap.R:150:3', 'test_gg_shap.R:191:3', 'test_gg_vimp.R:459:3', 'test_gg_vimp.R:481:3', 'test_gg_vimp.R:503:3', 'test_gg_vimp.R:532:3', 'test_gg_vimp.R:555:3', 'test_print_summary.R:184:3' • empty test (2): , • plot.gg_partialpro tests superseded by gg_partial_varpro shim; see test_gg_partial_varpro.R (3): 'test_gg_partialpro.R:157:3', 'test_gg_partialpro.R:166:3', 'test_gg_partialpro.R:176:3' [ FAIL 0 | WARN 24 | SKIP 30 | PASS 1336 ] Deleting unused snapshots: 'snapshots/gg-beta-varpro-class-binary.svg', 'snapshots/gg-beta-varpro-class-multiclass.svg', 'snapshots/gg-beta-varpro-default.svg', 'snapshots/gg-brier-survival-crps.svg', 'snapshots/gg-brier-survival-envelope.svg', 'snapshots/gg-brier-survival-overall.svg', 'snapshots/gg-error-classification-rf.svg', 'snapshots/gg-error-classification-rfsrc.svg', 'snapshots/gg-error-regression-rf.svg', 'snapshots/gg-error-regression-rfsrc.svg', 'snapshots/gg-error-survival-rfsrc.svg', 'snapshots/gg-isopro-default.svg', 'snapshots/gg-isopro-predict-overlay.svg', 'snapshots/gg-isopro-threshold.svg', 'snapshots/gg-ivarpro-class-distribution.svg', 'snapshots/gg-ivarpro-class-which-obs.svg', 'snapshots/gg-ivarpro-regr-distribution.svg', 'snapshots/gg-ivarpro-regr-which-obs.svg', …, 'snapshots/gg-vimp-regression-rfsrc.svg', and 'snapshots/gg-vimp-survival-rfsrc.svg' > > proc.time() user system elapsed 132.461 15.646 185.034