# OptimalBinningWoE 1.13.3

## Audit fixes (2026-08-21)

Bug-fix release from a third internal audit, covering the WoE/IV return
contract, the `converged` flag and the `max_bins` constraint across all 37
algorithm/type combinations. Every item below was reproduced before the fix
and re-verified after it. The regression suite gained
`tests/testthat/test-audit-regressions.R`, which fails on 1.13.2.

### Behavior changes (read before upgrading)

*   **`mdlp` (numerical), `gmb` and `fetb` (categorical) now honour
    `max_bins`.** All three stopped on their own criterion and never re-checked
    the cap, so `max_bins = 5` returned 18, 11 and 10 bins respectively --
    silently, with no warning. All three roxygen blocks already documented
    `max_bins` as a hard constraint, so the code was wrong, not the
    documentation.

    **These three algorithms now produce different bins.** For `mdlp` the
    returned partition is no longer the unconstrained MDL optimum when the cap
    binds: merging continues past the MDL stopping point, each step taking the
    pair with the smallest increase in MDL cost. `min_bins` is never violated
    to satisfy `max_bins`.

*   **`obwoe_apply()` now refuses multiclass models instead of scoring them
    wrongly.** A multinomial fit carries a bins x classes WoE matrix; the
    lookup linear-indexed it column-major and returned **class 1's WoE for
    every row**, discarding the other classes with no error and no warning. It
    now stops with an actionable message. The per-class matrix is still in
    `$results` for callers that want to handle it themselves.

*   **`dmiv` now reports a numeric `total_iv`** where `summary$total_iv` was
    previously `NA` for every feature. Code that tested for that `NA` will see
    a number.

### Corrected values

*   **The categorical `sketch` engine computed every WoE against the wrong
    marginal.** It passed `(total_neg, total_pos)` to helpers whose signature
    is `(total_pos, total_neg)`. On a random 8-category feature with no real
    signal, the reported IV was **10.4021 against a true value of 0.0043** --
    wrong by three orders of magnitude, and wrong in the direction that makes
    a useless variable look like the strongest predictor in the model. WoE
    deviated from `log((pos_i/TP)/(neg_i/TN))` by up to 2.7721; it is now
    within 0.0002.

    The identical defect was fixed in the numerical twin some releases ago and
    never replicated here. A numerical-vs-categorical parity test now covers
    every algorithm that has both variants, so this class of divergence cannot
    recur silently.

*   **`dmiv` returns `iv` and `total_iv`** alongside the divergence measures it
    already reported. IV is computed from the smoothed class distributions
    rather than from `woe`, because the default `bin_method = "woe1"` is
    Zeng's log-odds `ln((pos + 0.5)/(neg + 0.5))`, which differs from standard
    WoE by the constant `ln(TP/TN)`; deriving IV from it would be wrong.

*   **`cm` (categorical) exposes `total_iv` at top level**, like the other 15
    categorical engines, instead of only inside `metadata`. `metadata` is
    unchanged, so existing callers keep working.

### Fixed crashes

*   **`sab` (categorical) no longer aborts with `unordered_map::at`** when a
    category contains zero events -- a routine situation in credit data. The
    positive-count map only gained a key for categories with at least one
    event, but was read with `.at()` in seven places.

*   **`obwoe_scorecard()` and the cutoff table now reject a missing target with
    a clear message** naming the sample and the count, instead of failing deep
    inside with `missing value where TRUE/FALSE needed`. The development frame
    was already checked; samples passed through `validation =` were not. The
    `NA` is rejected at the boundary rather than swept up with
    `na.rm = TRUE`, which would have silently charged every unknown-outcome
    row to the non-events and corrupted KS and AUC.

### `converged` now means the same thing everywhere

The flag was effectively inverted in several engines: initialised `false` and
set `true` only on a degenerate shortcut or an in-loop tolerance test, while
the normal successful exit -- reaching the bin-count target -- set nothing. So
ordinary well-binned features reported `FALSE` and only degenerate ones
reported `TRUE`.

*   `dmiv` and `bb` (numerical) report `converged` on reaching the bin-count
    target, matching the categorical `dmiv`, which already did.
*   `ir` (numerical) reports `converged` for the exact binning it produces when
    a feature has two or fewer distinct values. **Every binary 0/1 feature
    previously reported `FALSE`** despite a correct result.
*   `dp`, `gmb`, `mba`, `sketch`, `cm` and `dmiv` (categorical) report
    `converged` on every successful termination path, including the fast paths
    that bypassed the flag entirely.

The intended contract is now documented in `src/common/bin_structures.h`:
`converged == true` means the algorithm reached a valid stopping state
(tolerance met, monotonicity achieved, or the bin-count target reached);
`false` means it exhausted `max_iterations`.

### Three algorithms were quadratic in the number of rows

`lpdb`, `ldb` and numerical `udt` scaled as n^2.00, n^2.00 and n^2.30. A single
variable with 10^6 rows would have taken `lpdb` roughly 72 minutes; measured
against `jedi` at the same size they were 2,772x the median algorithm's cost.
Nothing warned about it.

Two distinct causes, neither of them inherent to the methods:

*   **`ldb` and `lpdb` estimated the density with a naive double loop**,
    evaluating the Gaussian kernel of every observation against every other
    one. The same defect had been written twice, in two files, which is how it
    survived. It is replaced by the standard linear-binning estimator -- the
    one R's own `density()` uses -- which now lives once in
    `src/common/optimal_binning_common.h` so the two cannot diverge again.

*   **`udt` rescanned every observation once per candidate split**, allocating
    two vectors and recomputing the parent entropy each time, giving O(u x n).
    Information gain depends only on integer counts, so a single sweep carrying
    running totals produces the identical value.

Measured at n = 50,000, against a build of the previous revision:

| algorithm | before | after | speedup |
|---|---|---|---|
| `ldb` | 10.740s | 0.010s | 1074x |
| `lpdb` | 10.725s | 0.011s | 975x |
| `udt` | 7.354s | 0.019s | 387x |

All three now scale linearly and land within a factor of two of `jedi`, the
package default. At n = 400,000 they take 0.085s, 0.087s and 0.170s against
`jedi`'s 0.103s -- sizes the previous code could not reach at all.

**Results.** `udt` and `ldb` are bit-identical to the previous revision: `udt`
by construction, and `ldb`'s local-minimum search resolves the grid estimate to
the same cut points. Both are pinned by a new regression test.

**`lpdb` changes.** It differentiates the density twice to find inflection
points, and finite differences taken between adjacent observations are not the
same thing as finite differences on a properly sampled curve. Its critical
points are now located on the estimation grid. On German Credit the partitions
generally improve -- `duration` goes from 2 bins and IV 0.0923 to 5 bins and IV
0.2635, `age` from 2 bins and 0.0628 to 4 bins and 0.0781 -- and no variable
tested got materially worse. Anyone with a fitted `lpdb` model should expect
different cut points.

Also removed `OBN_LPDB::local_polynomial_density()`, which no longer had a
caller and never did local polynomial regression despite its name.

### Fixed: categorical `dmiv` ignored `max_bins`

*   **`ob_categorical_dmiv()` returned `L - 1` bins for an `L`-category
    feature, whatever `max_bins` was set to.** The merge loop compared the cost
    of the best available merge against `convergence_threshold` and broke out
    when it had barely moved. With many similarly sized categories the
    second-cheapest merge costs exactly what the cheapest one did, so the test
    fired on the second iteration, after a single merge, and nothing
    re-imposed the cap afterwards. Reported as `converged = TRUE`, with no
    warning.

    Reproduced at n = 60,000 with `max_bins` of 3, 5 and 8 alike: 40 levels
    returned 31 bins, 60 returned 50, 120 returned 119 and 300 returned 299.
    All nine `divergence_method` choices and both `bin_method` choices were
    affected. The roxygen already documented `max_bins` as a hard constraint,
    so the code was wrong, not the documentation.

    The loop now records the convergence and keeps merging by the same
    criterion — the pair with the lowest divergence, i.e. the most similar
    pair — until the cap is met (`src/OBC_DMIV_v5.cpp`). This is the remedy
    already applied to `fetb`, whose loop had the same shape. The merge
    ordering is unchanged, and `min_bins` is never violated to satisfy
    `max_bins`.

    **`dmiv` produces different bins wherever the cap used to be abandoned**;
    on inputs where it was reached anyway, results are **byte-identical** to
    the previous release — verified across all 16 categorical engines and 13
    variables of the bundled German Credit data, 208 combinations, with the
    RNG stream fixed so the stochastic engines are comparable. The numerical
    `dmiv` never had the defect and is untouched.

    A new regression test asserts that every categorical engine honours
    `max_bins` on a high-cardinality feature, at two values of `bin_cutoff`
    and two of `max_bins`.

*   `?ob_categorical_dmiv` described `convergence_threshold` as stopping the
    merging. It records convergence; `max_bins` is a hard constraint and
    merging continues until the bin count meets it.

*   `find_most_similar_bins()` now seeds its best pair with the first mergeable
    one instead of `{0, 0}`. The search only replaces that seed on a strictly
    smaller divergence, so a distance matrix that was entirely `double::max` --
    or that held a `NaN`, against which every comparison is false -- would have
    returned a pair naming the same bin twice, and merging a bin with itself
    then erasing the duplicate would have dropped its observations. No input
    reaching that state was found, so this closes a defensive gap rather than a
    demonstrated defect.

### Fixed: categorical `mba` read past the end of its bin vector

*   **`ob_categorical_mba()` performed an out-of-range read while reducing the
    pre-bins.** The list of candidate bins to merge is built before any merging
    starts, and every merge erases a bin, so an index taken later in that list
    could point past the end of the shrunken vector. The existing guard only
    clamped the indices passed to the merge itself, not the read that selects a
    merge partner.

    This is undefined behaviour, not a wrong number: under a checked standard
    library it aborts the R session, and without one it reads foreign memory and
    carries on. Reproduced with 60 categories, `max_n_prebins = 20` and
    `n = 60,000`, at either the default `bin_cutoff` or a smaller one.

    Stale indices are now skipped. The guard fires only where the previous code
    was already out of range, so results are **byte-identical** on every input
    that worked before — verified across all 16 categorical engines and 13
    variables of the bundled German Credit data. `mba` rejoins the regression
    test that asserts every categorical engine's bins account for every
    observation.

### Fixed: categorical `ivb` and `gmb` dropped observations

*   **`ob_categorical_ivb()` and `ob_categorical_gmb()` discarded the categories
    that did not fit within `max_n_prebins` instead of pooling them**, so those
    observations left the binning entirely. The vector of bins was resized, and
    everything past the cap was destroyed along with its counts. Nothing
    signalled it: no warning, `error = FALSE`, `converged = TRUE`, and a
    `total_iv` reported as if it described the whole sample.

    Default settings hide the defect — `bin_cutoff = 0.05` lets at most 20
    categories survive the rare-category merge, which is exactly the default
    `max_n_prebins` — but a smaller cutoff reaches it. With 60 levels and
    `bin_cutoff = 0.005`, both engines lost **39,306 of 60,000 rows (65.5%)**;
    `dp` and `jedi` accounted for every row on the same input.

    Both now fold the excess into the smallest retained bin
    (`src/OBC_IVB_v5.cpp`, `src/OBC_GMB_v5.cpp`). Which categories are kept as
    separate identities is unchanged — still the `max_n_prebins` most frequent —
    so results on the path where the cap never bound are **byte-identical** to
    the previous release: verified across all 16 categorical engines and 13
    variables of the bundled German Credit data, 208 combinations, with the RNG
    stream fixed so the stochastic engines are comparable.

    A new regression test asserts that every categorical engine's bins account
    for every observation.

### Documentation

*   **New vignette, `Algorithm Reference: the 37 Binning Engines`.** Reference
    documentation for all 28 algorithms across their 37 algorithm/feature-type
    combinations, written from a reading of the C++ implementation of every
    engine and a check of that reading against the literature each one invokes.
    Entries are organised by the mechanism the code actually uses — exact
    dynamic programming, recursive entropy partitioning, statistical merging,
    isotonic regression, density estimation, divergence, streaming,
    metaheuristic, and greedy IV merging — which groups the engines differently
    from their names.

    Two rules governed it: a claim appears only if it traces to a specific line
    of the shipped source or to a publication verified to exist, and the name is
    never taken as evidence of the mechanism. Where neither could be
    established, the vignette says so — it closes with a section listing what
    could **not** be verified, including the provenance of the Information Value
    interpretation bands and whether Kerber's ChiMerge uses the continuity
    correction this package implements.

    Substantive findings it documents: **`obwoe()` does not forward
    algorithm-specific parameters** and drops them silently, demonstrated live;
    **`mdlp` does not implement the Fayyad–Irani criterion it cites** while
    `fast_mdlp` does; **`fetb` computes a hypergeometric point probability, not
    a Fisher exact-test p-value**; **`bb` neither branches nor bounds** and
    numerical `dp` builds no dynamic-programming table, while the package's only
    real DP sits inside `sketch` behind an `n <= 50` threshold; the numerical
    `sketch` **departs from the KLL construction it cites** in both compactor
    capacity and compaction rule, so the quoted error bound does not transfer;
    and `dmiv`'s default `bin_method = "woe1"` is a per-bin log-odds rather
    than standard WoE.

    It also records parameters that are accepted and never read
    (`polynomial_degree` in `lpdb`; `max_n_prebins` in `fast_mdlp`, numerical
    `sketch`, categorical `fetb` and `sab`), `convergence_threshold` being inert
    in five numerical engines, and three citation errors in the shipped
    documentation.


*   **`max_n_prebins` is documented as the modelling decision it is.** For
    numerical features, pre-binning runs before any algorithm sees the data, so
    the default of 20 quantile cells can smear a heavy tail and lose the signal
    in it before optimization begins -- silently, with `converged = TRUE`.
    Benchmarks on two open datasets (76,020 x 369 and 590,540 x 454, five-fold
    held-out IV) move the median held-out IV of heavy-tailed numerical
    predictors by +129% and +39% when the parameter is raised from 20 to 200,
    with the bin count essentially unchanged.

    The default is deliberately **not** changed. The same experiment shows the
    effect runs both ways: on the larger benchmark, raising it cost 15% of
    held-out IV on the twenty-five strongest predictors while inflating IV on
    weak ones, and no cheap rule separated the two cases. `?control.obwoe` now
    says so, and recommends per-variable tuning against held-out data.

*   **`min_bins` is documented as frequently binding.** It reads as a safety
    floor but often sets the partition, because several algorithms stop merging
    on their own criterion well before `max_bins`. On German Credit `amount`
    with `max_bins = 5`, the default returns two bins and an IV of 0.0016 while
    `min_bins = 4` returns 0.0824 -- same data, same algorithm.

*   **Corrected the 1.13.0 entry on binary size.** It described `-Os`,
    `-fvisibility=hidden`, `-ffunction-sections`, `-Wl,--gc-sections` and a
    `cleanup` script, none of which are in the package: the visibility and
    section flags hid Rcpp symbols and broke the build on every platform and
    were reverted in `b74e95b`. The entry is corrected rather than deleted so
    the flags are not reinstated by someone reading the old claim.

### Smaller items

*   **An unmeasured IV is no longer reported as an IV of zero.** With no finite
    IV anywhere, `summary()` printed `Total IV: 0.0000` and
    `IV Range: [Inf, -Inf]`, asserting "no predictive power" about features
    that were never measured; `print()` on a `step_obwoe` recipe printed
    `total IV=0.0000`. Both report `NA` with a note. `obwoe_gains()` failed
    with `attempt to select less than one element in get1index` and
    `plot(type = "iv")` with `need finite 'xlim' values`; both now say what is
    actually wrong.

*   **`sketch` (numerical) returns a `bin` label for a constant feature**, so
    `summary$n_bins` is `1` instead of `NA`.

*   **`udt` (numerical) keeps WoE and IV finite with `laplace_smoothing = 0`.**
    A bin holding no events gave `woe = -Inf` and `iv = Inf`; both
    distributions are now floored with the package-wide epsilon, so the
    parameter stays usable. Reachable only through `ob_numerical_udt()`
    directly -- `obwoe()` never forwards the argument.

# OptimalBinningWoE 1.13.2

## The fitted object now records how it was fitted

*   **`obwoe()` returns the `control` it used**, alongside the effective
    `min_bins`, `max_bins` and `algorithm`. `call` was never a substitute: it
    records only the arguments the caller typed, never the defaults that
    actually applied, so a saved model could not say what produced it.

*   **A custom `bin_separator` now works end to end.** Everything that has to
    split grouped categories out of a bin label reads the separator from the
    model instead of assuming the package default: `obwoe_apply()`, the points
    table, the points SQL and the WoE SQL emitted into the workbook.

    This was a silent corruption, not a cosmetic gap. Fitting with
    `control.obwoe(bin_separator = "||")` and applying the model back to its
    own training data put **754 of 900 rows** on the `na_woe` fallback,
    because the labels were split on `"%;%"` and never matched. The generated
    SQL was worse: `a||e||c` came out as
    `g IN ('a', '|', '|', 'e', '|', '|', 'c')` -- broken SQL, no warning.
    Both are exact now.

*   **Models saved by earlier versions keep working.** They carry no `control`
    element, so the separator falls back to the package default rather than
    erroring on a missing field.

Nothing changes for the default configuration: `"%;%"` remains the default
everywhere, so only the custom-separator path -- which was broken -- behaves
differently.

# OptimalBinningWoE 1.13.1

## Audit fixes (2026-08-20)

Bug-fix release addressing an internal code audit of 1.13.0. Every item below
either changes a computed value that was previously wrong, or removes an API
surface the author never intended to publish. See the pull request for the
full item-by-item breakdown.

### API changes

*   `fit_logistic_regression()` is no longer exported. It is still used
    internally (renamed to `.ob_fit_logistic_regression()`) to implement
    `engine = "obwoe"` in `obwoe_scorecard()`, which is unaffected.

*   `ob_gains_table()` and `ob_gains_table_feature()` are renamed to
    `obwoe_gains_score()` and `obwoe_gains_variable()` respectively, to sit
    alongside the rest of the `obwoe_*` family. Behavior is unchanged; only
    the names moved. `obwoe_gains()` (the higher-level, plot-producing
    function) is a different function and was not renamed.

### Behavior changes (read before upgrading)

*   **`obwoe_apply()` now routes a missing categorical value to the fitted
    "missing" bin's WoE when the binning built one, instead of always using
    `na_woe`.** Previously `obwoe_apply()` ignored any missing-value bin
    learned during training and always returned `na_woe` for `NA`, while the
    generated deployment SQL (`obwoe_sql()`, `null_to_na_bin = TRUE` by
    default) routed `IS NULL` to that bin's WoE — so R and the SQL scored the
    same missing value differently. `na_woe` is now only a fallback for
    variables where no missing-value bin exists. If you fit a model with real
    `NA`s in a categorical predictor, the WoE `obwoe_apply()`/`predict()`
    return for `NA` may change in this version; it now matches the SQL and
    the bin actually fitted. See `?obwoe_apply` and `?obwoe_sql`.

*   **`obwoe_scorecard(..., drop_negative = TRUE)` now actually removes a
    single negative-coefficient variable instead of silently keeping it.**
    Previously the removal loop stopped one variable too early whenever
    exactly one variable had a negative coefficient, so that case fell
    through to a warning instead of either fixing the model or raising
    `stop()`. It now keeps removing negative-coefficient variables until
    either none remain or only one variable is left (in which case, if it is
    still negative, `obwoe_scorecard()` stops with an error, as documented).

*   **`ob_cutpoints_num()`'s bins are now right-closed `(a, b]`, and both
    `ob_cutpoints_num()` and `ob_cutpoints_cat()` return the pieces
    (`id`, and `cutpoints` for the numerical version) `ob_apply_woe_num()`/
    `ob_apply_woe_cat()` require.** Previously `ob_cutpoints_num()` built
    left-closed `[a, b)` bins — the opposite of `ob_apply_woe_num()`'s
    `include_upper_bound = TRUE` default — so a value sitting exactly on a
    cutpoint could get a different, often sign-flipped, WoE depending on
    whether it went through the fit or the apply side; and neither manual
    cutpoint function's result could be handed to its matching apply
    function at all, because both lacked the `id` element (and
    `ob_cutpoints_num()` the top-level `cutpoints`) the apply side requires.
    `ob_cutpoints_cat()`'s emitted bin labels also now use `"%;%"` (matching
    `ob_apply_woe_cat()`'s default separator and the main pipeline) instead
    of echoing the `"+"`-joined input verbatim; the `"+"` input format is
    unchanged. If you use `ob_cutpoints_num()`/`ob_cutpoints_cat()` and parse
    their `bin` labels yourself, check the new format. See
    `?ob_cutpoints_num` and `?ob_cutpoints_cat`.

### Fixes

*   Gains tables sorted by `sort_by = "bin"` (including the score bands in
    `obwoe_scorecard()`) were ordered lexicographically instead of by the
    bins' natural (level) order, silently understating KS by as much as a
    third on real data.
*   `step_obwoe(algorithm = "auto")` could resolve to the multiclass
    algorithm for a genuinely binary outcome whenever the outcome factor
    declared an unobserved third level.
*   `print()` on an unprepped `step_obwoe` recipe step errored when
    `algorithm = tune::tune()`.
*   `obwoe_gains(use_column = "woe", n_groups = k)` returned `NA` WoE and
    zero total IV for every bin.
*   `plot.obwoe_gains(type = "cumulative")` assumed every bin held an equal
    share of the population; the x-axis now reflects each bin's real size.
*   The advertised algorithm count was corrected from "36 (20 numerical, 16
    categorical)" to the actual 37 (21 numerical, 16 categorical) — the
    numerical `ir` algorithm was implemented, documented and tested, but was
    never counted.

# OptimalBinningWoE 1.13.0

## New features (2026-08-20)

### `obwoe_scorecard()` — the pipeline as one artefact

Runs the whole origination workflow in a single call — stratified split,
binning, screening by Information Value and correlation, model fitting, PDO
scaling — and returns an object that also writes itself out as an `.xlsx` model
document. The point is not convenience: each stage records why it did what it
did, so the workbook is reviewable evidence rather than a set of numbers.

*   **The binning sees the training rows only.** Binning is supervised: cut
    points and WoE are both chosen against the target, so fitting them on the
    full base before splitting leaks the hold-out into the transformation. The
    split therefore happens first, and the binning is fitted once, on the
    training rows, and applied everywhere else. Measured on German Credit, the
    difference is a hold-out AUC of 0.768 fitted the leaky way against 0.709
    fitted correctly — the leak buys six points of AUC that do not exist.

*   **A negative WoE coefficient is a fault, not a result.** The WoE already
    carries the direction of risk, so a negative slope means the model is
    reversing a variable to compensate for another. Such variables are dropped
    one at a time, worst first, and the model refitted, with each removal
    recorded.

*   **The points table is fixed, not per applicant.** Points follow the Siddiqi
    allocation, `points_ij = Offset/k - Factor(beta_j WoE_ij + alpha/k)`, so a
    bin is worth the same number of points to everyone. Rounding each bin once
    costs at most `k/2` points against the unrounded model score; that drift is
    measured on every sample and reported rather than hidden by re-apportioning
    the rounding per row, which would make the same bin worth different points
    to different applicants.

*   **The deployment SQL reproduces the R score exactly.** Both the WoE form and
    the integer-points form are generated, the total computed in an outer
    `SELECT` over a subquery — referring to a select-list alias within the same
    `SELECT` is a MySQL extension that ANSI SQL, SQLite, PostgreSQL, SQL Server
    and Oracle all reject. Verified against live SQLite on all 1000 German
    Credit rows: maximum difference 0, unseen categories included.

*   **A value in no fitted bin scores a defined fallback.** It is counted and
    warned about, per sample and per variable, but it still produces a score —
    both in R and in SQL, from one and the same `na_woe` figure. A single unseen
    category cannot void an application.

*   **Thirteen sheets.** Model summary, scorecard, coefficients with standard
    errors, bin statistics, screening funnel with a reason per rejected
    variable, correlations before and after pruning, score gains, PSI between
    samples, cut-off strategy, SQL in both forms, and a reproducibility record.

*   **Engines are a three-function contract** — `fit`, `link`, `coef` — with
    `glm`, the package's own C++ L-BFGS logistic regression, and `glmnet`
    registered, and custom engines accepted. `coef()` returning `NULL` declares
    the model non-additive, and the pipeline then produces no points table
    rather than fabricating one from a model that has no per-variable
    decomposition. A missing engine package is an error by default, not a
    silent substitution: a workbook documenting a model the analyst did not ask
    for is worse than a call that fails.

### Supporting functions

*   `obwoe_scale()` and `obwoe_score()` implement PDO scaling,
    `Factor = PDO/ln 2` and `Offset = Score0 - Factor ln(Odds0)`, in both score
    directions. Doubling the good:bad odds moves the score by exactly one PDO
    at every point of the range.
*   `obwoe_prune()` removes redundant variables iteratively, dropping the
    worse-ranked member of the strongest surviving pair and recomputing, rather
    than resolving all pairs against the original matrix at once.
*   `obwoe_psi()` computes the Population Stability Index between two samples,
    for both binned and continuous inputs, using interior quantiles so that a
    merely shifted distribution does not produce a degenerate band.
*   `obwoe_report()` writes the workbook for an already-fitted scorecard.
*   `predict()` methods for `"score"`, `"card"`, `"link"`, `"prob"` and
    `"woe"`, reading only the binning, the coefficients and the scaling.

### Fixed during review

*   **The screening funnel named the wrong stage.** Every variable that left
    the pipeline after Information Value screening was labelled
    `corr_pruned`, including those dropped by the negative-coefficient check
    and those that turned out constant after the WoE transform. The funnel is
    the sheet a reviewer reads to learn *why* a variable is absent, so a
    mislabelled rejection is a false statement in a model document. `stage`
    now distinguishes `in_model`, `sign_rejected`, `corr_pruned`,
    `constant_woe` and `screened_out`, each assigned by the step that actually
    rejected the variable.

*   **The cut-off strategy sheet ignored the score direction.** It always
    approved applicants scoring at or above the cut, which is right under the
    default scale but exactly inverted under
    `direction = "higher_is_riskier"` — the sheet then approved the worst
    applicants and reported an approved bad rate above the rejected one, at
    every one of its twenty cut-offs. The approval rule now follows the
    direction of the scale.

*   **`file` is validated before the pipeline runs.** A missing `openxlsx`, a
    non-existent directory or an unwritable one used to surface only at the
    final write, after the binning, the screening and the fit had all been
    computed and were about to be discarded. The check now happens up front:
    the failure arrives in about 0.04 s instead of after the whole run.

## Dependencies

`openxlsx` and `glmnet` are added to `Suggests`. Neither is needed unless a
workbook is written or `engine = "glmnet"` is requested.

# OptimalBinningWoE 1.12.0

## New features (2026-08-19)

Two additions close the gap between a fitted binning and a deployed scorecard:
deciding which variables deserve to enter the model, and shipping the accepted
transformation to the database where the data lives.

### `obwoe_select()` — automated variable screening

Screens every binned variable of an `obwoe` model against the two criteria that
govern variable admission in credit risk practice — **predictive strength**,
graded with the Siddiqi (2006) Information Value bands, and **guaranteed rank
ordering**, measured by monotonicity of the bin event rate — and returns a
verdict for each.

*   **Nothing is ever dropped.** A base with 500 candidate variables yields 500
    rows. Each carries a `selected` flag, a machine-readable `reason` listing
    *every* rule it violated, and a `reason_desc` in plain language, so the
    automatic verdict can be reviewed, overridden, or replaced by the analyst's
    own triage.

*   **Two levels of detail.** `detail = "summary"` gives one row per variable
    with its headline metrics; `detail = "full"` expands to one row per variable
    *and* optimised bin, carrying the complete gains table of
    `ob_gains_table()` (31 metrics: WoE, IV, lift, cumulative KS, precision,
    recall, F1, KL and JS divergence, ...) together with the interval bounds of
    numerical bins and the merged category lists of categorical ones.

*   **Metrics that describe the deployed score.** `ks`, `auc` and `gini` are
    computed on the WoE actually applied to the data, with bins ranked by that
    WoE and merged when tied. `auc` uses the tie-corrected Mann-Whitney form,
    which reproduces the rank-based AUC of the WoE-transformed observations to
    machine precision. `ks` is therefore the true KS of the transformed
    variable even when the binning is not monotonic.

*   **An honest treatment of ordering.** For numerical variables the bin
    sequence is intrinsic, so monotonicity is a genuine constraint. For nominal
    categories the sequence is a free relabelling, so the default
    `require_monotonic = "numeric"` applies the constraint only where it means
    something; `"all"` and `"none"` are available.

*   **Rejects suspicious variables by default.** `iv_max = 0.50` excludes the
    Siddiqi *Suspicious* band, where a single-variable IV is far more often a
    symptom of target leakage than of a dominant predictor. On the Statlog
    German Credit benchmark this is exactly what happens to the
    checking-account status (IV = 0.67).

*   Further gates for `min_bins`, `max_bins`, minimum bin population share,
    bins with no events or no non-events, and a `top_n` cut by IV, KS, Gini or
    AUC. An `Excellent`/`Good`/`Fair`/`Rejected` quality tier summarises how
    cleanly the algorithm categorised each variable.

*   Returns a `data.table` when that package is installed and an identical
    `data.frame` otherwise; the table is assembled column-wise in a single
    pass, so 500 variables are screened in about a second.

### `obwoe_sql()` — SQL code generation

Translates a fitted binning into executable SQL, so the WoE transformation runs
inside the database with no round trip through R.

*   **Exact interval semantics.** Numerical bins are half-open on the right,
    `(lower, upper]`, exactly as `obwoe_apply()` assigns them. Boundaries come
    from the fitted `cutpoints` vector, never parsed back from bin labels,
    whose formatting varies between algorithms. By default each branch states
    both of its bounds (`WHEN x > 7 AND x <= 10 THEN ...`) so it is correct in
    isolation; `explicit_bounds = FALSE` emits the shorter cascading form.

*   **Cut points that survive the round trip.** Literals are written as the
    shortest fixed-notation decimal that parses back to the identical IEEE 754
    double, and never in scientific notation. Rounding a boundary such as
    `4049.5` would silently move observations between bins, so exactness is the
    default; `digits` makes rounding an explicit choice.

*   **NULL cannot leak.** In SQL, `NULL <= 5` is `NULL`, not `FALSE`, so a
    missing value matches no comparison and would fall through to `ELSE`. Every
    generated expression opens with an explicit `WHEN <col> IS NULL` branch,
    and when the binner folded training missings into a category bin,
    `null_to_na_bin = TRUE` routes database `NULL`s to that same bin.

*   **Escaping that holds.** Single quotes are doubled per ANSI SQL; on MySQL,
    MariaDB and the Hive family backslashes are doubled too. Category names are
    matched byte for byte, whitespace included. Identifiers are quoted with the
    dialect's own delimiters and, by default, only when the name needs it or
    collides with a reserved word — which keeps generated code readable on
    case-folding engines such as Oracle and Snowflake.

*   14 dialects (`ansi`, `postgres`, `mysql`, `mariadb`, `sqlserver`, `oracle`,
    `spark`, `hive`, `databricks`, `bigquery`, `snowflake`, `redshift`,
    `duckdb`, `sqlite`), four assembly styles (`select`, `case`, `cte`,
    `view`), four output modes (`woe`, `bin`, `index`, `both`), an audit header
    recording package version and algorithm, and direct file output.

*   Accepts an `obwoe` object, a prepped `step_obwoe()` step, or a prepped
    recipe containing one, and composes with `obwoe_select()` through
    `features = sel$feature[sel$selected]`.

### Validation

*   **Real benchmark data is bundled.** `inst/extdata/germancredit.csv.gz`
    holds the Statlog (German Credit) dataset from the UCI Machine Learning
    Repository (1000 applications, 7 numerical and 13 categorical attributes,
    CC BY 4.0), in its labelled form — category names carrying spaces, slashes
    and colons make it a realistic test bed for SQL escaping.

*   `obwoe_select()` reproduces the **published Information Values** of that
    benchmark to four decimals for the 14 attributes the optimiser keeps
    ungrouped, and its IV, KS, AUC and Gini match an independent computation
    made from the raw observations to 1e-10.

*   The generated SQL is validated by **parsing and executing it**: the test
    suite carries a small `CASE` interpreter that reads the emitted text the
    way an engine would, and the resulting bin assignment is checked against
    the counts the binning algorithm itself reported — across five algorithms
    and all 20 German Credit variables. During development the same statements
    were additionally run against a live SQLite engine, whose results were
    identical. Edge cases covered include observations sitting exactly on a cut
    point, cut points with no exact binary representation, magnitudes from
    `1e-9` to `1e9`, single-bin variables, degenerate bins, reserved-word
    column names, and categories containing quotes, backslashes, tabs,
    accented characters and significant whitespace.

## Bug fixes

Both defects below were uncovered while validating the two new functions
against real data, and both are pinned by tests in
`tests/testthat/test-regression-audit.R` that fail on 1.11.0 — 31 assertions
in total.

*   **`obwoe_apply()` and `bake.step_obwoe()` silently failed to score any
    category carrying leading or trailing whitespace.** Both rebuilt their
    category-to-bin lookup with
    `trimws(strsplit(bin_label, "%;%", fixed = TRUE)[[1]])`. The binning
    engines join the original category strings with the separator and add no
    padding — verified here for all 15 categorical algorithms — so the split
    pieces are already the categories byte for byte, and the trimming turned a
    category such as `"  N/A  "` or `"PENDING "` into a key no observation
    could match. Those rows were assigned `bin = NA` and `woe = na_woe`, i.e.
    scored as unseen, with no warning: on a base whose codes come from a
    `CHAR(n)` column or a hand-maintained code table, an entire segment could
    be dropped from the model without a trace. The trimming is gone; categories
    are now matched exactly, as `obwoe_sql()` already did.

*   **`obwoe_gains()` reported the same lift for every bin.** The column was
    built with `ifelse(overall_rate > 0, df$pos_rate / overall_rate, 0)`;
    `ifelse()` returns a result shaped like its *test*, and that test is a
    scalar, so the expression collapsed to a length-one vector that R then
    recycled across the table. Every gains table with more than one bin
    reported the first bin's lift throughout, and `plot(type = "lift")` drew a
    flat line. The R gains table now agrees with the independent C++ engine
    (`ob_gains_table()`) to machine precision on `lift`, `woe`, `iv` and `ks`.

*   **`ob_numerical_ir()` reported counts that did not describe its own bins,
    and bins that were not monotonic.** `applyIsotonicRegression()` ran the
    Pool Adjacent Violators algorithm over the bin event rates and then
    overwrote each bin with `count_pos <- round(fitted_rate * count)`. Pooling
    adjacent violators means those bins form *one* block; keeping them separate
    and back-solving synthetic counts produced two defects at once. First,
    `count_pos` and `count_neg` no longer described the observations falling
    between the reported `cutpoints`, so WoE, IV, KS and every gains table
    derived from them referred to a distribution that does not exist — on the
    German Credit `amount` attribute the reported and observed event rates
    differed in four of six bins. Second, because of the rounding the reported
    bins were not even monotonic, which is the one property an isotonic binner
    exists to guarantee.

    PAVA blocks are now merged into single bins. This reproduces the isotonic
    fit exactly — with the bin counts as weights, a block's pooled rate *is*
    `sum(count_pos) / sum(count)` over that block — while `count`, `count_pos`
    and `count_neg` stay equal to what was observed. Features whose event rate
    is already monotone are unaffected and return bit-identical results; where
    PAVA had violators to pool, the binning is now coarser and genuinely
    rank-ordering. `min_bins` becomes a target rather than a guarantee for this
    algorithm, since monotonicity cannot always be attained at that resolution.

## Documentation

*   **The README is a third of its former length.** It now covers what the
    package is, the four steps of a run, how to choose an algorithm and where
    to read more; the extended worked examples moved into the vignettes, where
    an analyst can study them properly. Algorithm selection is presented as a
    decision graph (rendered by GitHub) with the family reference table kept
    below it.

*   **Two vignettes replace the single kitchen-sink one.**

    *Optimal Binning and Weight of Evidence: A Practical Guide* is the working
    reference: what WoE and IV measure and why monotonicity of the event rate
    and of the WoE are the same statement, reading bin and gains tables,
    screening a base with `obwoe_select()`, how the algorithm families differ,
    applying the transformation, exporting SQL, and preprocessing.

    *An Industrial Scorecard Pipeline* is new and runs an origination scorecard
    end to end: a wide synthetic base with the pathologies that matter (missing
    fields, rare dealer codes, near-duplicate vendor variables, pure noise and
    a leaky post-booking field), screening at scale, redundancy pruning with
    `obcorr()` in the WoE space, a `recipes` pipeline around `step_obwoe()`,
    logistic regression with the coefficient sign check, PDO scorecard points,
    out-of-time validation with gains and PSI, deployment through
    `obwoe_sql()`, tuning with tidymodels, and a governance checklist.

*   Both vignettes run on the bundled German Credit benchmark or on data they
    generate, so they build from `recipes` alone. The previous vignette
    required the Suggested `scorecard` package and failed to build without it;
    `scorecard` and `pROC` are no longer used anywhere and have been dropped
    from `Suggests`.

## Notes

*   `data.table` is used when installed, for `rbindlist()` and for the returned
    table type, and is declared in `Suggests` only: the package has no new hard
    dependency and behaves identically without it, as the test suite verifies.

# OptimalBinningWoE 1.11.0

## C++ Engine — Runtime Audit (2026-08-12)

Follow-up to the 1.10.0 static audit, this time driven by instrumented builds
(`-fsanitize=address,undefined`), a degenerate-input stress harness, and a
golden-output regression suite covering all 37 exported algorithms
(~3,200 result comparisons). All fixes below are covered by new tests in
`tests/testthat/test-regression-audit.R`, each of which fails on 1.10.0.

### Bug Fixes — crashes and hangs

*   **`ob_categorical_ivb` crashed the R session** (segmentation fault, not a
    catchable error) for **any feature with no more categories than
    `max_bins`** — with the default `max_bins = 5` this meant every 2-, 3-, 4-
    or 5-level predictor, i.e. sex, marital status, region, education. In
    `perform_binning()` the `ncat <= max_bins` fast path skipped
    `initialize_dp_structures()`, the only place `stats_cache` was created,
    while result assembly dereferenced it unconditionally. The cache is now
    built before the branch.

*   **`ob_numerical_ir`, `ob_numerical_jedi`, `ob_numerical_jedi_mwoe` hung
    forever** whenever `min_bins` exceeded the number of distinct feature
    values — a routine situation when one `min_bins` is applied across a whole
    feature set. The "ensure at least `min_bins`" loops called a split routine
    that silently declines to split unbounded intervals (and, in JEDI, ran
    before counts existed, so it always targeted the unsplittable `(-Inf, e1]`
    bin), making no progress and never terminating. Since these loops contained
    no `R_CheckUserInterrupt()`, the hang could not even be interrupted with
    Ctrl-C. The loops now consider only splittable bins and stop as soon as no
    progress is possible.

### Reproducibility

*   **`ob_categorical_sab` is now reproducible under `set.seed()`.** It was
    seeded from `std::random_device`, so identical input returned a different
    binning on every call with no way to control it — unusable for auditable
    or regulated models, and unreliable on some MinGW toolchains. The
    simulated-annealing search is now seeded from R's own RNG stream.
    **This changes `ob_categorical_sab` results**, which were previously
    random and therefore had no stable baseline to preserve.

### Output consistency

*   **`ob_numerical_sketch` now returns the `bin` label field** (plus
    `total_iv`) like every other numerical algorithm. Its absence broke
    generic consumers, including this package's own test helper. `bin_lower`
    and `bin_upper` are retained, so the change is purely additive; no numeric
    output changed.

*   The `ob_numerical_sketch` test disabled since 1.0.7 for a segfault has been
    **re-enabled**: the underlying `MergeCache` defect was removed in an earlier
    round, and the case was re-verified clean under
    `-fsanitize=address,undefined` at n = 500/1000/2000/5000.

### Parallelism (`obcorr`) — CRAN policy and determinism

*   **`obcorr()` no longer seizes every core.** With the default `threads = 0`
    it called `omp_set_num_threads(std::thread::hardware_concurrency())`,
    taking all available cores. CRAN Repository Policy requires a package never
    to use more than two cores simultaneously by default, since the check farm
    is a shared resource; this was an archival risk. The default is now at most
    2 threads, honouring any lower limit already set through `OMP_NUM_THREADS`,
    and capped by `omp_get_num_procs()`. An explicit positive `threads` is
    still respected.

*   **`obcorr()` returned its rows in a non-deterministic order.** Per-thread
    result buffers were spliced together inside an `omp critical` block, so the
    row order depended on thread scheduling — two runs on the same data with
    the same thread count could return the pairs in different orders. Any
    caller using `head()`, positional indexing, or a positional join saw
    different numbers between runs. Each iteration now writes to its own slot
    in a pre-sized vector, which is deterministic, independent of the thread
    count, and removes the critical section. Correlation *values* are
    unchanged; only the row order is now stable.

### Build hygiene

*   Removed eight stale Dropbox conflict copies (`*Cópia em conflito*.cpp/.h`)
    from `src/`. `R CMD build` would have shipped and compiled them, producing
    duplicate symbols. Guards added to `.gitignore` and `.Rbuildignore`.

### Interval convention — standardised on `(a, b]` (changes numeric output)

The package previously disagreed with itself about what a bin *is*. Bin labels
advertised `(a;b]`, but many algorithms assigned observations as `[a;b)`;
`ob_apply_woe_num()` had its two binary searches swapped, so *both* settings of
`include_upper_bound` did the opposite of what the argument documents; and
several equal-frequency pre-binners split runs of tied values, so their reported
counts could not be derived from their own reported cutpoints under any
convention. A value landing exactly on a cutpoint — routine for integer, rounded
or currency features — could be scored into a different bin than the one it was
trained in.

Measured across 63 algorithm/dataset combinations, **31 violated the documented
`(a, b]` convention before this release; 0 do now.**

*   `ob_apply_woe_num()`: `include_upper_bound = TRUE` now really means `(a, b]`
    (`lower_bound` search) and `FALSE` really means `[a, b)` (`upper_bound`).
    The two were previously exchanged.
*   `ob_numerical_dp`, `_fetb`, `_ldb`, `_oslp`: bin lookup switched from
    `upper_bound` to `lower_bound`, so boundary values stay in the bin below.
*   `ob_numerical_bb`, `_dmiv`: dropped an `+ EPSILON` added to the search key,
    which silently turned the documented `upper >= value` test into a strict one.
*   `ob_numerical_sketch`: interval test changed from `[lower, upper)` to
    `(lower, upper]` (first bin remains closed on the left, so the minimum is
    included).
*   `ob_numerical_mob`, `_mdlp`, `_mrblp`: equal-frequency pre-binning is now
    tie-aware — a run of identical values is never split across two pre-bins —
    and boundaries are the last value *in* the bin rather than the first value
    of the next. Their labels changed from `[a;b)` to `(a;b]` to match.
*   `ob_numerical_fast_mdlp`: the `force_min_bins()` fallback picked a split at
    a raw index midpoint, which could land inside a run of tied observations.
    It now moves the split to a genuine value boundary, as the MDL recursion
    already did.

**`ob_numerical_cm` silently discarded observations.** Its equal-frequency
pre-binner, on hitting a tie that straddled a bin boundary, advanced past the
tied records without ever assigning them to a bin. On tied or discrete features
this dropped a large share of the data — 26% on an integer feature and 40% on a
coarse one in testing — and the reported WoE/IV were computed from the surviving
subset. The tied records are now absorbed into the preceding bin.

### Known issues (not yet fixed)

*   `ob_categorical_dp` and `ob_categorical_fetb` report `WoE = IV = 0` for
    perfectly separating bins instead of applying smoothing.
*   Return fields are not uniform across algorithms: `event_rate` is present in
    only 13 of 37, `total_iv` in 28 of 37, and `iv` is missing from both
    `_dmiv` variants.
*   `ob_apply_woe_num()` does not support the multinomial `*_jedi_mwoe`
    variants, whose output carries per-class counts rather than `count_pos` /
    `count_neg`.

# OptimalBinningWoE 1.10.0

## C++ Engine — Comprehensive Audit & Hardening (2026-05-17)

This release is the result of a full static audit of the C++ engine covering
all 36 binning algorithms. No public R API was changed.

### Bug Fixes

*   **`OB_LogisticRegression`** — Replaced exact `det != 0` singularity guard
    with a threshold-based check (`|det| > 1e-10 × ‖H‖`); replaced
    `hessian.inverse()` with `Eigen::LDLT` decomposition for numerical
    stability; added `.cwiseMax(0.0)` before `sqrt()` to prevent `NaN`
    standard errors from near-zero diagonal entries.
*   **`OBN_MDLP` — monotonicity direction bug** — `is_monotonic()` and
    `enforce_monotonicity()` previously hardcoded ascending direction, causing
    unnecessary merges on negatively-correlated features. Both now auto-detect
    the dominant trend via Welford's slope algorithm before
    checking/enforcing monotonicity.
*   **`OBC_DP` — DP backtracking out-of-bounds** — Added guard before
    `static_cast<size_t>(prev_j)` in `backtrack_optimal_bins()`; invalid
    predecessor index now raises a descriptive runtime error instead of
    silent undefined behaviour.
*   **`OBN_DP` — push before validate** — Target value validation in
    `optimal_binning_numerical_dp()` now fires before the value is appended to
    `target_vec`, preventing insertion of invalid data.
*   **`OBN_MDLP` — `log2(0)` in MDL cost** — Guard added for the single-bin
    case where `log2(k-1)` would evaluate to `log2(0) = -Inf`.
*   **`NumericalBin` constructor invariant** — The 7-arg constructor now
    derives `count = count_pos + count_neg` regardless of the `c` argument,
    enforcing the `count == total()` invariant at construction time.

### Performance Improvements

*   **`OBC_DP` — DP outer loop removed** — The deterministic DP in
    `perform_dynamic_programming()` was wrapped in a redundant
    `max_iterations` outer loop (default 1000). Removing it yields up to
    **1000× speedup** for the categorical DP algorithm.
*   **`OBC_DP::ensure_max_prebins()`** — O(m² log m) full re-sort per merge
    step replaced with O(m log m + m²) `std::lower_bound + insert`.
*   **`OBN_MDLP::apply_mdl_merging()`** — O(k³) full-vector copy per
    candidate merge eliminated; MDL delta is now computed analytically from
    bin statistics in O(k²) per outer step.
*   **`OBN_BB::quantile()`** — Per-call sort-copy O(n_prebins × n log n)
    eliminated; `prebinning()` sorts once and passes the sorted vector to a
    stateless `quantile()`.
*   **`monotonicity_utils.h` — Welford index allocation** — Removed
    unnecessary `std::vector<double> indices(n)` heap allocation; loop index
    cast directly to `double`.
*   **`OBN_DP` — Pearson correlation instability** — Replaced naive
    two-pass Pearson formula (catastrophic cancellation risk) with
    `detect_trend_from_correlation()` using Welford's online algorithm.

### CRAN / ODR Safety

*   **`safe_math.h`** — All 6 functions changed from `constexpr` to `inline`;
    `std::log`, `std::exp`, `std::abs` and `std::isfinite` are not guaranteed
    `constexpr` in C++11/14, risking compilation failure on SOLARIS/Studio.
*   **`chi_square_utils.h`** — `CHI_SQUARE_CRITICAL_VALUES` namespace-scope
    `const` replaced with a function returning a `static` local instance
    (one shared copy per process, C++11 thread-safe init).
*   **`entropy_utils.h`** — `ENTROPY_LUT` (~81 KB) replaced with
    `entropy_lut_instance()` returning a `static` local; eliminates one copy
    per translation unit.
*   **`OBC_CM_v5`** — Duplicate `ChiSquareCache` class (global namespace)
    removed; file now uses `OptimalBinning::ChiSquareCache` from
    `chi_square_utils.h`.
*   **35 `.cpp` files** — Duplicate `using namespace Rcpp` appearing before
    `#include "common/"` headers removed, preventing potential name-resolution
    ordering issues.

### Code Quality

*   **`OBC_DP`** — Dead commented-out code blocks (`// struct CategoryStats`,
    `// Local CategoricalBin definition removed`) deleted.
*   **`OBN_DP`** — Local variable `total_count` renamed to `rare_total` to
    fix shadowing of the class member with the same name.
*   **`OBN_IR`** — `[[Rcpp::plugins(cpp17)]]` standardised to `cpp11` for
    consistency with the rest of the package.
*   **`OBC_DP`** — Auto-detection of monotonicity direction (`monotonic_trend
    = "auto"`) implemented in `compute_and_sort_event_rates()` via
    `detect_trend_welford_woe()`.

---

# OptimalBinningWoE 1.0.9

*   **CRAN Fix (2026-03-14)** - Replaced `Rf_error` with `Rcpp::stop`:
    *   **Fixed C++ Exception Handling**: Addressed an issue reported by @Enchufa2 regarding the usage of `::Rf_error` inside `catch(...)` blocks. Updated all instances to use `Rcpp::stop` to ensure proper C++ stack unwinding and avoid memory leaks.
    *   **Affected Files**: `src/OBN_LPDB_v5.cpp`, `src/OBN_EWB_v5.cpp`, `src/OBN_KMB_v5.cpp`, `src/OBN_LDB_v5.cpp`, `src/OBN_MBLP_v5.cpp`.

# OptimalBinningWoE 1.0.8

*   **CRAN Fix (2026-01-28)** - LTO/ODR Compliance:
    *   **Fixed One Definition Rule (ODR) violations**: Wrapped internal helper classes `IVCache` and `CumulativeStatsCache` in anonymous namespaces within `OBC_GMB_v5.cpp`, `OBC_IVB_v5.cpp`, and `OBC_JEDI_v5.cpp`. This resolves Link-Time Optimization (LTO) warnings/errors on CRAN checks.

# OptimalBinningWoE 1.0.7

*   **UBSAN Investigation Fix (2026-01-27)** - Addressing persistent memory safety errors:

    *   **Temporarily disabled `ob_categorical_sketch` tests**: The sketch-based categorical binning 
        algorithm is under investigation for persistent UBSAN memory errors that appear to be 
        related to cache invalidation timing in GitHub Actions CI environment.

    *   **Removed `MergeCache` class from `OBC_Sketch_v5.cpp`**: Completely removed the caching 
        mechanism and implemented on-the-fly divergence calculation to eliminate potential 
        memory corruption sources.

*   **Affected Files**:
    *   `src/OBC_Sketch_v5.cpp`: MergeCache class removed, divergence calculated on-the-fly
    *   `tests/testthat/test-categorical-all.R`: Sketch tests temporarily commented out

*   **No API Changes**: Fully backward compatible with v1.0.6.

# OptimalBinningWoE 1.0.6

*   **CRAN Fix (2026-01-26)** - Resolving AddressSanitizer memory safety errors:

    *   **Fixed heap-buffer-overflow in `OBN_CM_v5.cpp`**: The `calculate_inconsistency_rate()` function 
        was accessing `bins[j-1]` when `j=0` and `bins.size()==1`, causing invalid memory access. 
        Restructured bin-finding loop to avoid negative index access.

    *   **Fixed uninitialized bool in `OBC_MBA_v5.cpp`**: The `MergeCache::enabled` member was not 
        explicitly initialized, causing "load of value 128, which is not a valid value for type 'bool'" 
        runtime error. Added explicit `bool enabled = false` initialization.

*   **Affected Files**:
    *   `src/OBN_CM_v5.cpp` (lines 863-887): Safe bin-finding logic
    *   `src/OBC_MBA_v5.cpp` (line 26): Explicit bool initialization

*   **No API Changes**: Fully backward compatible with v1.0.5.

# OptimalBinningWoE 1.0.5

*   **CRAN Fix (2026-01-25)** - Resolving ERROR on macOS platforms during vignette re-build:

    *   **Fixed `obwoe_apply()` "breaks are not unique" error**: Enhanced cutpoint deduplication logic to properly handle cases where `sort(unique(cutpoints))` reduces the number of intervals. When the deduplicated cutpoint count doesn't match the original bin count, the function now uses a fallback mapping with dynamically generated interval labels and mean WoE values, avoiding the `cut.default()` error.

    *   This addresses the vignette build failure reported on r-release-macos-arm64, r-release-macos-x86_64, r-oldrel-macos-arm64, and r-oldrel-macos-x86_64 platforms.

*   **Internal Changes**:
    *   Added interval count validation after cutpoint deduplication (R/obwoe.R)
    *   Fallback to mean WoE when bin/interval mismatch occurs
    *   Dynamic interval label generation for edge cases

# OptimalBinningWoE 1.0.4

*   **CRITICAL CRAN Fixes (2026-01-24)** - Addressing ERROR and NOTE on macOS platforms:

    *   **Fixed macOS vignette ERROR**: Added comprehensive validation for duplicate cutpoints in `obwoe_apply()` and `bake.step_obwoe()`. The R base `cut()` function now receives guaranteed unique, sorted breaks, preventing the `"'breaks' are not unique"` error that was causing vignette build failures on macOS platforms.

    *   **Attempted to reduce the package binary size** with size optimization
        flags (`-Os`, `-fvisibility=hidden`, `-ffunction-sections`,
        `-fdata-sections`), the `-Wl,--gc-sections` linker flag and a `cleanup`
        script for symbol stripping.

        **This was reverted and no longer describes the package.**
        `-fvisibility=hidden` and `-Wl,--gc-sections` hid Rcpp symbols and broke
        the build on every platform, so they were removed in commit `b74e95b`;
        `-Os` and the `cleanup` script did not survive either. `src/Makevars`
        now sets only the OpenMP and BLAS/LAPACK flags. The entry is corrected
        here rather than deleted so that nobody reinstates flags that are
        already known to break the build.

        The size itself is not a problem: the shared object is large only
        because it carries debug symbols. Measured on 1.13.3, `.debug_info`
        alone is 33Mb against 2.2Mb of `.text`, and stripping takes the library
        from 70.4Mb to 2.6Mb. `R CMD check --as-cran` reports the installed size
        as INFO, not as a NOTE.

*   **Internal Changes**:

    *   Added `src/common/cutpoints_validator.h` - new C++ utility header with `validate_cutpoints()` function to ensure cutpoint uniqueness across all numerical binning algorithms. Uses floating-point tolerance (1e-10) for safe duplicate detection.

    *   Modified `get_cutpoints()` in `src/OBN_MOB_v5.cpp` (line 180) to apply validation before returning cutpoints.

    *   Modified `update_cutpoints()` in `src/OBN_UBSD_v5.cpp` (line 874) to apply validation before storing cutpoints.

    *   Added R-level validation in `obwoe_apply()` (R/obwoe.R, line 1550): cutpoints are now sorted and deduplicated using `sort(unique(cutpoints))` before constructing breaks vector.

    *   Added R-level validation in `bake.step_obwoe()` (R/step_obwoe.R, line 789): same deduplication logic for recipes integration.

    *   Enhanced vignette robustness (`vignettes/introduction.Rmd`): Added try-catch error handling in scorecard workflow to prevent build failures on edge-case data distributions.

*   **Affected Algorithms**: All 21 numerical binning algorithms now validate cutpoints to prevent duplicate breaks:
    *   Monotonic Optimal Binning (MOB)
    *   Dynamic Programming (DP)
    *   Chi-Merge (CM)
    *   Unsupervised Binning with Standard Deviation (UBSD)
    *   And 17 other numerical algorithms

*   **No API Changes**: Fully backward compatible with v1.0.3. All existing code will continue to work without modification.

# OptimalBinningWoE 1.0.3

*   **Critical Bug Fixes - KLL Sketch Algorithm (2026-01-20)**:
    *   Fixed **iterator invalidation** in `KLLSketch::compact_level()` - the `compactors.push_back()` call was invalidating references to vector elements, causing crashes with datasets larger than ~200 observations.
    *   Fixed **parameter order bug** in `calculate_metrics()` calls - swapped `(total_good, total_bad)` to correct order `(total_pos, total_neg)`, fixing incorrect WoE calculations.
    *   Fixed **half-open interval logic** in bin assignment - added explicit closed interval `[lower, upper]` check for the last bin to ensure boundary values are correctly assigned.
    *   Fixed **merge direction logic** in `enforce_bin_cutoff()` - corrected iterator invalidation when merging bins by always erasing the higher-indexed bin.
    *   Added **bounds safety checks** in DP optimization - ensured `k >= 2` and `k < n` to prevent undefined behavior with edge cases.
    *   Added **underflow guard** in compaction loop - check for `compactor.size() < 2` before iteration.
    *   Added **input validation** for non-finite values (Inf, NaN) in sketch updates.
    *   Improved **documentation** in `ob_numerical_sketch()` with clearer parameter descriptions and simplified examples.
    *   Replaced `special_codes` parameter with `max_n_prebins` for consistency with other algorithms.

*   **CRAN Reviewer Feedback (2026-01-17)**:
    *   Removed single quotes from author names (`Siddiqi`, `Navas-Palencia`) in DESCRIPTION.
    *   Removed commented-out code from examples in `obwoe_apply`.
    *   Replaced all `\dontrun{}` with `\donttest{}` in 12 function examples.
    *   Added proper `par()` restoration in examples and vignettes.

# OptimalBinningWoE 1.0.2

*   **CRAN Resubmission**:
    *   Updated `inst/WORDLIST` to include technical terms and author names (MILP, Navas, Palencia) to resolve spelling notes.
    *   Fixed `README.md` links for `CONTRIBUTING.md` and `CODE_OF_CONDUCT.md` to use absolute GitHub URLs, ensuring compliance with CRAN URI checks for ignored files.
    *   Added `Language: en-US` to `DESCRIPTION` metadata.

# OptimalBinningWoE 1.0.1

*   **CRAN Preparation**: Comprehensive updates for CRAN submission compliance.
*   **Documentation**:
    *   Enhanced `README.Rmd` with detailed algorithm descriptions, `tidymodels` integration examples, and performance metrics.
    *   Added `CODE_OF_CONDUCT.md` (Contributor Covenant v2.1) and `CONTRIBUTING.md` guidelines.
    *   Added `inst/WORDLIST` for spell checking.
*   **Metadata**:
    *   Updated `DESCRIPTION` with corrected fields (Authors, BugReports, Depends, References).
    *   Added `cran-comments.md` for submission notes.

# OptimalBinningWoE 1.0.0

## Initial Release

**OptimalBinningWoE** is a high-performance R package for optimal binning and Weight of Evidence (WoE) transformation, designed for credit scoring and predictive modeling.

### Key Features

*   **Comprehensive Algorithm Suite**: Implementation of 36 binning algorithms:
    *   **20 Numerical Algorithms**: Including MDLP (Minimum Description Length Principle), JEDI (Joint Entropy-Driven Information), MOB (Monotonic Optimal Binning), Sketch (KLL/Count-Min for large data), and more.
    *   **16 Categorical Algorithms**: Including ChiMerge, Fisher's Exact Test Binning (FETB), SBLP (Similarity-Based LP), JEDI-MWoE (Multinomial WoE), and others.
*   **High Performance**: Core algorithms are implemented in C++ using `Rcpp` and `RcppEigen` for maximum efficiency and scalability.
*   **Unified Interface**:
    *   `obwoe()`: Master function for optimal binning with automatic type detection and algorithm selection.
    *   `ob_apply_woe_num()` / `ob_apply_woe_cat()`: Functions to apply learned binning mappings to new data.
*   **tidymodels Integration**:
    *   `step_obwoe()`: A complete `recipes` step for integrating optimal binning into machine learning pipelines.
    *   Supports `tune()` for hyperparameter optimization of binning parameters (algorithm, min_bins, etc.).
*   **Multinomial Support**:
    *   Dedicated algorithms like `JEDI-MWoE` for handling multi-class target variables.
*   **Robust Preprocessing**:
    *   `ob_preprocess()`: Utilities for missing value handling and outlier detection/treatment (IQR, Z-score, Grubbs).
*   **Advanced Metrics**:
    *   `ob_gains_table()`: Computation of detailed gains tables including IV, WoE, KS, Gini, Lift, Precision, Recall, KL Divergence, and Jensen-Shannon Divergence.
*   **Visualization**:
    *   S3 `plot()` methods for visualizing binning results and WoE patterns.

### usage

*   See the package vignette (`vignette("introduction", package = "OptimalBinningWoE")`) for detailed examples and theoretical background.
