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scorecraft 0.3.0

• scr_lgd_downturn() and scr_ead_downturn() estimate the observed downturn impact, and the LGD reference value, on the training rows only, like the long-run averages; the hold-out stays independent evidence.
• scr_export(), scr_sql(file = ) and the classing lab functions follow the verbose key of the object’s configuration, as the function that fitted it does.
• The classing lab raises IV_RATIO_UNSTABLE, an advisory warning, when the train IV of a proposal is below iv_min and the hold-out/train IV ratio carries little information.
• scr_irb_params() lists the regulatory texts behind the presets; users check the tables against the texts in force before any regulatory use.
• Three vignettes: Get started, coarse classing, and scaling, alignment and challengers. The PD, LGD/EAD and capital guides are articles on the package website.
• A two-page cheat sheet ships in inst/cheatsheet (PDF and its HTML source).

scorecraft 0.2.0

The IRB layer: from the scorecard to regulatory risk parameters, with the same contracts as the scorecard pipeline (one configuration, ledgers with mandatory reasons, hold-out revalidation with frozen bins, hardened workbooks, production SQL verified against DuckDB and SQLite). Regimes are parameter tables selected by a preset, never prose.

• scr_irb_params() ships the numbers of three presets ("bcb", "basel3_final", "crr3"): PD floors, LGD input floors, foundation LGD, standardised CCFs, asset correlations, maturity rules, output floor and standardised risk weights; the tables are editable and edits are recorded.
• scr_default() builds the default flag from a monthly panel (days past due with absolute and relative materiality, unlikeliness to pay, probation, restructuring, obligor-level pulling effect); scr_default_rate() gives the default rates by cohort, grade, segment and exposure, with the long-run average and its benchmark.
• scr_bin_continuous() bins drivers against a bounded continuous target (LGD, CCF) and returns an object with the shape of the engine’s, so OptimalBinningWoE::obwoe_apply() and obwoe_sql() reproduce the bin means in R and in every SQL dialect; hold-out revalidation with frozen cut points and PSI.
• scr_config() gains the keys of stages 8 to 12 (default_*, pd_*, lgd_*, ccf_*, framework, capital_*, ecl_*), all registered in scr_config_keys() and validated.
• scr_demo_panel, scr_demo_lgd, scr_demo_lgd_cashflows, scr_demo_rates, scr_demo_ead and scr_demo_portfolio are new demonstration data.
• PD: scr_master_scale(), scr_calibrate() (intercept shift, log-odds (a, b), scaling, quasi-moment matching; a new alignment, the scorecard untouched), scr_grades() (geometric, quantile or supplied grades, merges below the minimum counts, monotone repair recorded), scr_moc() (estimation error computed; other categories with a mandatory reason), scr_pd() (floors from the preset), scr_migration(), scr_pd_validate() (Jeffreys, binomial, normal, Hosmer-Lemeshow, multi-period, AUC against the initial value, PSI, migration bandwidths, concentration; traffic lights), scr_pd_pit_ttc(), with predict(), scr_apply(), scr_sql() (grade and PD as a CASE on the score) and scr_export() methods.
• LGD: scr_workout() (discounted recoveries and costs, cures, merged re-defaults, extrapolated incomplete workouts, named funnel rules), scr_lgd() (cure stage on the binary engine, severity stage on the continuous binner with a fractional logit or a beta regression, hold-out revalidation, pools), scr_lgd_downturn(), scr_lgd_floor(), scr_elbe(), scr_lgd_validate(), with scr_apply(), scr_sql() (both stages, pool CASE, floored result) and scr_export() methods.
• EAD: scr_ead_data() (realised conversion factors under a fixed, cohort or variable horizon; conversion factor below and limit factor above a utilisation threshold; named funnel rules), scr_ead() (driver bins with admission rules, pools, estimation-error margin, standardised floor), scr_ead_downturn(), scr_ead_validate(), with scr_apply(), scr_sql() and scr_export() methods.
• Expected loss and capital: scr_el(), scr_irb_rw() (the risk-weight function with correlations, size adjustment, maturity, floors and the defaulted case), scr_sa_rw(), scr_capital() (reconciliation by segment, output floor, provisions shortfall and excess, floors impact, sensitivity grid, concentration), scr_pd_stress(), scr_ecl() (survival-weighted 12-month and lifetime expected credit loss with stages and scenarios), with scr_sql() (constants per pool, no normal quantile at run time) and scr_export() methods.
• scr_irb_rw() and scr_capital() read the supervisory LGD of the foundation approach from params$lgd_firb through a claim type; scr_sa_rw() and scr_capital() apply the non-granular retail weight with granular = FALSE; the Hosmer-Lemeshow light of scr_pd_validate() is green when every grade sits on the conservative side (the PD above the observed rate), since the statistic is two-sided.
• betareg (Suggests) powers the beta severity engine of scr_lgd().
• scr_sql() on a scorecard gains what = "all" (bin label, WOE and points of every variable next to the exact score and the whole-points score) and keep_columns (key columns carried into the output), for a deployment that reports the band of each variable with the score.
• After the documentation audit: scr_iv() ignores NA for every group type; scr_classing_read() validates the separator and the spec carries it into scr_classing_import(); the TOO_MANY_BINS screening rule can fire (the screen reads max_bins); scr_psi() stores and prints its thresholds; scr_default_rate() reports one long-run mean and benchmarks an optional lra_adjusted; one asset_class configuration key replaces pd_asset_class and capital_asset_class; scr_lgd_downturn() always records a reason; the traffic-light convention is red at or below the first threshold in PD, LGD and EAD; scr_apply() on an scr_ead takes what; scr_fetch() gains verbose and scr_run() follows config$verbose; the scr_demo columns carry English names (vl_partial_*, vl_noise_*, vl_constant, vl_near_const, vl_duplicate, vl_redundant, vl_late, ds_region, ds_band, ds_channel, ds_high_card).

Compiled kernels and big tables

• The package now compiles C++ code (‘Rcpp’ and ‘RcppArmadillo’). Thin internal R wrappers validate the input and call the kernels, which take plain vectors and matrices (never a data.table) and read the columns without copying them. The kernels are tested against their reference R implementations.
• Redundancy pruning in scr_bin() (Pearson or Spearman): each WOE column is ranked once and the correlation matrix comes from one BLAS cross-product, instead of ranking both columns of every pair. The greedy sweep gives exactly the same result as OptimalBinningWoE::obwoe_prune(), and is about 50 times faster at 150 columns.
• Somers’ D of LGD and EAD models is counted exactly in O(n log n) (Knight’s algorithm). It replaces an O(n^2) Kendall computation inside a 200-resample bootstrap. The EAD version is also exact now: it no longer groups the prediction into 60 quantile buckets.
• scr_ecl() streams the survival-weighted loss row by row and applies the scenario shocks on the fly. Memory is O(n) whatever the term (the matrix version built several n x T copies, about 2.9 GB each at n = 1e6, T = 360). Results are the same to 1e-15.
• scr_metrics() ranks the scores once; each bootstrap resample then re-tabulates counts, with no sort and no grouping by a double key. The cut-off sweep sorts once per sample. The monitor tabulates the base once for every period. Default rates by cohort use one rolling join. Every LGD cash-flow aggregation and EAD reference date is vectorised.
• Kernel threads follow config$nthread.

Correctness and numerical stability

• Wide tables: scr_triage() and the R pre-processing of scr_apply() reserve column slots before adding columns, so they no longer fail past about 1024 columns.
• Random numbers: seeds are local to the call. .Random.seed is restored on exit, and a bootstrap advances the user’s stream only by the replicate seeds it draws. Results for a given seed are unchanged.
• scr_split():
  • the target is checked for 0/1 before integer coercion (0.5 was truncated to 0);
  • text dates (as DBI returns them) and integer64 columns are read correctly;
  • rows with a missing date are reported.
• scr_bin(): under allow_derived_final = FALSE, derived flags leave before the redundancy pruning, so a flag can no longer remove a real column. The Rcpp subset-proxy warnings of OptimalBinningWoE::obwoe_gains_score() are muffled; its values are correct, and the fix belongs upstream.
• scr_config() validates every key of stages 0 to 7.
• LightGBM receives min_sum_hessian_in_leaf. The xgboost API is detected from xgb.train() (it works with xgboost 3).
• SQL string literals follow the dialect: backslashes are escaped only in MySQL, Spark/Hive/Databricks and BigQuery. A line break in a name can no longer escape an SQL comment. The scorecard’s SQL quotes identifiers as obwoe_sql() does. A row that falls in no fitted bin takes the points of WOE 0, in R and in SQL.
• Metrics and monitoring:
  • scr_metrics() refuses a factor or a non-0/1 outcome, and counts are kept in double to avoid integer overflow;
  • scr_psi() leaves bands empty in both samples out of the index and out of the degrees of freedom;
  • scr_monitor() keeps undated rows as a period;
  • a classing spec survives the CSV and xlsx round trips;
  • an export cannot be written outside the given directory.
• PD:
  • scr_default() could assign one unit’s flags to another under locales where the grouping order differed from the C-locale sort; it is fixed, and the state machine is now vectorised;
  • the PSI_ACTION gate of the LGD and EAD drivers compared against a flag scr_psi() never returns, so it never fired; it now does;
  • Hosmer-Lemeshow on fixed PDs uses K degrees of freedom;
  • the multi-period test is scaled by sd(DR_t - PD_t) (BCBS WP 14);
  • the AUC test uses the DeLong variance;
  • scr_moc() no longer edits the caller’s ledger;
  • point-in-time PDs accept a grade at zero.
• LGD and EAD:
  • scr_ead_data() paired default dates with facilities after a sort, so unsorted input put defaults on the wrong facility; it is fixed;
  • scr_ead() no longer needs data.table 1.15;
  • ccf_measure = "lf" keeps its pools;
  • a cure recovers ead + drawings - recovered;
  • the scr_elbe() uplift starts at zero at the date of default;
  • scr_lgd_downturn(), scr_lgd_floor() and scr_ead_validate() no longer modify their input.
• Capital and ECL:
  • the maturity adjustment is held at PD = 1e-5 below that point, where 1 - 1.5 b approaches zero and the risk weight exploded or turned negative without a PD floor;
  • a missing SME sales figure gets no firm-size adjustment (CRE31.9);
  • the ECL exit probability is capped at one, the LGD floored at zero, and scenario shocks and input lengths are validated.

Scorecard pipeline hardening

• scr_monitoring_plan() is the monitoring contract: created by scr_scorecard(), written to the Monitoring_Plan sheet, and read back by scr_monitor(plan = ) (a table or the strategy workbook), which now takes its PSI/CSI thresholds, alpha and min_events_per_period from it.
• scr_scorecard() stores the hold-out bin index of every variable, so the Stability_CSI_Timeline sheet is a real timeline by vintage without a scr_monitor() object.
• options(scorecraft.parallel = "fork" | "psock" | "serial") selects the parallel backend; results are identical under the three.
• scr_triage() is parallel by column as well.
• Workers never fail silently on any backend: a worker error is re-thrown with the failing item, a worker killed by the system is reported as such (instead of a NULL that surfaces later as a subscript error), warnings raised in a worker are re-raised in the parent, PSOCK workers run with a single data.table thread, and a data.table returned by a worker is re-allocated so that := works on it.
• options(scorecraft.fork_mem_fraction = 0.75) caps the fork workers by the memory available on Linux (Inf to disable), since forked workers duplicate the parent heap once the garbage collector runs.

scorecraft 0.1.0

First release. A production-grade scorecard engine for binary targets, built on ‘OptimalBinningWoE’: audit funnel, single configuration, named relaxation, first-class scale alignment, cut-off strategy and hardened deliverables.

• Seven stages, each an exported function, chained by scr_select() and scr_scorecard(): scr_split(), scr_triage(), scr_bin(), scr_model(), scr_align(), scr_cutoff()/scr_strategy()/scr_reject().
• scr_align() is a first-class stage: banded log-odds regression on the raw score composed with the PDO map, odds_orientation recorded, applied to any engine. It runs automatically inside scr_scorecard().
• scr_sql() emits production SQL in fourteen dialects: a pre-processing CTE, the WOE/BIN transformation from the authoritative cut points and, for a scorecard, the exact score plus whole points from the bin index. R-SQL equivalence is verified by test against DuckDB and SQLite.
• scr_bin() is parallelised by column (nthread), with the fits merged; the result is identical to the serial one.
• scr_metrics() always reports a bootstrap confidence interval for AUC/KS/Gini; scr_psi() reports the fixed threshold next to the sample-size-adjusted critical value of Yurdakul and Naranjo (2020).
• scr_scorecard(challenger = ) fits a tree challenger (xgboost or lightgbm) on the same WOE columns, aligned to the same scale, with supports_scorecard = FALSE.
• scr_reject() implements honest reject inference: population scope, band coverage and a 2x/4x/8x sensitivity band, never parcelling by default.
• scr_monitor() recomputes PSI/CSI (with the signed points shift) and the performance by vintage on new data; it never schedules itself.
• scr_export() writes four hardened .xlsx workbooks (selection, scorecard, validation, strategy), the SQL files and a Markdown summary.
• scr_coarse_classing() opens a manual binning lab: scr_classing_view(), scr_classing_propose() (breaks, groups, merge, split, missing_to, other_to, reset), scr_classing_accept()/scr_classing_discard() with a mandatory reason, scr_classing_choose() (keep/drop/force), scr_classing_spec()/scr_classing_read()/scr_classing_import() for a CSV/xlsx round trip, scr_classing_apply() to commit into a new scr_result, and scr_decisions() for the append-only ledger. Manual bins share the engine’s contract, so scr_scorecard(), scr_apply() and scr_sql() follow them unchanged; the funnel gains provenance.
• scr_connect() accepts any DBI driver next to an ODBC DSN, and scr_fetch() samples server-side with a dialect-aware random expression.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.