predict.scr_align       Apply an alignment to raw scores
predict.scr_grades      Grade a score vector with the cut points of an
                        scr_grades object
predict.scr_pd          Predict grade and PD from an scr_pd object
scr_align               Stage 5: align a raw score to the declared
                        scale
scr_apply               Apply the WOE transformation or the scorecard
                        to new data
scr_bin                 Stage 2: optimal binning, screening, hold-out
                        revalidation and pruning
scr_bin_continuous      Bin drivers against a continuous target (LGD,
                        CCF)
scr_calibrate           Calibrate the alignment to a central tendency
scr_capital             Expected loss, risk-weighted assets and capital
                        of a portfolio
scr_classing_accept     Accept or discard a proposal
scr_classing_apply      Commit the lab into a new selection result
scr_classing_choose     Choose the final variable list manually
scr_classing_propose    Propose manual bins for a variable
scr_classing_spec       Classing specification as a long table, with
                        its file round trip
scr_classing_view       Inspect the current bins of a variable in the
                        lab
scr_coarse_classing     Coarse classing lab: manual binning and manual
                        variable choice
scr_compare             Compare runs across targets
scr_config              Pipeline configuration
scr_config_keys         Dictionary of configuration keys
scr_connect             Connect to a database (ODBC DSN or any DBI
                        driver)
scr_core                Variables that cross several targets
scr_cutoff              Stage 6: cut-off sweep with frozen cuts
scr_decisions           Decision ledger of a lab, a result or a
                        scorecard
scr_default             Build the default flag from a monthly panel
scr_default_rate        One-year default rates by cohort and the
                        long-run average
scr_demo                Synthetic example data
scr_demo_ead            Synthetic monthly facility snapshots for the
                        EAD/CCF module
scr_demo_lgd            Synthetic default events for the workout LGD
                        examples
scr_demo_lgd_cashflows
                        Synthetic post-default cash flows of
                        'scr_demo_lgd'
scr_demo_panel          Synthetic monthly panel for the default engine
                        and PD calibration
scr_demo_portfolio      Synthetic exposure snapshot for expected loss,
                        capital and ECL
scr_demo_rates          Synthetic monthly reference rate series
scr_ead                 Estimate CCF pools from the reference data set
scr_ead_data            Build the realised-CCF reference data set from
                        facility snapshots
scr_ead_downturn        Downturn CCF per pool
scr_ead_validate        Validate CCF pools: calibration,
                        discrimination, back-testing and stability
scr_ecl                 Expected credit loss with stage allocation
scr_el                  Expected loss per exposure
scr_elbe                ELBE and in-default LGD on a grid of months
                        since default
scr_export              Write the deliverables
scr_fetch               Fetch a table with reproducible server-side
                        sampling
scr_funnel              Audit funnel: every input variable and its fate
scr_gains               Gains table, at bin level
scr_grades              Rating grades on the score
scr_irb_params          IRB parameter tables by framework preset
scr_irb_rw              IRB risk weight of one or many exposures
scr_iv                  Information Value of any grouping
scr_leakage             Leakage and suspicious-strength audit
scr_lgd                 Two-stage LGD model and pools on the reference
                        data set
scr_lgd_downturn        Downturn LGD per pool
scr_lgd_floor           Input floor on the downturn LGD per pool
scr_lgd_pools           LGD pools from the predicted LGD
scr_lgd_validate        Validation battery of an LGD model
scr_master_scale        Master scale of PD grades
scr_metrics             AUC, KS and Gini of a score, with a bootstrap
                        confidence interval
scr_migration           Migration matrix between two rating dates
scr_moc                 Margin of conservatism, by category
scr_model               Stages 3 and 4: multi-strategy selection and
                        consensus
scr_monitor             Monitor the scorecard on new data
scr_monitoring_plan     Monitoring plan read by scr_monitor()
scr_pd                  The PD model: grades, margin of conservatism
                        and the floor
scr_pd_pit_ttc          One-factor bridge between point-in-time and
                        through-the-cycle PD
scr_pd_stress           Stressed PD of the one-factor model
scr_pd_validate         Validate a PD model on a cohort panel
scr_presets             Selection presets, side by side
scr_psi                 Population stability index, with the fixed and
                        the sample-size-adjusted threshold
scr_reasons             Reason codes: the variables that took the most
                        points from each row
scr_reject              Stage 6: honest reject inference through a
                        sensitivity band
scr_result              Result of a selection
scr_run                 Run the selection for several targets straight
                        from the database
scr_runset              Set of runs, one per target
scr_sa_rw               Standardised risk weight of an exposure
scr_score_gains         Score gains per frozen band
scr_score_metrics       Score metrics per sample, with CI
scr_scorecard           Stages 4 and 5: points scorecard, aligned to
                        the declared scale
scr_select              Select variables for the scorecard
scr_selected            Variables approved for the scorecard
scr_split               Stage 0: type the data and split train and
                        hold-out
scr_sql                 Production SQL
scr_strategy            Stage 6: strategy table per band, with marginal
                        expected profit
scr_triage              Stage 1: descriptive triage and sentinel
                        resolution
scr_verbose             Switch progress messages on or off
scr_workout             Workout LGD: the reference data set from
                        default events and cash flows
