Changelog

Changelog

All notable changes to this project are documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[2.19.31] - 2026-08-17

Added

• All matrix-scoring commands accept repeatable --missing-value TOKEN options for exact, whitespace-trimmed survey-export markers such as NA, ., or -99. Tokens map to NaN only in scored numeric cells and participate in automatic header detection, retaining a headerless first row that begins with a missing marker. Plain, gzip-compressed, and standard-input streams share the implementation without adding a dependency.
• On a 2,000,000-cell delimited benchmark, the unchanged default path parses in 210 ms while explicit token matching takes 239 ms, a 13.5% opt-in cost without an extra matrix allocation.

[2.19.30] - 2026-08-10

Added

• All matrix-scoring commands accept --header auto|present|absent. The default preserves automatic detection, present supports fully numeric column names, and absent makes malformed first data rows fail instead of silently treating them as headers. The explicit contract works for plain, gzip-compressed, and standard-input delimited data without adding a dependency.

[2.19.29] - 2026-08-03

Changed

• Every public and command-line NPZ writer now stages a complete archive beside its destination and replaces the target atomically. Write failures retain existing content, clean up partial temporary output, and preserve an existing file's permission bits on successful replacement. Final symbolic links remain in place while their targets receive the completed archive.
• On a 500,000-respondent, 15-index archive, validated atomic saving takes 16.6 ms versus 12.5 ms for direct in-place serialization, with the same 4.0 MiB peak traced allocation. The response-time equivalent takes 1.6 ms versus 1.1 ms and 4.0 versus 3.9 MiB, without adding a dependency.

[2.19.28] - 2026-08-03

Added

• save_response_time_archive() writes prepared response-time scores, Boolean flags, resolved cutoff metadata, and optional respondent identifiers as the same versioned, pickle-free NPZ schema produced by the CLI.

Changed

• Public and command-line response-time persistence now share one validation and streaming boundary. It rejects unsupported metric/direction pairs, non-finite cutoffs, malformed or inconsistent flags, invalid score vectors, and invalid identifiers before opening the destination.
• On a 500,000-respondent archive, validated response-time saving takes 1.4 ms versus 1.2 ms for direct unvalidated serialization, with the same 3.9 MiB peak traced allocation and no added dependency.

[2.19.27] - 2026-08-03

Added

• load_response_time_archive() restores CLI response-time NPZ scores, flags, cutoff metadata, and optional respondent identifiers for later sensitivity analysis with response_time_score_flags().
• Public ResponseTimeArchive and ResponseTimeMetric types document the validated result and supported persisted metrics.

Changed

• Response-time loading always disables pickling and rejects unsupported or incomplete schemas, unsafe arrays, invalid metric/direction pairs, non-finite cutoffs, misaligned vectors, invalid identifiers, and flags inconsistent with either the fixed or percentile cutoff contract.
• On a 500,000-respondent archive, validated response-time loading takes 1.6 ms versus 0.9 ms for direct unvalidated member access, with 5.4 MiB peak traced allocation and no added dependency.

[2.19.26] - 2026-08-03

Added

• response_time_score_flags() reapplies fixed or percentile cutoffs to a retained direct timing score vector or fast-component mixture probabilities. Low-tail defaults suit timing summaries and consistency; high-tail defaults suit mixture probabilities.
• The public ResponseTimeFlagDirection type documents the supported suspicious tails, and the response-time benchmark now measures repeated cutoff scenarios.

Changed

• Single reusable score vectors and registered-index score mappings share one numeric, dimensionality, emptiness, and finiteness validator. The established response_time_flag() path delegates to the reusable implementation while preserving inclusive fixed cutoffs and tie-exclusive percentile cutoffs.
• On a 100,000-respondent, 80-item benchmark, five cutoff scenarios fall from 1.1683 seconds and 10.0 MiB peak temporary allocation to 3.2 ms and 1.2 MiB, a 368.9x speedup without adding a dependency.

[2.19.25] - 2026-08-03

Added

• save_score_archive() writes ordered raw registered-index vectors, optional respondent IDs, and soft failures as a compact versioned NPZ archive. Screen and composite archives round-trip through load_score_archive() and feed directly into the reusable scoring APIs.

Changed

• Public and command-line persistence now share one streaming, uncompressed, pickle-free serializer. The public writer validates paths, registry support, vector alignment, finiteness, identifiers, and failure metadata before opening the destination, without constructing an intermediate score matrix.
• On a 500,000-respondent, 15-index archive, validated saving takes 15.2 ms versus 14.7 ms for direct unvalidated serialization, with the same 4.0 MiB peak traced allocation and no added dependency.

[2.19.24] - 2026-08-03

Added

• load_score_archive() restores ordered raw registered-index vectors, respondent IDs, and soft failures from versioned screen NPZ output and composite NPZ output written with --include-components. Loaded vectors feed directly into screen_scores() or composite_scores().
• The public ScoreArchive type documents the validated loader result, and a reproducible benchmark compares validated archive loading with direct NumPy member access.

Changed

• Score archive loading always disables pickling and rejects unsupported schema versions, incompatible result types, missing or undeclared score members, invalid registry names, unsafe object arrays, non-finite scores, misaligned vectors, duplicate metadata, and invalid respondent identifiers.
• On a 500,000-respondent, 15-index archive, validated loading takes 11.4 ms versus 9.9 ms for direct unvalidated access, with the same 57.9 MiB peak traced allocation and no added dependency.

[2.19.23] - 2026-08-03

Added

• composite_scores() combines retained raw component vectors with automatic direction correction, optional standardization and weights, mean/sum/max reductions, and minimum-coverage masking. This enables composite sensitivity analysis without recalculating any index.
• A reproducible benchmark compares repeated full composite calculations with reusable component scores and verifies equivalent outputs for every scenario.

Changed

• Screening and composite reuse now share one validated score-vector boundary. Composite direction multipliers and weights are applied one vector at a time, avoiding a retained mapping of direction-corrected copies in regular detailed runs. Composite APIs now reject non-Boolean standardization controls.
• On a 10,000-respondent, 80-item benchmark, five weight scenarios fall from 39.7 to 1.4 ms and 12.9 to 0.7 MiB peak temporary allocation, a 27.9x speedup without adding a dependency.

[2.19.22] - 2026-08-03

Added

• screen_scores() applies registered-index flagging, coverage, consensus, and summaries to retained score vectors, enabling fixed-cutoff, percentile, and consensus sensitivity analysis without recalculating indices. Compatible float64 arrays are retained by reference and never mutated.
• The screening benchmark now compares repeated full calculations with the new reusable-score path and verifies identical thresholds and consensus decisions.

Changed

• Raw-data and precomputed-score screening share one result builder, keeping direction, tie, presence, completeness, and summary behavior aligned. On a 10,000-respondent, 80-item benchmark, five percentile scenarios fall from 175.7 to 5.1 ms and 21.5 to 1.6 MiB peak temporary allocation, a 34.2x speedup without adding a dependency.

[2.19.21] - 2026-08-03

Added

• Per-index screening summaries now report n_valid, n_unavailable, n_flagged, and flag_rate. The rate uses valid scores as its denominator and is unavailable when an index has no valid scores.
• Text, JSON, and NPZ screening output expose the new coverage metrics. NPZ archives retain aligned valid, unavailable, and rate arrays.

Changed

• Respondent flag counts, valid-score counts, and per-index summaries now share one non-stacking reduction pass. On a 500,000-respondent, 20-index benchmark with 10% unavailable scores, median reduction time falls from 29.90 to 27.51 ms with peak temporary allocation unchanged at about 15.0 MiB.
• Strict type checking now covers all benchmark modules in addition to the library, preventing synthetic result fixtures from drifting from public types.

Fixed

• The CLI output benchmark again runs for JSON, CSV, and NPZ screening results after its synthetic fixture fell behind recently added result fields.

[2.19.20] - 2026-08-03

Added

• A reproducible shared flagging benchmark covering fixed and percentile cutoffs, missing scores, throughput, and peak temporary allocation.

Changed

• Shared flagging now compares complete score vectors directly and derives sample cutoffs with a NaN-aware percentile reduction, avoiding repeated validity masks and filtered-score copies. On a one-million-score benchmark with 10% missing values, fixed-cutoff median time falls from 2.46 to 0.15 ms and peak temporary allocation from 9.6 to 1.0 MiB; percentile flagging falls from 10.74 to 8.55 ms and from 13.7 to 10.1 MiB, without adding a dependency.

[2.19.19] - 2026-08-03

Changed

• The integration dependency range now excludes yanked Polars 1.43.0 and the development lock resolves Polars 1.43.2 instead. The NumPy-only base install and optional plotting requirements are unchanged.

[2.19.18] - 2026-08-03

Added

• Screening now accepts per-index tail-percentile overrides through the percentiles mapping and repeatable --index-percentile INDEX=VALUE CLI option.
• Screening results, text, JSON, and NPZ output now identify each cutoff as fixed, percentile, or presence and retain the requested tail percentile.

Changed

• Fixed and percentile overrides for the same index are rejected as ambiguous. Presence-mode indices reject both numeric cutoff types, while the global percentile remains the fallback for indices without an override.

[2.19.17] - 2026-08-03

Added

• Screening now accepts min_valid_indices / --min-valid-indices to require enough available index scores before a respondent can receive a consensus flag.
• Screening results and every structured CLI format now report respondent-level valid_index_counts and consensus_eligible values. CSV output includes the equivalent singular columns, and configured NPZ archives retain the minimum.

Changed

• Composite and screening completeness controls now reuse one shared validator. Availability counts continue to accumulate one score vector at a time, without allocating a respondent-by-index matrix.

[2.19.16] - 2026-08-03

Added

• Attention-check scoring now supports explicit pass, fail, omit, and propagate missing-response policies through direct APIs, IndexOptions, and --infrequency-missing. The legacy pass policy remains the default.
• infrequency_flag() can now score and flag failure proportions, including missing-aware denominators and unavailable-row handling.

Changed

• Attention-check configuration now rejects non-Boolean proportion controls, duplicate or non-integer item indices, non-finite expected responses, and invalid count or proportion thresholds.

[2.19.15] - 2026-08-03

Added

• A reproducible Mahalanobis throughput and peak-allocation benchmark with configurable matrix size, repetitions, warmup, and random seed.

Changed

• Mahalanobis covariance and quadratic-form evaluation now use bounded respondent blocks, and all-zero covariance matrices use an exact zero pseudo-inverse. On a 100,000-respondent, 80-item benchmark, median time falls from 23.6 to 18.3 ms and peak temporary allocation from 123.1 to 7.6 MiB without adding a dependency.

[2.19.14] - 2026-08-03

Added

• Missing-response rates now accept respondent-specific Boolean applicability masks. Non-applicable cells are excluded from the missing count and denominator; rows without applicable selected items return unavailable scores and remain unflagged.
• Fixed required-item subsets now flow through IndexOptions, screening, composite scoring, and the --missing-item-indices command-line option.

[2.19.13] - 2026-08-03

Added

• The carelessness-onset benchmark now accepts a configurable missing-response rate for reproducing both complete and missing-data workloads.

Changed

• Missing-response onset detection now compresses bounded row blocks into equal retained-length groups and reuses the vectorized complete-response kernel instead of calculating each respondent independently. On a 10,000-respondent, 80-item benchmark with 10% missing responses, median time falls from 1.049 seconds to 19.9 ms. Peak temporary allocation rises from 0.3 to a bounded 2.6 MiB, without adding a dependency.

[2.19.12] - 2026-08-03

Added

• A reproducible complete-response carelessness-onset benchmark with configurable matrix size, response scale, window, minimum length, and seed.

Changed

• Complete-response onset detection now calculates stable sliding-window variability from rolling means and bounded deviation buffers, while its changepoint test retains candidate-position workspaces instead of complete centered and test-statistic matrices. On a 100,000-respondent, 80-item benchmark, median time falls from 187.7 to 109.6 ms and peak temporary allocation from 53.6 to 12.6 MiB, without adding a dependency.

[2.19.11] - 2026-08-03

Added

• A reproducible split-half individual-reliability benchmark with configurable matrix size, response scale, missing rate, split count, and random seeds.

Changed

• Individual reliability now generates its established random item splits once and scores them across bounded respondent blocks, reusing complete floating selections as centering buffers. On a 100,000-respondent, 80-item benchmark with 20 splits and 10% missing responses, median time falls from 941.9 to 579.0 ms and peak temporary allocation from 161.2 to 12.0 MiB, without adding a dependency.

[2.19.10] - 2026-08-02

Added

• A reproducible Guttman throughput and peak-allocation benchmark covering configurable matrix sizes, categorical scales, and missing-response rates.

Changed

• Guttman item means, difficulty-ordered selection, valid-response counts, and error accumulation now run in bounded respondent batches. On a 100,000-respondent, 80-item, five-category benchmark with 10% missing responses, median time falls from 137.0 to 112.3 ms and peak temporary allocation from 250.2 to 21.7 MiB, without adding a dependency.

Fixed

• Large low-cardinality datasets no longer switch away from categorical accumulation solely because the respondent-by-category product is large; fractional categories and high-cardinality responses preserve the direct ordered-pair definition within the same row bound.

[2.19.9] - 2026-08-02

Added

• Standalone semantic_syn_flag() and semantic_ant_flag() helpers now expose the registry's low-consistency flagging behavior for direct semantic-pair workflows. MAD accepts an optional fractional scale_min, also available as mad_scale_min in IndexOptions and --mad-scale-min in the CLI.

Changed

• Predefined semantic and MAD item pairs now share a bounded absolute-difference reducer. On a 100,000-respondent, 80-item, 40-pair benchmark with 10% missing responses, semantic synonym scoring falls from 56.6 to 44.0 ms and 173.3 to 6.8 MiB, semantic antonym scoring from 68.1 to 46.4 ms and 228.2 to 6.8 MiB, and MAD from 31.8 to 16.8 ms and 161.0 to 6.8 MiB, without adding a dependency.

Fixed

• MAD no longer truncates fractional response-scale endpoints during reverse scoring, and its documentation now correctly describes high scores as paired inconsistency. Entirely missing semantic and MAD matrices return unavailable scores without emitting empty-slice warnings.

[2.19.8] - 2026-08-02

Changed

• Response-time mean, median, standard-deviation, consistency, and mixture preprocessing now use bounded row reductions instead of complete timing-matrix workspaces. On a 100,000-respondent, 80-item benchmark with 10% missing times, peak temporary allocation falls from 77.1 to 1.3 MiB for the mean, 206.8 to 9.6 MiB for the median, 77.1 to 4.0 MiB for the standard deviation, 77.9 to 4.0 MiB for consistency, and 206.8 to 9.6 MiB for mixture scoring. Consistency time also falls from 40.7 to 27.3 ms, without adding a dependency.

Fixed

• Entirely missing response-time rows now return unavailable mean, median, standard-deviation, minimum, and consistency scores without emitting NumPy empty-slice warnings.

[2.19.7] - 2026-08-02

Changed

• IRV, simple and balanced acquiescence, extreme and midpoint response proportions, and combined response-pattern summaries now reduce in bounded row batches through a shared mean and population-standard-deviation layer. On a 100,000-respondent, 80-item benchmark with 10% missing responses, peak temporary allocation falls from 77.1 to 4.0 MiB for IRV, 138.2 to 2.3 MiB for acquiescence, 39.7 to 1.6 MiB for U3, 122.1 to 1.6 MiB for midpoint responding, and 118.3 to 4.0 MiB for combined response patterns. Combined response-pattern time also falls from 50.8 to 39.7 ms, without adding a dependency.

Fixed

• Missing-aware row means and standard deviations now return unavailable scores for entirely missing rows without emitting NumPy empty-slice warnings.

[2.19.6] - 2026-08-02

Changed

• Person–total item-profile means and respondent correlations now run in bounded batches through the shared row-correlation kernel instead of materializing several complete respondent-by-item arrays. On the 100,000-respondent, 80-item benchmark, median time falls from 56.7 to 24.0 ms and peak temporary allocation from 322.7 to 4.9 MiB, without adding a dependency.

Fixed

• Person–total scoring now ignores entirely unavailable items without emitting a warning when missing removal is enabled. Disabling missing removal restores strict propagation instead of silently discarding contaminated items, while constant respondent and item profiles remain unavailable.
• The index catalog now describes person_total as agreement with the sample item profile rather than as an extreme total-score measure.

[2.19.5] - 2026-08-02

Changed

• Psychometric synonym and antonym scoring now shares the allocation-light row-correlation kernel with even–odd consistency and evaluates every missing-response and seeded-resampling path in bounded respondent batches. On the 8,000-respondent, 40-item, 741-pair benchmark, median time falls from 32.2 to 15.6 ms and peak temporary allocation from 192.4 to 8.2 MiB, without adding a dependency.

Fixed

• Zero correlation thresholds now select only real item pairs from the strict lower triangle instead of also admitting diagonal and mirrored entries whose triangular-fill value happened to be zero.

[2.19.4] - 2026-08-02

Changed

• Multi-factor even–odd consistency now accumulates respondent sums and valid counts as each factor is scored instead of retaining and stacking every correlation vector. Its row-correlation kernel also avoids masked data copies and elementwise product temporaries, while compatible floating NumPy inputs are reused during validation. On the 100,000-respondent, 80-item, 20-factor benchmark, median time falls from 138.8 to 84.9 ms and peak temporary allocation from 112.2 to 7.8 MiB, without adding a dependency.

[2.19.3] - 2026-08-02

Changed

• Markov transition entropy now counts categorical pairs in bounded row batches and evaluates the equivalent conditional-entropy count formula, eliminating a transition-sized respondent ID vector and several probability tensors. On the 100,000-respondent, 80-item, five-state benchmark, median time falls from 99.2 to 75.0 ms and peak temporary allocation from 268.6 to 85.5 MiB, without adding a dependency.

Fixed

• Markov inputs with more than 64 distinct states now count only states and transition pairs observed within each row instead of attempting a dense global state-square allocation. High-cardinality and infinite categorical values therefore remain bounded and produce finite scores.

[2.19.2] - 2026-08-02

Changed

• Response-time Gaussian-mixture fitting now reuses its responsibility and scratch workspaces and evaluates ordinary densities in place. On the 100,000-observation, two-component EM benchmark, median time falls from 35.4 to 33.5 ms and peak temporary allocation from 6.1 to 4.7 MiB. The superseded internal normal-density helper is removed, without adding a dependency.

Fixed

• Mixture posterior rows whose ordinary Gaussian densities all underflow are now normalized in log space instead of returning an all-zero probability vector. Non-finite respondent medians are also excluded from fitting and returned as unavailable results.

[2.19.1] - 2026-08-02

Changed

• Composite and lz scoring now share one overflow-safe NumPy logistic kernel, removing four duplicate or direct exponential calculations. On the 10,000-respondent, 80-item lz benchmark, median time falls from 144.7 to 132.8 ms while peak temporary allocation remains 13.8 MiB, without adding a dependency.

Fixed

• Complete and missing-data lz likelihood calculations no longer emit overflow warnings for extreme user-supplied difficulty, discrimination, or theta values; probabilities retain finite-tail precision before established clipping is applied.

[2.19.0] - 2026-08-02

Added

• ier composite --include-probability now exports the overflow-safe, uncalibrated logistic transform alongside composite scores in text, JSON, CSV, and NPZ. Scoring still runs once, flags remain defined in composite-score units, structured formats identify the probability scale, and default schemas are unchanged. At 100,000 respondents and five component indices, probability-enabled CSV used 0.274 MiB peak output allocation, JSON used 0.536 MiB, and NPZ used 0.900 MiB, without adding a dependency.

[2.18.0] - 2026-08-02

Added

• composite_probability() now accepts return_diagnostics=True, matching the other composite helpers and returning ordered per-index soft-failure messages alongside the unchanged probability vector. The default remains a NumPy array, and the typed overloads preserve precise return types.

Fixed

• Logistic composite transformation now uses an overflow-safe piecewise calculation that preserves finite-tail precision and maps infinite scores to exact endpoints without warnings. On 500,000 scores spanning -1,000 to 1,000, the transform used 8.1 MiB peak temporary allocation and 1.9 ms median time, without adding a dependency.

[2.17.0] - 2026-08-02

Added

• ier composite now accepts mutually exclusive --threshold and --percentile options for opt-in respondent flagging. Fixed cutoffs include equality, percentile cutoffs use strict upper-tail comparisons, and the already-computed composite score vector is reused without rescoring indices. Text, JSON, CSV, and NPZ expose the flags; structured metadata records the resolved cutoff and its source, while default aggregate-only schemas remain unchanged. At 100,000 respondents and five component indices, flagged CSV used 0.274 MiB peak temporary allocation, JSON used 0.542 MiB, and NPZ used 0.900 MiB, without adding a dependency.

[2.16.0] - 2026-08-02

Added

• ier composite now supports --standardize and --no-standardize, matching the Python composite APIs while retaining standardized scoring as the default. Text, JSON, and NPZ outputs record the effective setting so aggregate values remain interpretable after export; CSV continues to contain only respondent rows. On 500,000 respondents and 20 indices, the raw-score mean used 8.6 MiB peak temporary allocation and 13.8 ms median reduction time, compared with 17.2 MiB and 52.9 ms for standardization, without adding a dependency.

[2.15.0] - 2026-08-02

Added

• ier composite --include-components now exposes successful raw per-index scores and respondent-level valid-index counts in text, JSON, CSV, and NPZ. The option reuses the detailed summary workflow without rescoring, retains existing failure metadata, streams JSON arrays and CSV rows, and writes typed NPZ members without stacking a component matrix. On 100,000 respondents and five indices, detailed CSV used 0.274 MiB peak temporary allocation, JSON used 0.525 MiB, and NPZ used 0.899 MiB, without adding a dependency.

Fixed

• Composite text ranking now excludes NaN and infinite aggregate scores instead of allowing unavailable rows to appear above finite scores.

[2.14.1] - 2026-08-02

Fixed

• ier composite now preserves the soft per-index failures already collected during scoring instead of silently discarding them, and ier screen mirrors its existing failures to standard error as well. Every output format emits a concise warning, text output includes an errors section, JSON includes an errors object, and NPZ stores aligned error_names and error_messages vectors. CSV remains an unchanged respondent table, score computation still runs once, and no dependency was added.

[2.14.0] - 2026-08-02

Added

• All composite APIs now accept an optional min_valid_indices requirement. Respondents with fewer available component scores receive NaN before flagging or logistic transformation, while the default preserves established reduction behavior. Detailed summaries expose respondent-aligned valid_index_counts, and the CLI accepts --min-valid-indices and records the rule in text, JSON, and NPZ metadata. Equal-weight means reuse their existing count vector: on 500,000 respondents and 20 indices, enabling a 10-index minimum held peak temporary allocation at 17.2 MiB and changed median time from 48.3 to 48.4 ms. The weighted path adds about 4.2 ms and one 3.8 MiB integer count vector only when the rule is enabled, without adding a dependency.

[2.13.0] - 2026-08-02

Added

• All composite APIs now accept optional positive finite per-index weights, applied after direction correction and optional standardization. Weighted means renormalize over the indices available for each respondent, partial mappings leave unspecified selected indices at weight 1, and resolved weights are included in composite summaries. The CLI accepts repeatable --weight INDEX=VALUE options and records explicit overrides in text, JSON, and NPZ metadata without adding a dependency. On a 500,000-respondent, 20-index standardized mean, weighting adds about 4.5 ms and 3.9 MiB of temporary allocation over the equal-weight reducer.

Changed

• Standardizing a constant index now retains its missing values instead of converting them to zero contributions, so per-respondent composite means can correctly exclude unavailable evidence.

[2.12.2] - 2026-08-02

Changed

• Screening flag counts and composite mean, sum, and maximum reductions now accumulate one index at a time instead of constructing additional respondent-by-index matrices. On 500,000 respondents and 20 indices, a standardized composite mean reduces peak temporary allocation from 252.3 MiB to 16.8 MiB and median reduction time from 79.9 to 42.2 ms; screening flag counts drop from 13.4 MiB to 3.9 MiB and from 5.5 to 3.7 ms, without adding a dependency.

[2.12.1] - 2026-08-02

Changed

• CLI screening, composite, and response-time JSON output now writes respondent arrays in bounded chunks to plain files, gzip files, or standard output while preserving the existing schema and strict null handling. For 100,000 respondents and five indices, screening JSON reduces peak output allocation from 44.5 MiB to 0.6 MiB, median serialization time from 0.83 to 0.57 seconds, and output size from 20.4 to 12.8 MiB without adding a dependency.

[2.12.0] - 2026-08-02

Added

• screen() and all composite APIs now accept an opt-in workers count, and the corresponding CLI commands accept --workers. Independent indices run concurrently through the Python standard library while results and failures retain selection order, and duplicate index selections are rejected rather than being scored ambiguously. Four workers reduce default screening time from 86.4 to 42.8 ms for 20,000 respondents × 80 items and from 531.0 to 282.5 ms for 100,000 × 80, without changing the single-worker default or dependencies.

[2.11.1] - 2026-08-02

Changed

• CLI matrix loading, text/JSON/CSV serialization, and command coordination now live in focused internal modules. The coordinator is roughly half its prior size while input memory use, serialization throughput, output schemas, and the NumPy-only runtime dependency remain unchanged.

[2.11.0] - 2026-08-02

Added

• CLI scoring commands now write versioned, pickle-free .npz result archives with typed scores, flags, metadata, failures, and optional respondent IDs. On a 100,000-respondent, five-index result, NPZ output is about 650 times faster than CSV and produces a 5.2 MiB file instead of 11.2 MiB, without adding a dependency.

[2.10.1] - 2026-08-02

Changed

• CLI CSV output now writes rows directly to plain files, gzip files, or standard output instead of retaining all rows and the complete serialized document. A 100,000-respondent, five-index export reduces peak allocation from 136.6 MiB to 0.3 MiB and median runtime from 1.93 to 1.81 seconds.

[2.10.0] - 2026-08-02

Added

• CLI scoring commands now accept uncompressed .npy matrices and memory-map them read-only for fast, low-overhead loading without new dependencies. A 25,000-respondent, 80-column benchmark initializes mapped input in about 0.3 ms with 0.3 MiB peak allocation, versus 343 ms and 16.4 MiB for CSV.

[2.9.2] - 2026-08-02

Changed

• CLI matrix loading now validates and converts rows directly into a compact numeric buffer instead of retaining a full raw string matrix during conversion. A 25,000-respondent, 80-column benchmark reduces peak allocation from about 100 MiB to 16 MiB and runs roughly 2.4 times as fast.

[2.9.1] - 2026-08-02

Added

• CLI scoring commands now accept - for forward-only standard input and explicit standard output. All CLI commands transparently read or write .gz files where applicable, using only the Python standard library.

[2.9.0] - 2026-08-02

Added

• The new ier response-time command scores timing matrices with mean, median, standard-deviation, minimum, consistency, or Gaussian-mixture metrics. It supports fixed or percentile flagging, named respondent and timing columns, mixture configuration, and text, JSON, or CSV output without new dependencies.

[2.8.2] - 2026-08-02

Changed

• Complete-data psychometric synonym and antonym scoring now evaluates respondent-by-pair workspaces in bounded batches. A 5,000-respondent, 80-item dense-pair benchmark reduces peak allocation from about 512 MiB to 10 MiB and runs roughly 1.2 times as fast.

[2.8.1] - 2026-08-02

Changed

• Complete-matrix lz person-fit scoring now batches safeguarded theta estimation and likelihood calculations in cache-sized workspaces instead of looping over respondents. The 10,000-respondent, 80-item benchmark runs roughly six times as fast, while missing-data rows retain the scalar fallback.

[2.8.0] - 2026-08-02

Added

• ier screen and ier composite now accept repeatable, comma-separated --item-columns selections. Named items are resolved from the input header in requested order, can be combined with --id-column, and allow survey exports containing unselected nonnumeric metadata to be scored directly.

[2.7.4] - 2026-08-02

Changed

• Resampled individual reliability now streams per-respondent correlation sums and valid counts instead of retaining a respondent-by-split matrix, uses a faster missing-free correlation path, and isolates seeded randomness from NumPy's global state. The 10,000-respondent, 80-item, 100-split benchmark runs roughly 2.6 times as fast with lower peak workspace.

[2.7.3] - 2026-08-02

Changed

• Complete-matrix carelessness-onset detection now evaluates sliding-window variability and changepoint statistics in bounded vectorized batches instead of nested respondent/window loops. The 10,000-respondent, 80-item benchmark runs over 200 times as fast while missing-data rows retain the established fallback behavior.

[2.7.2] - 2026-08-02

Changed

• Public percentile-capable flag helpers now share one cutoff-boundary policy: explicit thresholds include scores exactly at the cutoff, while sample-percentile thresholds continue to flag only the strict tail. This brings direct helper behavior in line with fixed thresholds in screen().

[2.7.1] - 2026-08-02

Changed

• Complete-matrix repeating-pattern scoring now streams match lengths in reverse using respondent-sized, smallest-safe integer vectors instead of several respondent-by-position integer matrices; the 10,000-respondent, 80-item benchmark runs roughly 1.6 times as fast with substantially less workspace, reducing the same default-screen benchmark by about 10%.

[2.7.0] - 2026-08-02

Added

• screen() and all composite orchestration APIs accept strict=True to raise a contextual error as soon as any selected index fails.
• The screen and composite CLI commands expose the same policy through --strict, while soft per-index errors remain the default.

[2.6.1] - 2026-08-02

Fixed

• Percentile-based flagging APIs now consistently reject booleans, non-finite thresholds, and percentiles outside 0–100 instead of silently returning misleading flags.
• Response-time flagging now shares the same validated threshold implementation as response-matrix indices.

[2.6.0] - 2026-08-02

Added

• Public missing_rate() and missing_rate_flag() helpers quantify response omissions across all items or a validated item subset.
• Opt-in missing_rate registry support for screening, composites, CLI output, fixed thresholds, and index discovery without changing default workflows.

[2.5.0] - 2026-08-02

Added

• CLI --id-column NAME support excludes a named identifier column from scoring and preserves unique, nonblank respondent IDs in text, JSON, and CSV outputs.

[2.4.3] - 2026-08-02

Changed

• Guttman scoring now directly encodes bounded integer response scales and counts grouped category positions in vectorized passes; the 10,000-respondent, 80-item categorical benchmark runs roughly four times as fast.

[2.4.2] - 2026-08-02

Changed

• Markov transition scoring now directly encodes bounded integer response scales and compacts unused states without sorting the full matrix; the 10,000-respondent, 80-item index benchmark runs roughly twice as fast.

[2.4.1] - 2026-08-02

Changed

• Mahalanobis distance evaluation now uses a BLAS-backed matrix product for the quadratic form; the 10,000-respondent, 80-item index benchmark runs roughly 31 times faster and default screening runs roughly twice as fast.

[2.4.0] - 2026-08-02

Added

• Public index_catalog() metadata for registered indices, including flagging modes, orchestration defaults, composite availability, and required options.
• ier indices discovery output in text, JSON, or CSV format.

Changed

• Local and CI version checks now verify that the editable project entry in uv.lock matches project.version before dependency synchronization.

[2.3.0] - 2026-08-02

Added

• screen() accepts fixed per-index thresholds alongside percentile defaults and returns the actual cutoff applied for each successful index.
• The CLI accepts repeatable --threshold INDEX=VALUE options and includes applied thresholds in JSON and text output.

[2.2.6] - 2026-08-02

Changed

• Longest-run scoring now processes complete matrices column-wise instead of looping over respondents; the 10,000-respondent, 80-item index benchmark runs roughly 47 times faster and the 1,000-respondent, 50-item default screening benchmark runs roughly 1.6 times faster.

[2.2.5] - 2026-08-02

Changed

• Repeating-pattern scoring now evaluates complete matrices in vectorized batches; the 1,000-respondent, 50-item index benchmark runs roughly 36 times faster and default screening runs roughly three times faster.

[2.2.4] - 2026-08-02

Added

• screen() now returns configurable respondent-level consensus_flags alongside per-index flags and counts, with a default min_flags=2 agreement threshold.
• The CLI exposes --min-flags and includes consensus decisions in text, JSON, and CSV output.

[2.2.3] - 2026-08-02

Changed

• Guttman scoring now counts ordered response pairs without materializing every respondent-by-item-pair value matrix, using an adaptive categorical fast path and bounded-memory fallback for high-cardinality data; the 3,000-respondent, 100-item categorical benchmark uses roughly one-sixth the peak process memory and runs about 1.7 times faster.

Fixed

• All-missing Guttman inputs now return missing scores without emitting empty-mean runtime warnings.

[2.2.2] - 2026-08-02

Changed

• Transition-entropy scoring now uses its vectorized batch implementation for complete matrices under the default missing-value policy; the 10,000-respondent, 80-item transition benchmark is roughly five times faster.

[2.2.1] - 2026-08-02

Changed

• Repeating-pattern screening now precomputes constant-pattern and periodic-match runs, avoiding repeated sorting and rescanning for every candidate position; the 1,000-respondent, 80-item screen benchmark is roughly nine times faster.

Fixed

• Architecture and index documentation no longer describes dependency-free statistical routines as requiring SciPy.

[2.2.0] - 2026-07-29

Added

• Dependency-free normal density/quantile helpers, bounded Newton IRT theta estimation, and chi-square quantiles backed by regularized incomplete-gamma series and continued-fraction calculations.
• SciPy-reference regression coverage for chi-square probabilities from 1e-12 through 1 - 1e-12 and 1–1,000 degrees of freedom.
• Targeted test, integration, lint, docs, and security dependency groups.

Changed

• Mahalanobis chi-square and z-score flagging, Mahalanobis Q-Q data, lz theta estimation, and response-time mixtures now run consistently in the base install.
• The lz theta solver now converges to a tighter score-equation tolerance; locked lz scores can shift by roughly 1e-6 from SciPy's default bounded tolerance.
• Ruff now owns lint and static-security checks, including selected Pylint and flake8-bandit rules; CI jobs install only their required dependency groups.
• The legacy full extra is an empty compatibility alias.

Removed

• SciPy and scipy-stubs, the redundant wheel build requirement, direct transitive pins for Pillow/Pygments/Requests, and unused pre-commit project dependency.
• Redundant Bandit and non-gating Pylint jobs and dependencies.

[2.1.7] - 2026-07-28

Fixed

• Semantic-antonym scoring now reflects reverse-keyed responses around the configured or inferred response scale, so consistent antonym pairs receive high consistency scores instead of being clipped as maximally inconsistent.

[2.1.6] - 2026-07-27

Fixed

• longstring() now rejects numeric and multidimensional NumPy arrays instead of silently converting them into meaningless character-run results; numeric survey matrices remain supported through longstring_scores().

[2.1.5] - 2026-07-27

Fixed

• CLI matrix loading now treats blank delimited fields as missing values instead of rejecting the file, and header detection no longer discards a first data row whose first value is blank.

[2.1.4] - 2026-07-27

Fixed

• CLI CSV export now represents NaN and infinite scores as empty cells instead of non-numeric nan and inf strings.

[2.1.3] - 2026-07-27

Fixed

• Invalid multi-character, empty, or newline CLI delimiters now return a concise error instead of leaking a Python traceback.

[2.1.2] - 2026-07-27

Fixed

• CLI matrix loading now accepts the documented whitespace-delimited format, including mixed runs of spaces and tabs.

[2.1.1] - 2026-07-27

Fixed

• CLI analysis and output failures now return concise error: messages instead of leaking Python tracebacks for invalid user input.
• CLI JSON export now emits standards-compliant null values for NaN and infinite scores or summary statistics.

[2.1.0] - 2026-07-24

Added

• Architecture note (docs/architecture.md) covering registry design, flagging, NA policy, and uncalibrated composite probabilities.
• Expanded golden / R-parity fixtures for guttman, markov, person_total, midpoint, lz, and onset, plus JSON harness under tests/fixtures/parity/.
• Synthetic detection-rate benchmark (benchmarks/bench_detection.py).
• GitHub issue templates (bug / feature / methods) and a PR template.
• Shared SciPy install hints via _optional_imports.require_scipy.

Changed

• Docs homepage quick start uses IndexOptions (2.0-compatible).
• dev extra composes full, plot, and docs instead of duplicating pins.
• Lint and security workflows also run on pushes to main.
• Screen throughput benchmark updated for the IndexOptions-only API.
• LICENSE copyright years updated through 2026.
• Package version bumped to 2.1.0.

Removed

• Unused raise_missing_config flag on score_registered_indices() (soft-fail is the only orchestration path).

[2.0.0] - 2026-07-24

Breaking

• screen() / composite() / composite_flag() / composite_summary() / composite_probability() accept configuration only via options=IndexOptions(...). Legacy per-index keyword arguments were removed.
• Removed deprecated build_index_options().
• String run-length helpers in ier.longstring are private (_run_length_encode, _run_length_decode, _longstr_message, _avgstr_message). Prefer longstring() / longstring_scores().
• mahad(..., flag=True, method="zscore") requires SciPy (same as chi2); it no longer silently falls back to a hardcoded threshold of 2.0.

Added

• CLI JSON/CSV export (--format json|csv, optional --output), plus IndexOptions knobs (--evenodd-factors, MAD/semantic/infrequency lists, etc.).
• Clearer jagged-CSV error when loading CLI matrices.
• Golden parity fixtures for longstring_pattern, mahad (iqr), psychsyn, and evenodd.
• Public export of longstring_scores.
• SECURITY.md and .github/CODEOWNERS.
• Release and PyPI publish workflows run the full CI test suite before shipping.
• CI also runs on pushes to main.

Changed

• composite() soft-fails missing index config (e.g. evenodd without factors), matching screen(); if no index succeeds, it still raises.
• Local scripts/check.sh pylint uses --fail-under=9.0 to match CI.
• Version-check workflow is callable-only (no duplicate PR trigger).
• Response-time helpers documented as intentionally out of the registry.
• Specialized tests split into test_composite.py, test_response_time.py, and a smaller test_specialized_indices.py.
• Package version bumped to 2.0.0.

Fixed

• Changelog no longer claims Codecov fails the job on upload error; uploads stay non-blocking when CODECOV_TOKEN is unset.

[1.8.0] - 2026-07-23

Added

• Public IndexOptions config object for screen() / composite() (preferred over long keyword lists); legacy kwargs remain supported when options is omitted.
• Package __version__ and ier CLI (ier screen, ier composite).
• Unified contributor check script (scripts/check.sh).
• Golden IRV / longstring parity fixtures (tests/test_golden_parity.py) and a concrete R-package comparison table in the docs.
• Docs build gated on pull requests via the veto workflow.

Changed

• screen() / composite() share the full IndexOptions surface (including scale bounds, longstring pattern length, onset, and acquiescence settings).
• screen() reuses shared threshold_flags for percentile flagging.
• visualize helpers are typed against ScreenResult.
• Version-check CI requires a bump only when src/ changes.
• Pre-commit Ruff pin aligned to 0.15.x; docs workflow uses actions/checkout@v7.
• Package version bumped to 1.8.0.

Fixed

• Release workflow no longer overwrites curated CHANGELOG.md when generating GitHub release notes.

[1.7.0] - 2026-07-23

Added

• Registry coverage for guttman, psychant, individual_reliability, onset, semantic_syn, semantic_ant, and infrequency in screen() / composite().
• onset presence-based flagging mode in screen().
• Expanded screen() / composite() configuration knobs for newly registered indices.
• MkDocs documentation site (getting started, workflows, index catalog, thresholds, R notes, API reference).
• Benchmark script for the screen() hot path (benchmarks/bench_screen.py).
• Example scripts under examples/.
• pandas / polars smoke tests; pandas and polars added to the dev extra.
• Curated changelog and expanded PyPI classifiers (Python 3.11–3.14).

Changed

• Default screen() / composite() Mahalanobis scoring uses a NumPy-safe path (method="iqr" distances). SciPy is only required for direct mahad(..., flag=True, method="chi2") (and related SciPy-only helpers).
• score_registered_indices soft-catches ValueError, RuntimeError, and TypeError into per-index errors.
• CI coverage gate aligned to 90%; Codecov upload remains non-blocking when a token is unset; Bandit no longer falls back to a looser severity filter.
• Package version bumped to 1.7.0.

Fixed

• Base-install footgun where default screening could abort on missing SciPy when computing Mahalanobis distances with the previous chi-squared default path.

[1.6.3] - 2026-05

Changed

• Dependency and CI maintenance releases (Dependabot, lockfile, Actions bumps).

Added

• Index orchestration registry hardening and related workflow improvements.

Earlier

Feature work through early 2026 introduced screening workflows, composite scoring, additional indices (including MAD, lz, acquiescence), visualizations, and multi-OS / multi-Python CI.