Data, Variables, Samples, and Measurement

10 algorithms in Financial Mathematics, Statistics, and Data Foundations · 10 with asserted arithmetic.

In this family#

1. Observations, Entities, Variables, and Datasets verified

   Counts what a table actually contains: how many rows, how many distinct columns across all of them, and how many distinct entities those rows describe.

   observationsEntitiesVariablesAndDatasets(input)
2. Numeric, Categorical, Ordinal, and Binary Variables verified

   Classifies every column of a table as binary, numeric, ordinal or categorical by inspecting the values actually present in it.

   numericCategoricalOrdinalAndBinaryVariables(input)
3. Population, Sample, Census, and Sampling Frame verified

   Relates the rows in hand to the population they are meant to represent and to the frame they were drawn from, reporting the sampling fraction and the frame's coverage.

   populationSampleCensusAndSamplingFrame(input)
4. Cross-Sectional, Time-Series, Panel, and Event Data verified

   Infers a table's layout from the shape of its own keys, deciding whether the rows are cross-sectional, a time series, a panel, or event records.

   crossSectionalTimeSeriesPanelAndEventData(input)
5. Identifiers, Keys, Joins, and Data Grain verified

   Checks whether a proposed set of key columns really identifies one row each, which is what makes a table safe to join.

   identifiersKeysJoinsAndDataGrain(input)
6. Timestamps, Time Zones, Calendars, and Observation Time verified

   Audits a table's timestamps: whether the rows arrive in chronological order, whether every stamp carries a zone, and which rows sit at the two ends of the span.

   timestampsTimeZonesCalendarsAndObservationTime(input)
7. Missing, Nonfinite, Censored, and Truncated Values verified

   Tallies the ways a table's value column can fail to be an ordinary number, separating missing entries, non-finite ones and censored rows.

   missingNonfiniteCensoredAndTruncatedValues(input)
8. Measurement Error, Resolution, Accuracy, and Precision verified

   Compares repeated measurements against a known reference to separate accuracy from precision, and reads the instrument's resolution off the spacing of the values it reports.

   measurementErrorResolutionAccuracyAndPrecision(input)
9. Revisions, Vintages, and Point-in-Time Availability verified

   Picks the value that was genuinely knowable at a given moment by keeping only the vintages published on or before it, which is what stops a backtest reading the future.

   revisionsVintagesAndPointInTimeAvailability(input)
10. Data Provenance, Lineage, Ownership, and Licensing verified

    Checks a dataset's provenance record against the five fields the engine treats as mandatory and reports which of them are absent.

    dataProvenanceLineageOwnershipAndLicensing(input)

What they share#

Every topic here is a record-transform, so once you have called one the rest follow the same shape. Import paths differ only in the final segment:

import { observationsEntitiesVariablesAndDatasets } from "fintech-algorithms/foundations/data-variables-samples-and-measurement/observations-entities-variables-and-datasets";
import { numericCategoricalOrdinalAndBinaryVariables } from "fintech-algorithms/foundations/data-variables-samples-and-measurement/numeric-categorical-ordinal-and-binary-variables";

Read them in the order above — the sequence is pedagogical, not alphabetical.

Where this sits#

Financial Mathematics, Statistics, and Data Foundations collects 120 algorithms across 12 families. For the concept behind this family rather than the call signatures, see the concept guides.