#MoonMissingness

#Positioning

#Core capabilities

• Diagnose rectangular shape, missing rates, observed counts, row/column summaries, joint patterns, pairwise relationships, and conditional signals.
• Screen missingness mechanisms with documented MCAR/MAR engineering heuristics, and generate deterministic synthetic missingness for tests.
• Impute with constant, mean, median, nearest-neighbour, regression, forward and backward sequence filling, rolling, hot-deck, iterative MICE-style, ensemble, and uncertainty-aware strategies.
• Parse and normalize CSV data, including quoted fields, escaped quotes, missing-value tokens, numeric conversion, schema profiling, and row filters.
• Evaluate repairs with deterministic holdouts, MAE/RMSE, completeness reports, strategy selection, calibration, and reproducible benchmark checksums.

#Quick start

///|
let dataset : Array[Array[Double?]] = [
  [Some(1.0), None, Some(3.0)],
  [Some(2.0), Some(4.0), None],
  [None, None, Some(9.0)],
]

///|
let rates = @diagnose.missing_rate(dataset)

///|
let report = @diagnose.missing_rate_report(dataset)

///|
let filled = @impute.impute_median(dataset)

///|
let with_indicators = @impute.append_missing_indicators(dataset)

#CLI

moon run cmd/main
moon run --target native cmd/bench

#Architecture

• diagnose: validation, summaries, pattern analysis, matrices, simulation, sampling, and missingness screening.
• impute: simple, KNN, sequence, regression, iterative, robust, ensemble, uncertainty, calibration, pipeline, and evaluation helpers.
• csv: dependency-free CSV parsing, profiling, normalization, and transforms.
• cmd/main: minimal usage example.
• cmd/bench: reproducible native benchmark.

#Benchmark

mean_impute_checksum=64851810.91227517
knn_impute_checksum=64779603.6
mice_impute_checksum=64893757.64677953
holdout_cells=1061
holdout_mae=Some(649.1812912346851)
holdout_rmse=Some(750.081167953114)

#Tests

moon check --deny-warn
moon test --deny-warn
moon check --target all
moon test --target all

#Continuous integration

#License