idionomics 0.1.0

Initial CRAN release.

Core algorithm

• iarimax() — per-subject ARIMAX fitting via forecast::auto.arima() followed by random-effects meta-analysis via metafor::rma(). Supports multi-predictor models, optional fixed_d for cross-subject comparability, and keep_models for retaining raw model objects.

Preprocessing

• i_screener() — pre-pipeline data quality screening on raw data. Three criteria: minimum observations (min_n_subject), minimum within-person SD (min_sd), and maximum modal response proportion (max_mode_pct). Supports "filter", "flag", and "report" output modes.

• pmstandardize() — within-person z-scoring (person-mean centering and person-SD scaling).

• i_detrender() — within-person linear detrending via lm(col ~ timevar). Per-column filtering with pre- and post-detrend variance guards.

Inference

• i_pval() — per-subject p-values using the two-tailed t-distribution with ML-based degrees of freedom (n_valid - n_params).

• sden_test() — Sign Divergence Test (SDT) and Equisyncratic Null Test (ENT) with automatic selection based on the pooled REMA p-value.

Loop detection

• looping_machine() — directed loop detection across three variables. Fits three iarimax() legs, applies i_pval(), and computes Loop_positive_directed.

S3 methods

• summary.iarimax_results() — subject counts, direction/significance counts, REMA estimates, and heterogeneity statistics.

• plot.iarimax_results() — caterpillar plot with per-subject confidence intervals and REMA band overlay.

• summary.sden_results() — test type, hypothesis, and binomial test results.