A toolkit for idionomic science, a research philosophy that places the unit of the ensemble (individual/couple/group) at the center of analysis. Rather than assuming a common distribution, a similar enough process for each unit, and fitting a single model to the whole ensemble, idionomic methods model each unit separately, then aggregate upward if sensible. The group-level picture emerges from individual results, not the other way around, while explicitly evaluating whether aggregation is reasonable given the measured level of heterogeneity of effects. The package is built around intensive longitudinal data where each participant contributes a time series. It provides a pipeline from preprocessing through modeling to group-level summaries. Current functions: data quality screening (i_screener()), within-person standardization (pmstandardize()), linear detrending (i_detrender()), per-subject ARIMAX (AutoRegressive Integrated Moving Average with eXogenous inputs) modeling and meta-analysis (iarimax()), individual p-values (i_pval()), Sign Divergence and Equisyncratic Null tests (sden_test()), and directed loop detection (looping_machine()). Methods are described in Hernandez et al. (2024) <doi:10.1007/978-3-030-77644-2_136-1>, Ciarrochi et al. (2024) <doi:10.1007/s10608-024-10486-w>, and Sahdra et al. (2024) <doi:10.1016/j.jcbs.2024.100728>.