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  "Description": "Implements controlled interrupted time series (CITS)\nanalysis for evaluating interventions in comparative\ntime-series data. The package provides tools for preparing\npanel time-series datasets, fitting models using generalized\nleast squares (GLS) with optional autoregressive–moving-average\n(ARMA) error structures, and computing fitted values and robust\nstandard errors using cluster-robust variance estimators (CR2).\nVisualization functions enable clear presentation of estimated\neffects and counterfactual trajectories following\ninterventions. Background on methods for causal inference in\ninterrupted time series can be found in Linden and Adams (2011)\n<doi:10.1111/j.1365-2753.2010.01504.x> and Lopez Bernal,\nCummins, and Gasparrini (2018) <doi:10.1093/ije/dyy135>.",
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