rollcast: Probabilistic Forecasting with Adaptive Mixtures of Rolling Statistics

Implements a probabilistic time-series forecasting framework based on adaptive mixtures of rolling statistical anchors. Rolling means, medians, minimum and maximum values, regression endpoints, and user-specified quantiles define candidate forecast locations. A proper-score gating model assigns state-dependent mixture weights, optional state-conditional residual sampling adds local dispersion, and recursive simulation produces marginal and joint predictive distributions. Numeric hyperparameters can be supplied as scalars or candidate vectors for causal validation-based selection.

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