Weighted Adaptive Prediction with Structured Dependence R package wired version 1.0.1 Builds a joint probabilistic forecast across series and horizons using adaptive copulas (Gaussian/t) with shrinkage-repaired correlations. At the low level it calls a probabilistic mixer per series and horizon, which backtests several simple predictors, predicts next-window Continuous Ranked Probability Score (CRPS), and converts those scores into softmax weights to form a calibrated mixture (r/q/p/dfun). The mixer blends eight simple predictors: a naive predictor that wraps the last move in a PERT distribution; an arima predictor using auto.arima for one-step forecasts; an Exponentially Weighted Moving Average (EWMA) gaussian predictor with mean/variance under a Gaussian; a historical bootstrap predictor that resamples past horizon-aligned moves; a drift residual bootstrap predictor combining linear trend with bootstrapped residuals; a volatility-scaled naive predictor centering on the last move and scaling by recent volatility; a robust median mad predictor using median/MAD with Laplace or Normal shape; and a shrunk quantile predictor that fits a few quantile regressions over time and interpolates to a full predictive. The function then couples the per-series mixtures on a common transform (additive/multiplicative/log-multiplicative), simulates coherent draws, and returns both transformed- and level-scale samplers and summaries. Software Depends: R (>= 4.1.0) Imports: mc2d (>= 0.2.1), forecast (>= 8.24.0), quantreg (>= 6.1), MASS (>= 7.3-65), imputeTS (>= 3.4) Suggests: knitr, testthat (>= 3.0.0) Giancarlo Vercellino <giancarlo.vercellino@gmail.com> Comprehensive R Archive Network (CRAN) Giancarlo Vercellino [aut, cre, cph] GPL-3 2026-04-13 application/tgz https://CRAN.R-project.org/package=wired doi:10.32614/CRAN.package.wired