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model$alpha,
model$symmetric, model$integrate,
model$scorecast, model$lr,
model$Csat and model$KI are now collected in
model$args.forecast moved from Imports to
Depends, so it is attached together with
conformalForecast.acmcp() now accepts ma_method = "CSS" for
faster MA scorecaster fitting at longer forecast horizons, while
retaining "CSS-ML" as the default, and reports successful
scorecasts in scorecast_times.cvforecast(), update(), and the evaluation
functions.scp() and
acp(), ensuring that the infinite finite-sample correction
is retained with the correct sign.kess = TRUE now requires a continuous quantile
estimator (quantiletype = 4:9); an informative error is
returned for discontinuous quantile estimators.acmcp() no longer fits MA models before its recursive
scorecaster inputs are available.update() now works on pid(),
acmcp() and acp(symmetric = TRUE) objects, and
resumes from the last computed step instead of recomputing the whole
history.update() now replays stored arguments, preserves
forecasts at all frequencies, validates new data and external
regressors, and handles failed final model fits. External regressors are
retained by pid() and acmcp(), enabling
external-regressor updates for all four conformal methods.pid() and acmcp() can be called with their
documented defaults.print() and summary() no longer repeat the
cross-validation header.cvforecast() no longer warns about arguments
legitimately passed to forecastfun, and survives a failing
final model fit.cp_times is reported per forecast horizon.coverage() and width() accept
x, LOWER and UPPER through
..., and require a single level.lagmatrix() handles lags larger than the number of
rows.scp() and acp() when
ncal is too large.math-rendering: mathjax).Winkler
measure name, the lagmatrix() error message, and several
typos.conformal() interface,
incremental updates, external regressors, clearer method descriptions,
and updated ggplot2 calls.vignette("update"), on extending a fitted conformal
pipeline with newly arrived observations instead of refitting it from
scratch.conformal(),
and describes each method, its key formula and its tuning arguments in
more detail.coverage() and
width() and cpforecast accuracy, and use fixed
random seeds for reproducibility.scp (split conformal prediction)acp (adaptive conformal prediction)pid (conformal PID control method)acmcp (autocorrelated multi-step conformal
prediction).These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.