Changes in version 0.2.0                        

New features

  - Confidence bands via block bootstrap for all four filters
    (hp_filter(), hamilton_filter(), bhp_filter(), mbh_filter()). The
    new boot_iter and block_size arguments add $trend_lower /
    $trend_upper to the result: a 95% normal-approximation band (trend
    ± 1.96 * sd) built from a Circular Block Bootstrap of the cycle,
    with each replicate refit by the same estimator as the base fit.
  - New autoplot() method for macrofilter objects (ggplot2): draws the
    observed series, the estimated trend, and the confidence ribbon when
    present, with the time axis reconstructed from the stored temporal
    identity.
  - mbh_filter() gains hp_lambda to control the HP-based
    auto-calibration of the Huber threshold d when the input is a plain
    numeric vector whose true frequency is not annual.

Performance

  - The HP system matrix is now Cholesky-factorized once and reused
    across every bootstrap replicate (and every bHP inner iteration),
    instead of being re-factorized on each solve. This markedly speeds
    up hp_filter() and bhp_filter() with boot_iter > 0 (and the base bHP
    fit), with bit-identical results.

Other changes

  - The d = "auto" calibration in mbh_filter() now uses the MAD of the
    HP cyclical residual (output-gap scale) instead of mad(diff(y)), and
    reports the chosen value via a message().
  - Filters now return a list of class c("macrofilter", "list") and
    store the temporal identity (meta$ts_class, meta$tsp, meta$idx) so
    trend, cycle and bands can all be mapped back to dates for plotting.

Documentation

  - New vignette Uncertainty Bands via Block Bootstrap covering
    boot_iter, block_size, the end-point fan and the Hamilton
    conditional band.
  - mbh_filter() documents the mstop–d interaction (reducing mstop on
    long log-level series under-smooths the trend); hamilton_filter()
    documents the conditional bootstrap band behaviour.