NEWS

MacroFilters 0.2.1

mbh_filter()'s automatic knot count is now capped at 250 (min(max(20, floor(n / 2)), 250)). Series of 500 observations or fewer are unaffected; the cap only bounds the B-spline basis for long or high-frequency inputs, where extra knots inflate memory and runtime without adding flexibility (in a P-spline the difference penalty, not the knot count, controls smoothness).

Corrected the MBH parameter tables in the Introduction vignette: the d = "auto" default is calibrated from the MAD of the HP cyclical residual (not first differences), and the default learning rate is nu = 0.1.
Fixed the COVID-19 highlight in the Introduction cycle plot, which was anchored to stale fixed indices instead of the 2020 date window.

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.

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.

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.

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.