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).
Documentation
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.
MacroFilters 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.
