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This release improves how the frequency window for smoothness estimation is chosen. By default the confidence bands are now narrower, and regression bands are always finite. The previous behaviour is still available (see the note below).
A new default window rule
(methods_get_xi = "snr"). The upper cutoff
frequency is now chosen from the signal-to-noise ratio of the empirical
Fourier transform. The original rule of Schennach (2020) relies on a
worst-case bound that selects no frequency at realistic sample sizes, so
the package used to fall back to a wide window that gave a flat
smoothness envelope and very wide bands. The new rule always returns a
usable window and produces confidence intervals roughly 0.4 times as
wide.
More accurate regression bias bounds. Two
corrections are now on by default. noise_floor = "auto"
uses the noise floor appropriate for a general response, and
envelope_use_Y = TRUE fits the smoothness envelope to the
joint spectrum of Y and X rather than the
marginal spectrum of X, which had under-estimated the
bias.
Finite bands for difficult cases
(integer_r = TRUE). When the data do not show a
clear power-law decay, the fitted slope can fall below the minimum the
method assumes. It is now raised to that minimum and the amplitude
refit, which keeps the bias bound finite. This avoids the very wide
bands that could otherwise appear for very smooth densities or
polynomial conditional means. A warning is shown when this adjustment is
made.
Refreshed plots. Density, regression, and
Fourier-transform plots now use a cleaner theme with a
colorblind-friendly palette and a legend. The Fourier-transform plot
shows a wider frequency range with the selected window shaded, so you
can see where the signal gives way to noise; the new
xi_range and expand arguments control the
displayed range. Custom colors through fill_ci and
fill_bias still work.
methods_get_xi = "Schennach_loose",
noise_floor = "compact", and
envelope_use_Y = FALSE.First stable release with a modern S3 interface, performance improvements, and expanded documentation.
bbnp_density, bbnp_regression).This release focuses on modernizing the package interface and improving performance.
Modern S3 interface:
biasBound_density() and
biasBound_condExpectation() now return S3 objects
(bbnp_density, bbnp_regression) with standard
methods: print(), summary(),
plot(), coef(), confint(), and
fitted() (regression).
More faithful implementation of Schennach (2020):
Performance improvements:
Docs & usability:
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.
Health stats visible at Monitor.