guynason.r-universe.dev

[guynason] locits 1.7.8

g.nason@imperial.ac.uk (Guy Nason) — Thu, 24 Jul 2025 16:10:13 GMT

Provides test of second-order stationarity for time series (for dyadic and arbitrary-n length data). Provides localized autocovariance, with confidence intervals, for locally stationary (nonstationary) time series. See Nason, G P (2013) "A test for second-order stationarity and approximate confidence intervals for localized autocovariance for locally stationary time series." Journal of the Royal Statistical Society, Series B, 75, 879-904. .

[guynason] waveband 4.7.4

g.nason@imperial.ac.uk (Guy Nason) — Mon, 04 Nov 2024 17:50:02 GMT

Computes Bayesian wavelet shrinkage credible intervals for nonparametric regression. The method uses cumulants to derive Bayesian credible intervals for wavelet regression estimates. The first four cumulants of the posterior distribution of the estimates are expressed in terms of the observed data and integer powers of the mother wavelet functions. These powers are closely approximated by linear combinations of wavelet scaling functions at an appropriate finer scale. Hence, a suitable modification of the discrete wavelet transform allows the posterior cumulants to be found efficiently for any data set. Johnson transformations then yield the credible intervals themselves. Barber, S., Nason, G.P. and Silverman, B.W. (2002) .

[guynason] DDHFm 1.1.4

g.nason@imperial.ac.uk (Guy Nason) — Sat, 05 Oct 2024 02:58:03 GMT

Contains the normalizing and variance stabilizing Data-Driven Haar-Fisz algorithm. Also contains related algorithms for simulating from certain microarray gene intensity models and evaluation of certain transformations. Contains cDNA and shipping credit flow data.

[guynason] wavethresh 4.7.3

g.nason@imperial.ac.uk (Guy Nason) — Tue, 20 Aug 2024 02:55:38 GMT

Performs 1, 2 and 3D real and complex-valued wavelet transforms, nondecimated transforms, wavelet packet transforms, nondecimated wavelet packet transforms, multiple wavelet transforms, complex-valued wavelet transforms, wavelet shrinkage for various kinds of data, locally stationary wavelet time series, nonstationary multiscale transfer function modeling, density estimation.

[guynason] hwwntest 1.3.2

g.nason@imperial.ac.uk (Guy Nason) — Wed, 13 Sep 2023 08:31:46 GMT

Provides methods to test whether time series is consistent with white noise. Two new tests based on Haar wavelets and general wavelets described by Nason and Savchev (2014) are provided and, for comparison purposes this package also implements the B test of Bartlett (1967) . Functionality is provided to compute an approximation to the theoretical power of the general wavelet test in the case of general ARMA alternatives.

[guynason] costat 2.4.1

g.nason@imperial.ac.uk (Guy Nason) — Wed, 06 Sep 2023 22:30:46 GMT

Contains functions that can determine whether a time series is second-order stationary or not (and hence evidence for locally stationarity). Given two non-stationary series (i.e. locally stationary series) this package can then discover time-varying linear combinations that are second-order stationary. Cardinali, A. and Nason, G.P. (2013) .

[guynason] haarfisz 4.5.4

g.nason@imperial.ac.uk (Guy Nason) — Fri, 01 Sep 2023 12:46:16 GMT

A Haar-Fisz algorithm for Poisson intensity estimation. Will denoise Poisson distributed sequences where underlying intensity is not constant. Uses the multiscale variance-stabilization method called the Haar-Fisz transform. Contains functions to carry out the forward and inverse Haar-Fisz transform and denoising on near-Gaussian sequences. Can also carry out cycle-spinning. Main reference: Fryzlewicz, P. and Nason, G.P. (2004) "A Haar-Fisz algorithm for Poisson intensity estimation." Journal of Computational and Graphical Statistics, 13, 621-638. .

[guynason] BootWPTOS 1.2.1

g.nason@imperial.ac.uk (Guy Nason) — Fri, 20 May 2022 11:40:02 GMT

Provides significance tests for second-order stationarity for time series using bootstrap wavelet packet tests. Provides functionality to visualize the time series with the results of the hypothesis tests superimposed. The methodology is described in Cardinali, A and Nason, G P (2016) "Practical powerful wavelet packet tests for second-order stationarity." Applied and Computational Harmonic Analysis, 44, 558-585 .