R-universe search: needs:wavethresh

stephenslab

EbayesThresh:Empirical Bayes Thresholding and Related Methods

Empirical Bayes thresholding using the methods developed by I. M. Johnstone and B. W. Silverman. The basic problem is to estimate a mean vector given a vector of observations of the mean vector plus white noise, taking advantage of possible sparsity in the mean vector. Within a Bayesian formulation, the elements of the mean vector are modelled as having, independently, a distribution that is a mixture of an atom of probability at zero and a suitable heavy-tailed distribution. The mixing parameter can be estimated by a marginal maximum likelihood approach. This leads to an adaptive thresholding approach on the original data. Extensions of the basic method, in particular to wavelet thresholding, are also implemented within the package.

Maintained by Peter Carbonetto. Last updated 7 years ago.

5 stars 8.95 score 54 scripts 14 dependents

jacobkap

caesar:Encrypts and Decrypts Strings

Encrypts and decrypts strings using either the Caesar cipher or a pseudorandom number generation (using set.seed()) method.

Maintained by Jacob Kaplan. Last updated 5 years ago.

1 stars 6.01 score 26 scripts

lisalibungan

shapeR:Collection and Analysis of Otolith Shape Data

Studies otolith shape variation among fish populations. Otoliths are calcified structures found in the inner ear of teleost fish and their shape has been known to vary among several fish populations and stocks, making them very useful in taxonomy, species identification and to study geographic variations. The package extends previously described software used for otolith shape analysis by allowing the user to automatically extract closed contour outlines from a large number of images, perform smoothing to eliminate pixel noise, choose from conducting either a Fourier or wavelet transform to the outlines and visualize the mean shape. The output of the package are independent Fourier or wavelet coefficients which can be directly imported into a wide range of statistical packages in R. The package might prove useful in studies of any two dimensional objects.

Maintained by Lisa Anne Libungan. Last updated 7 years ago.

9 stars 4.45 score 21 scripts

cran

adlift:An Adaptive Lifting Scheme Algorithm

Adaptive wavelet lifting transforms for signal denoising using optimal local neighbourhood regression, from Nunes et al. (2006) <doi:10.1007/s11222-006-6560-y>.

Maintained by Matt Nunes. Last updated 2 years ago.

openblas

4.33 score 9 dependents

trambakbanerjee

asus:Adaptive SURE Thresholding Using Side Information

Provides the ASUS procedure for estimating a high dimensional sparse parameter in the presence of auxiliary data that encode side information on sparsity. It is a robust data combination procedure in the sense that even when pooling non-informative auxiliary data ASUS would be at least as efficient as competing soft thresholding based methods that do not use auxiliary data. For more information, please see the paper Adaptive Sparse Estimation with Side Information by Banerjee, Mukherjee and Sun (JASA 2020).

Maintained by Trambak Banerjee. Last updated 2 years ago.

3 stars 4.29 score 13 scripts

bioc

CONFESS:Cell OrderiNg by FluorEScence Signal

Single Cell Fluidigm Spot Detector.

Maintained by Diana LOW. Last updated 5 months ago.

immunooncology geneexpression dataimport cellbiology clustering rnaseq qualitycontrol visualization timecourse regression classification

3.90 score 2 scripts

cran

binhf:Haar-Fisz Functions for Binomial Data

Binomial Haar-Fisz transforms for Gaussianization as in Nunes and Nason (2009).

Maintained by Matt Nunes. Last updated 7 years ago.

3.85 score 3 dependents

euanmcgonigle

TrendLSW:Wavelet Methods for Analysing Locally Stationary Time Series

Fitting models for, and simulation of, trend locally stationary wavelet (TLSW) time series models, which take account of time-varying trend and dependence structure in a univariate time series. The TLSW model, and its estimation, is described in McGonigle, Killick and Nunes (2022a) <doi:10.1111/jtsa.12643>, (2022b) <doi:10.1214/22-EJS2044>. New users will likely want to start with the TLSW function.

Maintained by Euan T. McGonigle. Last updated 11 months ago.

nonparametric-regression spectral-analysis spectrum time-series time-series-analysis wavelets

1 stars 3.30 score 3 scripts

bioc

deltaGseg:deltaGseg

Identifying distinct subpopulations through multiscale time series analysis

Maintained by Diana Low. Last updated 5 months ago.

proteomics timecourse visualization clustering

3.30 score 2 scripts

ecologicaltools

IBRtools:Integrating Biomarker-Based Assessments and Radarchart Creation

Several functions to calculate two important indexes (IBR (Integrated Biomarker Response) and IBRv2 (Integrated Biological Response version 2)), it also calculates the standardized values for enzyme activity for each index, and it has a graphing function to perform radarplots that make great data visualization for this type of data. Beliaeff, B., & Burgeot, T. (2002). <https://pubmed.ncbi.nlm.nih.gov/12069320/>. Sanchez, W., Burgeot, T., & Porcher, J.-M. (2013).<doi:10.1007/s11356-012-1359-1>. Devin, S., Burgeot, T., Giambérini, L., Minguez, L., & Pain-Devin, S. (2014). <doi:10.1007/s11356-013-2169-9>. Minato N. (2022). <https://minato.sip21c.org/msb/>.

Maintained by Anna Carolina Resende. Last updated 2 years ago.

data-visualization devtools enzyme-activity ibr ibrv indexes-ibr integrated-biomarker-response radarchart rstudio-cloud starplot

3 stars 3.18 score 2 scripts

haydarde

dLagM:Time Series Regression Models with Distributed Lag Models

Provides time series regression models with one predictor using finite distributed lag models, polynomial (Almon) distributed lag models, geometric distributed lag models with Koyck transformation, and autoregressive distributed lag models. It also consists of functions for computation of h-step ahead forecasts from these models. See Demirhan (2020)(<doi:10.1371/journal.pone.0228812>) and Baltagi (2011)(<doi:10.1007/978-3-642-20059-5>) for more information.

Maintained by Haydar Demirhan. Last updated 1 years ago.

2 stars 3.18 score 127 scripts

nilotpalsanyal

BHMSMAfMRI:Bayesian Hierarchical Multi-Subject Multiscale Analysis of Functional MRI (fMRI) Data

Package BHMSMAfMRI performs Bayesian hierarchical multi-subject multiscale analysis of fMRI data as described in Sanyal & Ferreira (2012) <DOI:10.1016/j.neuroimage.2012.08.041>, or other multiscale data, using wavelet based prior that borrows strength across subjects and provides posterior smoothed images of the effect sizes and samples from the posterior distribution.

Maintained by Nilotpal Sanyal. Last updated 2 years ago.

bayesian-hierarchical-models fmri-data-analysis multiscale-data wavelet-transform openblas cpp openmp

2.81 score 13 scripts

nilotpalsanyal

NLPwavelet:Bayesian Wavelet Analysis Using Non-Local Priors

Package NLPwavelet performs Bayesian wavelet analysis using individual non-local priors as described in Sanyal & Ferreira (2017) <DOI:10.1007/s13571-016-0129-3> and non-local prior mixtures as described in Sanyal (2025) <DOI:10.48550/arXiv.2501.18134>.

Maintained by Nilotpal Sanyal. Last updated 2 months ago.

openblas cpp openmp

2.70 score

dkimstatlab

GSD:Graph Signal Decomposition

Graph signals residing on the vertices of a graph have recently gained prominence in research in various fields. Many methodologies have been proposed to analyze graph signals by adapting classical signal processing tools. Recently, several notable graph signal decomposition methods have been proposed, which include graph Fourier decomposition based on graph Fourier transform, graph empirical mode decomposition, and statistical graph empirical mode decomposition. This package efficiently implements multiscale analysis applicable to various fields, and offers an effective tool for visualizing and decomposing graph signals. For the detailed methodology, see Ortega et al. (2018) <doi:10.1109/JPROC.2018.2820126>, Shuman et al. (2013) <doi:10.1109/MSP.2012.2235192>, Tremblay et al. (2014) <https://www.eurasip.org/Proceedings/Eusipco/Eusipco2014/HTML/papers/1569922141.pdf>, and Cho et al. (2024) "Statistical graph empirical mode decomposition by graph denoising and boundary treatment".

Maintained by Donghoh Kim. Last updated 1 years ago.

2.70 score

kylecaudle

rTensor2:MultiLinear Algebra

A set of tools for basic tensor operators. A tensor in the context of data analysis in a multidimensional array. The tools in this package rely on using any discrete transformation (e.g. Fast Fourier Transform (FFT)). Standard tools included are the Eigenvalue decomposition of a tensor, the QR decomposition and LU decomposition. Other functionality includes the inverse of a tensor and the transpose of a symmetric tensor. Functionality in the package is outlined in Kernfeld et al. (2015) <https://www.sciencedirect.com/science/article/pii/S0024379515004358>.

Maintained by Kyle Caudle. Last updated 1 years ago.

2.48 score 2 scripts 1 dependents

cran

nlt:A Nondecimated Lifting Transform for Signal Denoising

Uses a modified lifting algorithm on which it builds the nondecimated lifting transform. It has applications in wavelet shrinkage.

Maintained by Matt Nunes. Last updated 7 years ago.

2.08 score 4 dependents

cran

DDHFm:Variance Stabilization by Data-Driven Haar-Fisz (for Microarrays)

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.

Maintained by Guy Nason. Last updated 6 months ago.

2.00 score

kylecaudle

TensorTools:Multilinear Algebra

A set of tools for basic tensor operators. A tensor in the context of data analysis in a multidimensional array. The tools in this package rely on using any discrete transformation (e.g. Fast Fourier Transform (FFT)). Standard tools included are the Eigenvalue decomposition of a tensor, the QR decomposition and LU decomposition. Other functionality includes the inverse of a tensor and the transpose of a symmetric tensor. Functionality in the package is outlined in Kernfeld, E., Kilmer, M., and Aeron, S. (2015) <doi:10.1016/j.laa.2015.07.021>.

Maintained by Kyle Caudle. Last updated 5 months ago.

2.00 score

cran

locits:Test of Stationarity and Localized Autocovariance

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. <doi:10.1111/rssb.12015>.

Maintained by Guy Nason. Last updated 2 years ago.

1 stars 1.95 score 3 dependents

eckleyi

LS2W:Locally Stationary Two-Dimensional Wavelet Process Estimation Scheme

Estimates two-dimensional local wavelet spectra.

Maintained by Idris Eckley. Last updated 2 years ago.

1.88 score 25 scripts 1 dependents

cran

mvLSW:Multivariate, Locally Stationary Wavelet Process Estimation

Tools for analysing multivariate time series with wavelets. This includes: simulation of a multivariate locally stationary wavelet (mvLSW) process from a multivariate evolutionary wavelet spectrum (mvEWS); estimation of the mvEWS, local coherence and local partial coherence. See Park, Eckley and Ombao (2014) <doi:10.1109/TSP.2014.2343937> for details.

Maintained by Daniel Grose. Last updated 3 years ago.

1.78 score 2 dependents

cran

liftLRD:Wavelet Lifting Estimators of the Hurst Exponent for Regularly and Irregularly Sampled Time Series

Implementations of Hurst exponent estimators based on the relationship between wavelet lifting scales and wavelet energy of Knight et al (2017) <doi:10.1007/s11222-016-9698-2>.

Maintained by Matt Nunes. Last updated 2 years ago.

2 stars 1.78 score 1 dependents

cran

CNLTreg:Complex-Valued Wavelet Lifting for Signal Denoising

Implementations of recent complex-valued wavelet shrinkage procedures for smoothing irregularly sampled signals, see Hamilton et al (2018) <doi:10.1080/00401706.2017.1281846>.

Maintained by Matt Nunes. Last updated 7 years ago.

1.78 score 2 dependents

cran

AnomalyScore:Anomaly Scoring for Multivariate Time Series

Compute an anomaly score for multivariate time series based on the k-nearest neighbors algorithm. Different computations of distances between time series are provided.

Maintained by Guillermo Granados. Last updated 4 months ago.

1.70 score

cran

POCRE:Penalized Orthogonal-Components Regression

Penalized orthogonal-components regression (POCRE) is a supervised dimension reduction method for high-dimensional data. It sequentially constructs orthogonal components (with selected features) which are maximally correlated to the response residuals. POCRE can also construct common components for multiple responses and thus build up latent-variable models.

Maintained by Dabao Zhang. Last updated 3 years ago.

1.60 score

adunaic

lpacf:Local Partial Autocorrelation Function Estimation for Locally Stationary Wavelet Processes

Provides the method for computing the local partial autocorrelation function for locally stationary wavelet time series from Killick, Knight, Nason, Eckley (2020) <doi:10.1214/20-EJS1748>.

Maintained by Rebecca Killick. Last updated 2 years ago.

1.48 score 2 scripts 1 dependents

cran

waveband:Computes Credible Intervals for Bayesian Wavelet Shrinkage

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) <doi:10.1111/1467-9868.00332>.

Maintained by Guy Nason. Last updated 5 months ago.

fortran

1.30 score

vitara-p

icmm:Empirical Bayes Variable Selection via ICM/M Algorithm

Empirical Bayes variable selection via ICM/M algorithm for normal, binary logistic, and Cox's regression. The basic problem is to fit high-dimensional regression which sparse coefficients. This package allows incorporating the Ising prior to capture structure of predictors in the modeling process. More information can be found in the papers listed in the URL below.

Maintained by Vitara Pungpapong. Last updated 4 years ago.

1.28 score 19 scripts

lm186

grove:Wavelet Functional ANOVA Through Markov Groves

Functional denoising and functional ANOVA through wavelet-domain Markov groves. Fore more details see: Ma L. and Soriano J. (2018) Efficient functional ANOVA through wavelet-domain Markov groves. <arXiv:1602.03990v2 [stat.ME]>.

Maintained by Li Ma. Last updated 1 years ago.

openblas cpp

1.18 score 15 scripts

dkimstatlab

CVThresh:Level-Dependent Cross-Validation Thresholding

The level-dependent cross-validation method is implemented for the selection of thresholding value in wavelet shrinkage. This procedure is implemented by coupling a conventional cross validation with an imputation method due to a limitation of data length, a power of 2. It can be easily applied to classical leave-one-out and k-fold cross validation. Since the procedure is computationally fast, a level-dependent cross validation can be performed for wavelet shrinkage of various data such as a data with correlated errors.

Maintained by Donghoh Kim. Last updated 3 years ago.

1.04 score 11 scripts

kylecaudle

LTAR:Tensor Forecasting Functions

A set of tools for forecasting the next step in a multidimensional setting using tensors. In the examples, a forecast is made of sea surface temperatures of a geographic grid (i.e. lat/long). Each observation is a matrix, the entries in the matrix and the sea surface temperature at a particular lattitude/longitude. Cates, J., Hoover, R. C., Caudle, K., Kopp, R., & Ozdemir, C. (2021) "Transform-Based Tensor Auto Regression for Multilinear Time Series Forecasting" in 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 461-466), IEEE <doi:10.1109/ICMLA52953.2021.00078>.

Maintained by Kyle Caudle. Last updated 2 years ago.

1.00 score

cran

LS2Wstat:A Multiscale Test of Spatial Stationarity for LS2W Processes

Wavelet-based methods for testing stationarity and quadtree segmenting of images, see Taylor et al (2014) <doi:10.1080/00401706.2013.823890>.

Maintained by Matt Nunes. Last updated 2 years ago.

1 stars 1.00 score

cran

costat:Time Series Costationarity Determination

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) <doi:10.18637/jss.v055.i01>.

Maintained by Guy Nason. Last updated 2 years ago.

1.00 score

adunaic

forecastLSW:Forecasting Routines for Locally Stationary Wavelet Processes

Implementation to perform forecasting of locally stationary wavelet processes by examining the local second order structure of the time series.

Maintained by Rebecca Killick. Last updated 2 years ago.

1.00 score 3 scripts

cran

LSWPlib:Simulation and Spectral Estimation of Locally Stationary Wavelet Packet Processes

Library of functions for the statistical analysis and simulation of Locally Stationary Wavelet Packet (LSWP) processes. The methods implemented by this library are described in Cardinali and Nason (2017) <doi:10.1111/jtsa.12230>.

Maintained by Alessandro Cardinali. Last updated 3 years ago.

1.00 score

cran

mvLSWimpute:Imputation Methods for Multivariate Locally Stationary Time Series

Implementation of imputation techniques based on locally stationary wavelet time series forecasting methods from Wilson, R. E. et al. (2021) <doi:10.1007/s11222-021-09998-2>.

Maintained by Matt Nunes. Last updated 3 years ago.

1.00 score

cran

haarfisz:Software to Perform Haar Fisz Transforms

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. <doi:10.1198/106186004X2697>.

Maintained by Guy Nason. Last updated 2 years ago.

1.00 score

cran

hwwntest:Tests of White Noise using Wavelets

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) <doi:10.1002/sta4.69> are provided and, for comparison purposes this package also implements the B test of Bartlett (1967) <doi:10.2307/2333850>. Functionality is provided to compute an approximation to the theoretical power of the general wavelet test in the case of general ARMA alternatives.

Maintained by Guy Nason. Last updated 2 years ago.

1 stars 1.00 score

cran

CNLTtsa:Complex-Valued Wavelet Lifting for Univariate and Bivariate Time Series Analysis

Implementations of recent complex-valued wavelet spectral procedures for analysis of irregularly sampled signals, see Hamilton et al (2018) <doi:10.1080/00401706.2017.1281846>.

Maintained by Matt Nunes. Last updated 7 years ago.

1.00 score

cran

CliftLRD:Complex-Valued Wavelet Lifting Estimators of the Hurst Exponent for Irregularly Sampled Time Series

Implementation of Hurst exponent estimators based on complex-valued lifting wavelet energy from Knight, M. I and Nunes, M. A. (2018) <doi:10.1007/s11222-018-9820-8>.

Maintained by Matt Nunes. Last updated 7 years ago.

1.00 score

cran

BootWPTOS:Test Stationarity using Bootstrap Wavelet Packet Tests

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 <doi:10.1016/j.acha.2016.06.006>.

Maintained by Guy Nason. Last updated 3 years ago.

1.00 score