R-universe search: fft

ndphillips

FFTrees:Generate, Visualise, and Evaluate Fast-and-Frugal Decision Trees

Create, visualize, and test fast-and-frugal decision trees (FFTs) using the algorithms and methods described by Phillips, Neth, Woike & Gaissmaier (2017), <doi:10.1017/S1930297500006239>. FFTs are simple and transparent decision trees for solving binary classification problems. FFTs can be preferable to more complex algorithms because they require very little information, are easy to understand and communicate, and are robust against overfitting.

Maintained by Hansjoerg Neth. Last updated 6 months ago.

36.3 match 136 stars 9.53 score 144 scripts

krahim

fftwtools:Wrapper for 'FFTW3' Includes: One-Dimensional, Two-Dimensional, Three-Dimensional, and Multivariate Transforms

Provides a wrapper for several 'FFTW' functions. This package provides access to the two-dimensional 'FFT', the multivariate 'FFT', and the one-dimensional real to complex 'FFT' using the 'FFTW3' library. The package includes the functions fftw() and mvfftw() which are designed to mimic the functionality of the R functions fft() and mvfft(). The 'FFT' functions have a parameter that allows them to not return the redundant complex conjugate when the input is real data.

Maintained by Karim Rahim. Last updated 9 months ago.

fftw3

8.1 match 10 stars 8.78 score 24 scripts 64 dependents

abarbour

psd:Adaptive, Sine-Multitaper Power Spectral Density and Cross Spectrum Estimation

Produces power spectral density estimates through iterative refinement of the optimal number of sine-tapers at each frequency. This optimization procedure is based on the method of Riedel and Sidorenko (1995), which minimizes the Mean Square Error (sum of variance and bias) at each frequency, but modified for computational stability. The same procedure can now be used to calculate the cross spectrum (multivariate analyses).

Maintained by Andrew J. Barbour. Last updated 2 years ago.

multitaper power-spectral-density power-spectrum psd spectral-density-estimates spectrum openblas cpp

8.7 match 9 stars 7.12 score 122 scripts 1 dependents

mlverse

torch:Tensors and Neural Networks with 'GPU' Acceleration

Provides functionality to define and train neural networks similar to 'PyTorch' by Paszke et al (2019) <doi:10.48550/arXiv.1912.01703> but written entirely in R using the 'libtorch' library. Also supports low-level tensor operations and 'GPU' acceleration.

Maintained by Daniel Falbel. Last updated 5 days ago.

autograd deep-learning torch cpp

3.3 match 521 stars 16.50 score 1.4k scripts 39 dependents

martin3141

spant:MR Spectroscopy Analysis Tools

Tools for reading, visualising and processing Magnetic Resonance Spectroscopy data. The package includes methods for spectral fitting: Wilson (2021) <DOI:10.1002/mrm.28385> and spectral alignment: Wilson (2018) <DOI:10.1002/mrm.27605>.

Maintained by Martin Wilson. Last updated 4 days ago.

brain mri mrs mrshub spectroscopy fortran

6.3 match 25 stars 8.52 score 81 scripts

asgr

imager:Image Processing Library Based on 'CImg'

Fast image processing for images in up to 4 dimensions (two spatial dimensions, one time/depth dimension, one colour dimension). Provides most traditional image processing tools (filtering, morphology, transformations, etc.) as well as various functions for easily analysing image data using R. The package wraps 'CImg', <http://cimg.eu>, a simple, modern C++ library for image processing.

Maintained by Aaron Robotham. Last updated 8 days ago.

libx11 fftw3 tiff cpp openmp

3.3 match 17 stars 13.53 score 2.4k scripts 44 dependents

daniellga

harmonium:Audio analysis and I/O

Audio analysis and I/O.

Maintained by Daniel Gurgel. Last updated 5 months ago.

audio-analysis audio-decoder audio-resampling fft stft rust cargo alsa-lib

11.0 match 3 stars 3.93 score 33 scripts

spatstat

spatstat.explore:Exploratory Data Analysis for the 'spatstat' Family

Functionality for exploratory data analysis and nonparametric analysis of spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.

Maintained by Adrian Baddeley. Last updated 12 days ago.

cluster-detection confidence-intervals hypothesis-testing k-function roc-curves scan-statistics significance-testing simulation-envelopes spatial-analysis spatial-data-analysis spatial-sharpening spatial-smoothing spatial-statistics

4.1 match 1 stars 10.18 score 67 scripts 150 dependents

spsanderson

healthyR.ts:The Time Series Modeling Companion to 'healthyR'

Hospital time series data analysis workflow tools, modeling, and automations. This library provides many useful tools to review common administrative time series hospital data. Some of these include average length of stay, and readmission rates. The aim is to provide a simple and consistent verb framework that takes the guesswork out of everything.

Maintained by Steven Sanderson. Last updated 6 months ago.

ai arima-forecasting arima-model ets forecasting ggplot2 machine-learning modeling prophet time-series time-series-analysis workflows

5.3 match 19 stars 7.58 score 56 scripts 1 dependents

uligges

fftw:Fast FFT and DCT Based on the FFTW Library

Provides a simple and efficient wrapper around the fastest Fourier transform in the west (FFTW) library <http://www.fftw.org/>.

Maintained by Uwe Ligges. Last updated 6 months ago.

fftw3

8.0 match 4.92 score 39 scripts 17 dependents

gjmvanboxtel

gsignal:Signal Processing

R implementation of the 'Octave' package 'signal', containing a variety of signal processing tools, such as signal generation and measurement, correlation and convolution, filtering, filter design, filter analysis and conversion, power spectrum analysis, system identification, decimation and sample rate change, and windowing.

Maintained by Geert van Boxtel. Last updated 2 months ago.

signal-processing signals cpp

3.9 match 24 stars 10.07 score 133 scripts 34 dependents

seil85

spectral:Common Methods of Spectral Data Analysis

On discrete data spectral analysis is performed by Fourier and Hilbert transforms as well as with model based analysis called Lomb-Scargle method. Fragmented and irregularly spaced data can be processed in almost all methods. Both, FFT as well as LOMB methods take multivariate data and return standardized PSD. For didactic reasons an analytical approach for deconvolution of noise spectra and sampling function is provided. A user friendly interface helps to interpret the results.

Maintained by Martin Seilmayer. Last updated 4 years ago.

13.3 match 2.81 score 36 scripts 1 dependents

r-forge

signal:Signal Processing

A set of signal processing functions originally written for 'Matlab' and 'Octave'. Includes filter generation utilities, filtering functions, resampling routines, and visualization of filter models. It also includes interpolation functions.

Maintained by Uwe Ligges. Last updated 1 years ago.

fortran

4.0 match 8.78 score 828 scripts 151 dependents

kaskr

RTMB:'R' Bindings for 'TMB'

Native 'R' interface to 'TMB' (Template Model Builder) so models can be written entirely in 'R' rather than 'C++'. Automatic differentiation, to any order, is available for a rich subset of 'R' features, including linear algebra for dense and sparse matrices, complex arithmetic, Fast Fourier Transform, probability distributions and special functions. 'RTMB' provides easy access to model fitting and validation following the principles of Kristensen, K., Nielsen, A., Berg, C. W., Skaug, H., & Bell, B. M. (2016) <DOI:10.18637/jss.v070.i05> and Thygesen, U.H., Albertsen, C.M., Berg, C.W. et al. (2017) <DOI:10.1007/s10651-017-0372-4>.

Maintained by Kasper Kristensen. Last updated 8 hours ago.

openblas cpp

3.3 match 54 stars 10.46 score 394 scripts 9 dependents

jonathanlees

RSEIS:Seismic Time Series Analysis Tools

Multiple interactive codes to view and analyze seismic data, via spectrum analysis, wavelet transforms, particle motion, hodograms. Includes general time-series tools, plotting, filtering, interactive display.

Maintained by Jonathan M. Lees. Last updated 7 months ago.

6.0 match 3 stars 4.27 score 262 scripts 4 dependents

dnychka

fields:Tools for Spatial Data

For curve, surface and function fitting with an emphasis on splines, spatial data, geostatistics, and spatial statistics. The major methods include cubic, and thin plate splines, Kriging, and compactly supported covariance functions for large data sets. The splines and Kriging methods are supported by functions that can determine the smoothing parameter (nugget and sill variance) and other covariance function parameters by cross validation and also by restricted maximum likelihood. For Kriging there is an easy to use function that also estimates the correlation scale (range parameter). A major feature is that any covariance function implemented in R and following a simple format can be used for spatial prediction. There are also many useful functions for plotting and working with spatial data as images. This package also contains an implementation of sparse matrix methods for large spatial data sets and currently requires the sparse matrix (spam) package. Use help(fields) to get started and for an overview. The fields source code is deliberately commented and provides useful explanations of numerical details as a companion to the manual pages. The commented source code can be viewed by expanding the source code version and looking in the R subdirectory. The reference for fields can be generated by the citation function in R and has DOI <doi:10.5065/D6W957CT>. Development of this package was supported in part by the National Science Foundation Grant 1417857, the National Center for Atmospheric Research, and Colorado School of Mines. See the Fields URL for a vignette on using this package and some background on spatial statistics.

Maintained by Douglas Nychka. Last updated 9 months ago.

fortran

2.0 match 15 stars 12.70 score 7.7k scripts 297 dependents

florianpein

stepR:Multiscale Change-Point Inference

Allows fitting of step-functions to univariate serial data where neither the number of jumps nor their positions is known by implementing the multiscale regression estimators SMUCE, simulataneous multiscale changepoint estimator, (K. Frick, A. Munk and H. Sieling, 2014) <doi:10.1111/rssb.12047> and HSMUCE, heterogeneous SMUCE, (F. Pein, H. Sieling and A. Munk, 2017) <doi:10.1111/rssb.12202>. In addition, confidence intervals for the change-point locations and bands for the unknown signal can be obtained.

Maintained by Pein Florian. Last updated 6 months ago.

openblas cpp

6.8 match 1 stars 3.58 score 64 scripts

kaskr

RTMBp:'R' Bindings for 'TMB'

Native 'R' interface to 'TMB' (Template Model Builder) so models can be written entirely in 'R' rather than 'C++'. Automatic differentiation, to any order, is available for a rich subset of 'R' features, including linear algebra for dense and sparse matrices, complex arithmetic, Fast Fourier Transform, probability distributions and special functions. 'RTMBp' provides easy access to model fitting and validation following the principles of Kristensen, K., Nielsen, A., Berg, C. W., Skaug, H., & Bell, B. M. (2016) <DOI:10.18637/jss.v070.i05> and Thygesen, U.H., Albertsen, C.M., Berg, C.W. et al. (2017) <DOI:10.1007/s10651-017-0372-4>.

Maintained by Kasper Kristensen. Last updated 2 months ago.

openblas cpp openmp

3.3 match 51 stars 6.44 score 1 scripts

tsmodels

tsmarch:Multivariate ARCH Models

Feasible Multivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models including Dynamic Conditional Correlation (DCC), Copula GARCH and Generalized Orthogonal GARCH with Generalized Hyperbolic distribution. A review of some of these models can be found in Boudt, Galanos, Payseur and Zivot (2019) <doi:10.1016/bs.host.2019.01.001>.

Maintained by Alexios Galanos. Last updated 7 days ago.

econometrics finance garch multivariate-timeseries time-series openblas cpp

3.5 match 7 stars 5.80 score 3 scripts

mikkelvembye

AIscreenR:AI Screening Tools in R for Systematic Reviewing

Provides functions to conduct title and abstract screening in systematic reviews using large language models, such as the Generative Pre-trained Transformer (GPT) models from 'OpenAI' <https://platform.openai.com/>. These functions can enhance the quality of title and abstract screenings while reducing the total screening time significantly. In addition, the package includes tools for quality assessment of title and abstract screenings, as described in Vembye, Christensen, Mølgaard, and Schytt (2024) <DOI:10.31219/osf.io/yrhzm>.

Maintained by Mikkel H. Vembye. Last updated 3 months ago.

gpt openai screening systematic-review

3.3 match 10 stars 6.11 score 7 scripts

clobatofern95

GPUmatrix:Basic Linear Algebra with GPU

GPUs are great resources for data analysis, especially in statistics and linear algebra. Unfortunately, very few packages connect R to the GPU, and none of them are transparent enough to run the computations on the GPU without substantial changes to the code. The maintenance of these packages is cumbersome: several of the earlier attempts have been removed from their respective repositories. It would be desirable to have a properly maintained R package that takes advantage of the GPU with minimal changes to the existing code. We have developed the GPUmatrix package (available on CRAN). GPUmatrix mimics the behavior of the Matrix package and extends R to use the GPU for computations. It includes single(FP32) and double(FP64) precision data types, and provides support for sparse matrices. It is easy to learn, and requires very few code changes to perform the operations on the GPU. GPUmatrix relies on either the Torch or Tensorflow R packages to perform the GPU operations. We have demonstrated its usefulness for several statistical applications and machine learning applications: non-negative matrix factorization, logistic regression and general linear models. We have also included a comparison of GPU and CPU performance on different matrix operations.

Maintained by Cesar Lobato-Fernandez. Last updated 1 years ago.

5.1 match 3.93 score 57 scripts 1 dependents

thk686

fftab:Tidy Manipulation of Fourier Transformed Data

The 'fftab' package stores Fourier coefficients in a tibble and allows their manipulation in various ways. Functions are available for converting between complex, rectangular ('re', 'im'), and polar ('mod', 'arg') representations, as well as for extracting components as vectors or matrices. Inputs can include vectors, time series, and arrays of arbitrary dimensions, which are restored to their original form when inverting the transform. Since 'fftab' stores Fourier frequencies as columns in the tibble, many standard operations on spectral data can be easily performed using tidy packages like 'dplyr'.

Maintained by Timothy Keitt. Last updated 2 months ago.

5.5 match 3.54 score 3 scripts

tomaskrehlik

frequencyConnectedness:Spectral Decomposition of Connectedness Measures

Accompanies a paper (Barunik, Krehlik (2018) <doi:10.1093/jjfinec/nby001>) dedicated to spectral decomposition of connectedness measures and their interpretation. We implement all the developed estimators as well as the historical counterparts. For more information, see the help or GitHub page (<https://github.com/tomaskrehlik/frequencyConnectedness>) for relevant information.

Maintained by Tomas Krehlik. Last updated 2 years ago.

3.3 match 100 stars 5.88 score 50 scripts 1 dependents

fabsig

spate:Spatio-Temporal Modeling of Large Data Using a Spectral SPDE Approach

Functionality for spatio-temporal modeling of large data sets is provided. A Gaussian process in space and time is defined through a stochastic partial differential equation (SPDE). The SPDE is solved in the spectral space, and after discretizing in time and space, a linear Gaussian state space model is obtained. When doing inference, the main computational difficulty consists in evaluating the likelihood and in sampling from the full conditional of the spectral coefficients, or equivalently, the latent space-time process. In comparison to the traditional approach of using a spatio-temporal covariance function, the spectral SPDE approach is computationally advantageous. See Sigrist, Kuensch, and Stahel (2015) <doi:10.1111/rssb.12061> for more information on the methodology. This package aims at providing tools for two different modeling approaches. First, the SPDE based spatio-temporal model can be used as a component in a customized hierarchical Bayesian model (HBM). The functions of the package then provide parameterizations of the process part of the model as well as computationally efficient algorithms needed for doing inference with the HBM. Alternatively, the adaptive MCMC algorithm implemented in the package can be used as an algorithm for doing inference without any additional modeling. The MCMC algorithm supports data that follow a Gaussian or a censored distribution with point mass at zero. Covariates can be included in the model through a regression term.

Maintained by Fabio Sigrist. Last updated 1 years ago.

fftw3

4.3 match 1 stars 4.16 score 29 scripts

roaldarbol

animovement:An R toolbox for analysing animal movement across space and time

An R toolbox for analysing animal movement across space and time.

Maintained by Mikkel Roald-Arbøl. Last updated 3 months ago.

animal-behaviour animal-movement neuroethology neuroscience

3.5 match 10 stars 4.81 score 8 scripts

cefet-rj-dal

harbinger:A Unified Time Series Event Detection Framework

By analyzing time series, it is possible to observe significant changes in the behavior of observations that frequently characterize events. Events present themselves as anomalies, change points, or motifs. In the literature, there are several methods for detecting events. However, searching for a suitable time series method is a complex task, especially considering that the nature of events is often unknown. This work presents Harbinger, a framework for integrating and analyzing event detection methods. Harbinger contains several state-of-the-art methods described in Salles et al. (2020) <doi:10.5753/sbbd.2020.13626>.

Maintained by Eduardo Ogasawara. Last updated 4 months ago.

1.9 match 18 stars 8.32 score 216 scripts

tylermorganwall

rayimage:Image Processing for Simulated Cameras

Uses convolution-based techniques to generate simulated camera bokeh, depth of field, and other camera effects, using an image and an optional depth map. Accepts both filename inputs and in-memory array representations of images and matrices. Includes functions to perform 2D convolutions, reorient and resize images/matrices, add image and text overlays, generate camera vignette effects, and add titles to images.

Maintained by Tyler Morgan-Wall. Last updated 2 months ago.

cpp

2.0 match 52 stars 7.67 score 22 scripts 12 dependents

petolau

TSrepr:Time Series Representations

Methods for representations (i.e. dimensionality reduction, preprocessing, feature extraction) of time series to help more accurate and effective time series data mining. Non-data adaptive, data adaptive, model-based and data dictated (clipped) representation methods are implemented. Also various normalisation methods (min-max, z-score, Box-Cox, Yeo-Johnson), and forecasting accuracy measures are implemented.

Maintained by Peter Laurinec. Last updated 5 years ago.

data-analysis data-mining data-mining-algorithms data-science representation time-series time-series-analysis time-series-classification time-series-clustering time-series-data-mining time-series-representations cpp

2.0 match 97 stars 7.23 score 117 scripts

bentaylor1

lgcp:Log-Gaussian Cox Process

Spatial and spatio-temporal modelling of point patterns using the log-Gaussian Cox process. Bayesian inference for spatial, spatiotemporal, multivariate and aggregated point processes using Markov chain Monte Carlo. See Benjamin M. Taylor, Tilman M. Davies, Barry S. Rowlingson, Peter J. Diggle (2015) <doi:10.18637/jss.v063.i07>.

Maintained by Benjamin M. Taylor. Last updated 1 years ago.

3.9 match 3.43 score 27 scripts

khliland

baseline:Baseline Correction of Spectra

Collection of baseline correction algorithms, along with a framework and a Tcl/Tk enabled GUI for optimising baseline algorithm parameters. Typical use of the package is for removing background effects from spectra originating from various types of spectroscopy and spectrometry, possibly optimizing this with regard to regression or classification results. Correction methods include polynomial fitting, weighted local smoothers and many more.

Maintained by Kristian Hovde Liland. Last updated 10 months ago.

1.9 match 9 stars 7.07 score 74 scripts 12 dependents

adafede

cascade:Contextualizing untargeted Annotation with Semi-quantitative Charged Aerosol Detection for pertinent characterization of natural Extracts

This package provides the infrastructure to perform Automated Composition Assessment of Natural Extracts.

Maintained by Adriano Rutz. Last updated 4 days ago.

metabolite annotation charged aerosol detector semi-quantitative natural products computational metabolomics specialized metabolome

2.3 match 2 stars 5.76 score 40 scripts 1 dependents

dkimstatlab

SynchWave:Synchrosqueezed Wavelet Transform

The synchrosqueezed wavelet transform is implemented. The package is a translation of MATLAB Synchrosqueezing Toolbox, version 1.1 originally developed by Eugene Brevdo (2012). The C code for curve_ext was authored by Jianfeng Lu, and translated to Fortran by Dongik Jang. Synchrosqueezing is based on the papers: [1] Daubechies, I., Lu, J. and Wu, H. T. (2011) Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis, 30. 243-261. [2] Thakur, G., Brevdo, E., Fukar, N. S. and Wu, H-T. (2013) The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications. Signal Processing, 93, 1079-1094.

Maintained by Donghoh Kim. Last updated 3 years ago.

fortran

6.1 match 2 stars 2.08 score 20 scripts 1 dependents

zeehio

sgolay:Efficient Savitzky-Golay Filtering

Smoothing signals and computing their derivatives is a common requirement in signal processing workflows. Savitzky-Golay filters are a established method able to do both (Savitzky and Golay, 1964 <doi:10.1021/ac60214a047>). This package implements one dimensional Savitzky-Golay filters that can be applied to vectors and matrices (either row-wise or column-wise). Vectorization and memory allocations have been profiled to reduce computational fingerprint. Short filter lengths are implemented in the direct space, while longer filters are implemented in frequency space, using a Fast Fourier Transform (FFT).

Maintained by Sergio Oller Moreno. Last updated 2 years ago.

openblas

3.6 match 7 stars 3.54 score 2 scripts

matrix-profile-foundation

tsmp:Time Series with Matrix Profile

A toolkit implementing the Matrix Profile concept that was created by CS-UCR <http://www.cs.ucr.edu/~eamonn/MatrixProfile.html>.

Maintained by Francisco Bischoff. Last updated 3 years ago.

algorithm matrix-profile motif-search time-series cpp

1.7 match 72 stars 7.29 score 179 scripts 1 dependents

bioc

nucleR:Nucleosome positioning package for R

Nucleosome positioning for Tiling Arrays and NGS experiments.

Maintained by Alba Sala. Last updated 5 months ago.

nucleosomepositioning coverage chipseq microarray sequencing genetics qualitycontrol dataimport

2.3 match 5.32 score 21 scripts

fj86

PoissonBinomial:Efficient Computation of Ordinary and Generalized Poisson Binomial Distributions

Efficient implementations of multiple exact and approximate methods as described in Hong (2013) <doi:10.1016/j.csda.2012.10.006>, Biscarri, Zhao & Brunner (2018) <doi:10.1016/j.csda.2018.01.007> and Zhang, Hong & Balakrishnan (2018) <doi:10.1080/00949655.2018.1440294> for computing the probability mass, cumulative distribution and quantile functions, as well as generating random numbers for both the ordinary and generalized Poisson binomial distribution.

Maintained by Florian Junge. Last updated 7 months ago.

fftw3 cpp

2.4 match 3 stars 4.87 score 10 scripts 2 dependents

mmaechler

longmemo:Statistics for Long-Memory Processes (Book Jan Beran), and Related Functionality

Datasets and Functionality from 'Jan Beran' (1994). Statistics for Long-Memory Processes; Chapman & Hall. Estimation of Hurst (and more) parameters for fractional Gaussian noise, 'fARIMA' and 'FEXP' models.

Maintained by Martin Maechler. Last updated 8 months ago.

2.3 match 2 stars 5.10 score 46 scripts 4 dependents

cran

wavethresh:Wavelets Statistics and Transforms

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.

Maintained by Guy Nason. Last updated 7 months ago.

1.8 match 5.90 score 41 dependents

rpkgs

rtrend:Trend Estimating Tools

The traditional linear regression trend, Modified Mann-Kendall (MK) non-parameter trend and bootstrap trend are included in this package. Linear regression trend is rewritten by '.lm.fit'. MK trend is rewritten by 'Rcpp'. Finally, those functions are about 10 times faster than previous version in R. Reference: Hamed, K. H., & Rao, A. R. (1998). A modified Mann-Kendall trend test for autocorrelated data. Journal of hydrology, 204(1-4), 182-196. <doi:10.1016/S0022-1694(97)00125-X>.

Maintained by Dongdong Kong. Last updated 1 years ago.

openblas cpp openmp

2.3 match 3 stars 4.56 score 24 scripts

georgeweigt

itsmr:Time Series Analysis Using the Innovations Algorithm

Provides functions for modeling and forecasting time series data. Forecasting is based on the innovations algorithm. A description of the innovations algorithm can be found in the textbook "Introduction to Time Series and Forecasting" by Peter J. Brockwell and Richard A. Davis. <https://link.springer.com/book/10.1007/b97391>.

Maintained by George Weigt. Last updated 3 years ago.

4.3 match 2.34 score 218 scripts

dipterix

ravetools:Signal and Image Processing Toolbox for Analyzing Intracranial Electroencephalography Data

Implemented fast and memory-efficient Notch-filter, Welch-periodogram, discrete wavelet spectrogram for minutes of high-resolution signals, fast 3D convolution, image registration, 3D mesh manipulation; providing fundamental toolbox for intracranial Electroencephalography (iEEG) pipelines. Documentation and examples about 'RAVE' project are provided at <https://rave.wiki>, and the paper by John F. Magnotti, Zhengjia Wang, Michael S. Beauchamp (2020) <doi:10.1016/j.neuroimage.2020.117341>; see 'citation("ravetools")' for details.

Maintained by Zhengjia Wang. Last updated 19 days ago.

fftw3 cpp

1.7 match 3 stars 5.13 score 20 scripts 1 dependents

mandymejia

fMRItools:Routines for Common fMRI Processing Tasks

Supports fMRI (functional magnetic resonance imaging) analysis tasks including reading in 'CIFTI', 'GIFTI' and 'NIFTI' data, temporal filtering, nuisance regression, and aCompCor (anatomical Components Correction) (Muschelli et al. (2014) <doi:10.1016/j.neuroimage.2014.03.028>).

Maintained by Amanda Mejia. Last updated 25 days ago.

1.7 match 2 stars 5.10 score 35 scripts 4 dependents

romanflury

mrbsizeR:Scale Space Multiresolution Analysis of Random Signals

A method for the multiresolution analysis of spatial fields and images to capture scale-dependent features. mrbsizeR is based on scale space smoothing and uses differences of smooths at neighbouring scales for finding features on different scales. To infer which of the captured features are credible, Bayesian analysis is used. The scale space multiresolution analysis has three steps: (1) Bayesian signal reconstruction. (2) Using differences of smooths, scale-dependent features of the reconstructed signal can be found. (3) Posterior credibility analysis of the differences of smooths created. The method has first been proposed by Holmstrom, Pasanen, Furrer, Sain (2011) <DOI:10.1016/j.csda.2011.04.011>. Matlab code is available under <http://cc.oulu.fi/~lpasanen/MRBSiZer/>.

Maintained by Roman Flury. Last updated 5 years ago.

cpp

1.8 match 1 stars 4.22 score 33 scripts

gillian-earthscope

IRISSeismic:Classes and Methods for Seismic Data Analysis

Provides classes and methods for seismic data analysis. The base classes and methods are inspired by the python code found in the 'ObsPy' python toolbox <https://github.com/obspy/obspy>. Additional classes and methods support data returned by web services provided by EarthScope. <https://service.earthscope.org/>.

Maintained by Gillian Sharer. Last updated 4 months ago.

2.3 match 3.18 score 50 scripts 1 dependents

eogasawara

tspredit:Time Series Prediction Integrated Tuning

Prediction is one of the most important activities while working with time series. There are many alternative ways to model the time series. Finding the right one is challenging to model them. Most data-driven models (either statistical or machine learning) demand tuning. Setting them right is mandatory for good predictions. It is even more complex since time series prediction also demands choosing a data pre-processing that complies with the chosen model. Many time series frameworks have features to build and tune models. The package differs as it provides a framework that seamlessly integrates tuning data pre-processing activities with the building of models. The package provides functions for defining and conducting time series prediction, including data pre(post)processing, decomposition, tuning, modeling, prediction, and accuracy assessment. More information is available at Izau et al. <doi:10.5753/sbbd.2022.224330>.

Maintained by Eduardo Ogasawara. Last updated 4 months ago.

2.3 match 2.92 score 56 scripts

andrewthomasjones

logKDE:Computing Log-Transformed Kernel Density Estimates for Positive Data

Computes log-transformed kernel density estimates for positive data using a variety of kernels. It follows the methods described in Jones, Nguyen and McLachlan (2018) <doi:10.21105/joss.00870>.

Maintained by Andrew Thomas Jones. Last updated 7 years ago.

cpp

1.7 match 1 stars 3.78 score 12 scripts

mbeauvai

AFM:Atomic Force Microscope Image Analysis

Provides Atomic Force Microscope images analysis such as Gaussian mixes identification, Power Spectral Density, roughness against lengthscale, experimental variogram and variogram models, fractal dimension and scale, 2D network analysis. The AFM images can be exported to STL format for 3D printing.

Maintained by Mathieu Beauvais. Last updated 4 years ago.

1.9 match 1 stars 2.96 score 92 scripts

mrparker909

quickNmix:Asymptotic N-Mixture Model Fitting

For fitting N-mixture models using either FFT or asymptotic approaches. FFT N-mixture models extend the work of Cowen et al. (2017) <doi:10.1111/biom.12701>. Asymptotic N-mixture models extend the work of Dail and Madsen (2011) <doi:10.1111/j.1541-0420.2010.01465.x>, to consider asymptotic solutions to the open population N-mixture models. The FFT models are derived and described in "Parker, M.R.P., Elliott, L., Cowen, L.L.E. (2022). Computational efficiency and precision for replicated-count and batch-marked hidden population models [Manuscript in preparation]. Department of Statistics and Actuarial Sciences, Simon Fraser University.". The asymptotic models are derived and described in: "Parker, M.R.P., Elliott, L., Cowen, L.L.E., Cao, J. (2022). Fast asymptotic solutions for N-mixtures on large populations [Manuscript in preparation]. Department of Statistics and Actuarial Sciences, Simon Fraser University.".

Maintained by Matthew RP Parker. Last updated 3 years ago.

2.7 match 1.70 score 5 scripts

cran

smoothie:Two-Dimensional Field Smoothing

Perform two-dimensional smoothing for spatial fields using FFT and the convolution theorem (see Gilleland 2013, <doi:10.5065/D61834G2>).

Maintained by Eric Gilleland. Last updated 4 months ago.

2.2 match 1.78 score 2 dependents

yili-hong

poibin:The Poisson Binomial Distribution

Implementation of both the exact and approximation methods for computing the cdf of the Poisson binomial distribution as described in Hong (2013) <doi: 10.1016/j.csda.2012.10.006>. It also provides the pmf, quantile function, and random number generation for the Poisson binomial distribution. The C code for fast Fourier transformation (FFT) is written by R Core Team (2019)<https://www.R-project.org/>, which implements the FFT algorithm in Singleton (1969) <doi: 10.1109/TAU.1969.1162042>.

Maintained by Yili Hong. Last updated 7 months ago.

0.8 match 3 stars 4.99 score 80 scripts 9 dependents

cran

TSSS:Time Series Analysis with State Space Model

Functions for statistical analysis, modeling and simulation of time series with state space model, based on the methodology in Kitagawa (2020, ISBN: 978-0-367-18733-0).

Maintained by Masami Saga. Last updated 2 years ago.

fortran

1.9 match 2 stars 1.78 score

cran

mSTEM:Multiple Testing of Local Extrema for Detection of Change Points

A new approach to detect change points based on smoothing and multiple testing, which is for long data sequence modeled as piecewise constant functions plus stationary Gaussian noise, see Dan Cheng and Armin Schwartzman (2015) <arXiv:1504.06384>.

Maintained by Zhibing He. Last updated 6 years ago.

1.7 match 1.70 score

yhankcheng

psdr:Use Time Series to Generate and Compare Power Spectral Density

Functions that allow you to generate and compare power spectral density (PSD) plots given time series data. Fast Fourier Transform (FFT) is used to take a time series data, analyze the oscillations, and then output the frequencies of these oscillations in the time series in the form of a PSD plot.Thus given a time series, the dominant frequencies in the time series can be identified. Additional functions in this package allow the dominant frequencies of multiple groups of time series to be compared with each other. To see example usage with the main functions of this package, please visit this site: <https://yhhc2.github.io/psdr/articles/Introduction.html>. The mathematical operations used to generate the PSDs are described in these sites: <https://www.mathworks.com/help/matlab/ref/fft.html>. <https://www.mathworks.com/help/signal/ug/power-spectral-density-estimates-using-fft.html>.

Maintained by Yong-Han Hank Cheng. Last updated 4 years ago.

0.9 match 3.00 score 4 scripts

cran

timsac:Time Series Analysis and Control Package

Functions for statistical analysis, prediction and control of time series based mainly on Akaike and Nakagawa (1988) <ISBN 978-90-277-2786-2>.

Maintained by Masami Saga. Last updated 2 years ago.

fortran

1.8 match 1 stars 1.48 score 1 dependents

martakarass

runstats:Fast Computation of Running Statistics for Time Series

Provides methods for fast computation of running sample statistics for time series. These include: (1) mean, (2) standard deviation, and (3) variance over a fixed-length window of time-series, (4) correlation, (5) covariance, and (6) Euclidean distance (L2 norm) between short-time pattern and time-series. Implemented methods utilize Convolution Theorem to compute convolutions via Fast Fourier Transform (FFT).

Maintained by Marta Karas. Last updated 3 years ago.

0.5 match 2 stars 5.03 score 18 scripts 1 dependents

cran

dSTEM:Multiple Testing of Local Extrema for Detection of Change Points

Simultaneously detect the number and locations of change points in piecewise linear models under stationary Gaussian noise allowing autocorrelated random noise. The core idea is to transform the problem of detecting change points into the detection of local extrema (local maxima and local minima)through kernel smoothing and differentiation of the data sequence, see Cheng et al. (2020) <doi:10.1214/20-EJS1751>. A low-computational and fast algorithm call 'dSTEM' is introduced to detect change points based on the 'STEM' algorithm in D. Cheng and A. Schwartzman (2017) <doi:10.1214/16-AOS1458>.

Maintained by Zhibing He. Last updated 2 years ago.

1.8 match 1.00 score

cran

ShapePattern:Tools for Analyzing Shapes and Patterns

This is an evolving and growing collection of tools for the quantification, assessment, and comparison of shape and pattern. This collection provides tools for: (1) the spatial decomposition of planar shapes using 'ShrinkShape' to incrementally shrink shapes to extinction while computing area, perimeter, and number of parts at each iteration of shrinking; the spectra of results are returned in graphic and tabular formats (Remmel 2015) <doi:10.1111/cag.12222>, (2) simulating landscape patterns, (3) provision of tools for estimating composition and configuration parameters from a categorical (binary) landscape map (grid) and then simulates a selected number of statistically similar landscapes. Class-focused pattern metrics are computed for each simulated map to produce empirical distributions against which statistical comparisons can be made. The code permits the analysis of single maps or pairs of maps (Remmel and Fortin 2013) <doi:10.1007/s10980-013-9905-x>, (4) counting the number of each first-order pattern element and converting that information into both frequency and empirical probability vectors (Remmel 2020) <doi:10.3390/e22040420>, and (5) computing the porosity of raster patches <doi:10.3390/su10103413>. NOTE: This is a consolidation of existing packages ('PatternClass', 'ShapePattern') to begin warehousing all shape and pattern code in a common package. Additional utility tools for handling data are provided and this package will be added to as more tools are created, cleaned-up, and documented. Note that all future developments will appear in this package and that 'PatternClass' will eventually be archived.

Maintained by Tarmo K. Remmel. Last updated 4 months ago.

1.7 match 1.00 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.

0.5 match 2.48 score 2 scripts 1 dependents

cran

ShiftConvolvePoibin:Exactly Computing the Tail of the Poisson-Binomial Distribution

An exact method for computing the Poisson-Binomial Distribution (PBD). The package provides a function for generating a random sample from the PBD, as well as two distinct approaches for computing the density, distribution, and quantile functions of the PBD. The first method uses direct-convolution, or a dynamic-programming approach which is numerically stable but can be slow for a large input due to its quadratic complexity. The second method is much faster on large inputs thanks to its use of Fast Fourier Transform (FFT) based convolutions. Notably in this case the package uses an exponential shift to practically guarantee the relative accuracy of the computation of an arbitrarily small tail of the PBD -- something that FFT-based methods often struggle with. This ShiftConvolvePoiBin method is described in Peres, Lee and Keich (2020) <arXiv:2004.07429> where it is also shown to be competitive with the fastest implementations for exactly computing the entire Poisson-Binomial distribution.

Maintained by Andrew Lee. Last updated 5 years ago.

0.8 match 1.48 score 1 dependents

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 6 months ago.

0.5 match 2.00 score

cran

sazedR:Parameter-Free Domain-Agnostic Season Length Detection in Time Series

Spectral and Average Autocorrelation Zero Distance Density ('sazed') is a method for estimating the season length of a seasonal time series. 'sazed' is aimed at practitioners, as it employs only domain-agnostic preprocessing and does not depend on parameter tuning or empirical constants. The computation of 'sazed' relies on the efficient autocorrelation computation methods suggested by Thibauld Nion (2012, URL: <https://etudes.tibonihoo.net/literate_musing/autocorrelations.html>) and by Bob Carpenter (2012, URL: <https://lingpipe-blog.com/2012/06/08/autocorrelation-fft-kiss-eigen/>).

Maintained by Tiago Santos. Last updated 5 years ago.

0.5 match 1.70 score

yili-hong

PoissonMultinomial:The Poisson-Multinomial Distribution

Implementation of the exact, normal approximation, and simulation-based methods for computing the probability mass function (pmf) and cumulative distribution function (cdf) of the Poisson-Multinomial distribution, together with a random number generator for the distribution. The exact method is based on multi-dimensional fast Fourier transformation (FFT) of the characteristic function of the Poisson-Multinomial distribution. The normal approximation method uses a multivariate normal distribution to approximate the pmf of the distribution based on central limit theorem. The simulation method is based on the law of large numbers. Details about the methods are available in Lin, Wang, and Hong (2022) <DOI:10.1007/s00180-022-01299-0>.

Maintained by Yili Hong. Last updated 1 years ago.

fftw3 cpp

0.5 match 1 stars 1.00 score