R-universe search: exports:downsample

Showing 14 of total 14 results (show query)

bioc

S4Arrays:Foundation of array-like containers in Bioconductor

The S4Arrays package defines the Array virtual class to be extended by other S4 classes that wish to implement a container with an array-like semantic. It also provides: (1) low-level functionality meant to help the developer of such container to implement basic operations like display, subsetting, or coercion of their array-like objects to an ordinary matrix or array, and (2) a framework that facilitates block processing of array-like objects (typically on-disk objects).

Maintained by Hervé Pagès. Last updated 2 months ago.

infrastructure datarepresentation bioconductor-package core-package

5 stars 10.99 score 8 scripts 1.2k dependents

welch-lab

rliger:Linked Inference of Genomic Experimental Relationships

Uses an extension of nonnegative matrix factorization to identify shared and dataset-specific factors. See Welch J, Kozareva V, et al (2019) <doi:10.1016/j.cell.2019.05.006>, and Liu J, Gao C, Sodicoff J, et al (2020) <doi:10.1038/s41596-020-0391-8> for more details.

Maintained by Yichen Wang. Last updated 2 months ago.

nonnegative-matrix-factorization single-cell openblas cpp

408 stars 10.77 score 334 scripts 1 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

24 stars 10.07 score 133 scripts 34 dependents

bioc

matter:Out-of-core statistical computing and signal processing

Toolbox for larger-than-memory scientific computing and visualization, providing efficient out-of-core data structures using files or shared memory, for dense and sparse vectors, matrices, and arrays, with applications to nonuniformly sampled signals and images.

Maintained by Kylie A. Bemis. Last updated 4 months ago.

infrastructure datarepresentation dataimport dimensionreduction preprocessing cpp

57 stars 9.52 score 64 scripts 2 dependents

ludvigolsen

groupdata2:Creating Groups from Data

Methods for dividing data into groups. Create balanced partitions and cross-validation folds. Perform time series windowing and general grouping and splitting of data. Balance existing groups with up- and downsampling or collapse them to fewer groups.

Maintained by Ludvig Renbo Olsen. Last updated 3 months ago.

balance cross-validation data data-frame fold group-factor groups participants partition split staircase

27 stars 9.04 score 338 scripts 7 dependents

r-forge

tuneR:Analysis of Music and Speech

Analyze music and speech, extract features like MFCCs, handle wave files and their representation in various ways, read mp3, read midi, perform steps of a transcription, ... Also contains functions ported from the 'rastamat' 'Matlab' package.

Maintained by Uwe Ligges. Last updated 12 months ago.

7.93 score 1.1k scripts 44 dependents

edzer

trajectories:Classes and Methods for Trajectory Data

Classes and methods for trajectory data, with support for nesting individual Track objects in track sets (Tracks) and track sets for different entities in collections of Tracks. Methods include selection, generalization, aggregation, intersection, simulation, and plotting.

Maintained by Edzer Pebesma. Last updated 7 months ago.

31 stars 7.25 score 76 scripts 1 dependents

hannahlowens

voluModel:Modeling Species Distributions in Three Dimensions

Facilitates modeling species' ecological niches and geographic distributions based on occurrences and environments that have a vertical as well as horizontal component, and projecting models into three-dimensional geographic space. Working in three dimensions is useful in an aquatic context when the organisms one wishes to model can be found across a wide range of depths in the water column. The package also contains functions to automatically generate marine training model training regions using machine learning, and interpolate and smooth patchily sampled environmental rasters using thin plate splines. Davis Rabosky AR, Cox CL, Rabosky DL, Title PO, Holmes IA, Feldman A, McGuire JA (2016) <doi:10.1038/ncomms11484>. Nychka D, Furrer R, Paige J, Sain S (2021) <doi:10.5065/D6W957CT>. Pateiro-Lopez B, Rodriguez-Casal A (2022) <https://CRAN.R-project.org/package=alphahull>.

Maintained by Hannah L. Owens. Last updated 16 days ago.

9 stars 6.60 score 35 scripts

demsarjure

autohrf:Automated Generation of Data-Informed GLM Models in Task-Based fMRI Data Analysis

Analysis of task-related functional magnetic resonance imaging (fMRI) activity at the level of individual participants is commonly based on general linear modelling (GLM) that allows us to estimate to what extent the blood oxygenation level dependent (BOLD) signal can be explained by task response predictors specified in the GLM model. The predictors are constructed by convolving the hypothesised timecourse of neural activity with an assumed hemodynamic response function (HRF). To get valid and precise estimates of task response, it is important to construct a model of neural activity that best matches actual neuronal activity. The construction of models is most often driven by predefined assumptions on the components of brain activity and their duration based on the task design and specific aims of the study. However, our assumptions about the onset and duration of component processes might be wrong and can also differ across brain regions. This can result in inappropriate or suboptimal models, bad fitting of the model to the actual data and invalid estimations of brain activity. Here we present an approach in which theoretically driven models of task response are used to define constraints based on which the final model is derived computationally using the actual data. Specifically, we developed 'autohrf' — a package for the 'R' programming language that allows for data-driven estimation of HRF models. The package uses genetic algorithms to efficiently search for models that fit the underlying data well. The package uses automated parameter search to find the onset and duration of task predictors which result in the highest fitness of the resulting GLM based on the fMRI signal under predefined restrictions. We evaluate the usefulness of the 'autohrf' package on publicly available datasets of task-related fMRI activity. Our results suggest that by using 'autohrf' users can find better task related brain activity models in a quick and efficient manner.

Maintained by Jure Demšar. Last updated 1 years ago.

2 stars 4.72 score 13 scripts

bioc

scmeth:Functions to conduct quality control analysis in methylation data

Functions to analyze methylation data can be found here. Some functions are relevant for single cell methylation data but most other functions can be used for any methylation data. Highlight of this workflow is the comprehensive quality control report.

Maintained by Divy Kangeyan. Last updated 5 months ago.

dnamethylation qualitycontrol preprocessing singlecell immunooncology bioconductor-package methylation single-cell-methylation

4.70 score 5 scripts

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.

3 stars 4.27 score 262 scripts 4 dependents

cran

Mercator:Clustering and Visualizing Distance Matrices

Defines the classes used to explore, cluster and visualize distance matrices, especially those arising from binary data. See Abrams and colleagues, 2021, <doi:10.1093/bioinformatics/btab037>.

Maintained by Kevin R. Coombes. Last updated 5 months ago.

clustering

4.26 score 1 dependents

akkyro

PupilPre:Preprocessing Pupil Size Data

Pupillometric data collected using SR Research Eyelink eye trackers requires significant preprocessing. This package contains functions for preparing pupil dilation data for visualization and statistical analysis. Specifically, it provides a pipeline of functions which aid in data validation, the removal of blinks/artifacts, downsampling, and baselining, among others. Additionally, plotting functions for creating grand average and conditional average plots are provided. See the vignette for samples of the functionality. The package is designed for handling data collected with SR Research Eyelink eye trackers using Sample Reports created in SR Research Data Viewer.

Maintained by Aki-Juhani Kyröläinen. Last updated 5 years ago.

2.81 score 13 scripts

jkh1

LOMAR:Localization Microscopy Data Analysis

Read, register and compare point sets from single molecule localization microscopy.

Maintained by Jean-Karim Heriche. Last updated 6 months ago.

cpp

1.70 score 1 scripts