R-universe search: exports:values<-

Showing 12 of total 12 results (show query)

bioc

GenomicRanges:Representation and manipulation of genomic intervals

The ability to efficiently represent and manipulate genomic annotations and alignments is playing a central role when it comes to analyzing high-throughput sequencing data (a.k.a. NGS data). The GenomicRanges package defines general purpose containers for storing and manipulating genomic intervals and variables defined along a genome. More specialized containers for representing and manipulating short alignments against a reference genome, or a matrix-like summarization of an experiment, are defined in the GenomicAlignments and SummarizedExperiment packages, respectively. Both packages build on top of the GenomicRanges infrastructure.

Maintained by Hervé Pagès. Last updated 4 months ago.

genetics infrastructure datarepresentation sequencing annotation genomeannotation coverage bioconductor-package core-package

44 stars 17.68 score 13k scripts 1.3k dependents

rspatial

terra:Spatial Data Analysis

Methods for spatial data analysis with vector (points, lines, polygons) and raster (grid) data. Methods for vector data include geometric operations such as intersect and buffer. Raster methods include local, focal, global, zonal and geometric operations. The predict and interpolate methods facilitate the use of regression type (interpolation, machine learning) models for spatial prediction, including with satellite remote sensing data. Processing of very large files is supported. See the manual and tutorials on <https://rspatial.org/> to get started. 'terra' replaces the 'raster' package ('terra' can do more, and it is faster and easier to use).

Maintained by Robert J. Hijmans. Last updated 9 hours ago.

geospatial raster spatial vector onetbb proj gdal geos cpp

560 stars 17.65 score 17k scripts 856 dependents

rspatial

raster:Geographic Data Analysis and Modeling

Reading, writing, manipulating, analyzing and modeling of spatial data. This package has been superseded by the "terra" package <https://CRAN.R-project.org/package=terra>.

Maintained by Robert J. Hijmans. Last updated 1 days ago.

cpp

163 stars 17.23 score 58k scripts 562 dependents

bioc

SummarizedExperiment:A container (S4 class) for matrix-like assays

The SummarizedExperiment container contains one or more assays, each represented by a matrix-like object of numeric or other mode. The rows typically represent genomic ranges of interest and the columns represent samples.

Maintained by Hervé Pagès. Last updated 5 months ago.

genetics infrastructure sequencing annotation coverage genomeannotation bioconductor-package core-package

34 stars 16.84 score 8.6k scripts 1.2k dependents

bioc

S4Vectors:Foundation of vector-like and list-like containers in Bioconductor

The S4Vectors package defines the Vector and List virtual classes and a set of generic functions that extend the semantic of ordinary vectors and lists in R. Package developers can easily implement vector-like or list-like objects as concrete subclasses of Vector or List. In addition, a few low-level concrete subclasses of general interest (e.g. DataFrame, Rle, Factor, and Hits) are implemented in the S4Vectors package itself (many more are implemented in the IRanges package and in other Bioconductor infrastructure packages).

Maintained by Hervé Pagès. Last updated 2 months ago.

infrastructure datarepresentation bioconductor-package core-package

18 stars 16.05 score 1.0k scripts 1.9k dependents

bioc

Gviz:Plotting data and annotation information along genomic coordinates

Genomic data analyses requires integrated visualization of known genomic information and new experimental data. Gviz uses the biomaRt and the rtracklayer packages to perform live annotation queries to Ensembl and UCSC and translates this to e.g. gene/transcript structures in viewports of the grid graphics package. This results in genomic information plotted together with your data.

Maintained by Robert Ivanek. Last updated 5 months ago.

visualization microarray sequencing

79 stars 13.05 score 1.4k scripts 46 dependents

nimble-dev

nimble:MCMC, Particle Filtering, and Programmable Hierarchical Modeling

A system for writing hierarchical statistical models largely compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models and do basic R-style math, and compiling both models and nimbleFunctions via custom-generated C++. 'NIMBLE' includes default methods for MCMC, Laplace Approximation, Monte Carlo Expectation Maximization, and some other tools. The nimbleFunction system makes it easy to do things like implement new MCMC samplers from R, customize the assignment of samplers to different parts of a model from R, and compile the new samplers automatically via C++ alongside the samplers 'NIMBLE' provides. 'NIMBLE' extends the 'BUGS'/'JAGS' language by making it extensible: New distributions and functions can be added, including as calls to external compiled code. Although most people think of MCMC as the main goal of the 'BUGS'/'JAGS' language for writing models, one can use 'NIMBLE' for writing arbitrary other kinds of model-generic algorithms as well. A full User Manual is available at <https://r-nimble.org>.

Maintained by Christopher Paciorek. Last updated 17 days ago.

bayesian-inference bayesian-methods hierarchical-models mcmc probabilistic-programming openblas cpp

169 stars 12.97 score 2.6k scripts 19 dependents

crunch-io

crunch:Crunch.io Data Tools

The Crunch.io service <https://crunch.io/> provides a cloud-based data store and analytic engine, as well as an intuitive web interface. Using this package, analysts can interact with and manipulate Crunch datasets from within R. Importantly, this allows technical researchers to collaborate naturally with team members, managers, and clients who prefer a point-and-click interface.

Maintained by Greg Freedman Ellis. Last updated 8 days ago.

9 stars 10.47 score 200 scripts 2 dependents

drjphughesjr

hash:Full Featured Implementation of Hash Tables/Associative Arrays/Dictionaries

Implements a data structure similar to hashes in Perl and dictionaries in Python but with a purposefully R flavor. For objects of appreciable size, access using hashes outperforms native named lists and vectors.

Maintained by John Hughes. Last updated 2 years ago.

1 stars 7.54 score 4.0k scripts 50 dependents

bioc

autonomics:Unified Statistical Modeling of Omics Data

This package unifies access to Statistal Modeling of Omics Data. Across linear modeling engines (lm, lme, lmer, limma, and wilcoxon). Across coding systems (treatment, difference, deviation, etc). Across model formulae (with/without intercept, random effect, interaction or nesting). Across omics platforms (microarray, rnaseq, msproteomics, affinity proteomics, metabolomics). Across projection methods (pca, pls, sma, lda, spls, opls). Across clustering methods (hclust, pam, cmeans). It provides a fast enrichment analysis implementation. And an intuitive contrastogram visualisation to summarize contrast effects in complex designs.

Maintained by Aditya Bhagwat. Last updated 2 months ago.

software dataimport preprocessing dimensionreduction principalcomponent regression differentialexpression genesetenrichment transcriptomics transcription geneexpression rnaseq microarray proteomics metabolomics massspectrometry

5.95 score 5 scripts

icosa-grid

icosa:Global Triangular and Penta-Hexagonal Grids Based on Tessellated Icosahedra

Implementation of icosahedral grids in three dimensions. The spherical-triangular tessellation can be set to create grids with custom resolutions. Both the primary triangular and their inverted penta-hexagonal grids can be calculated. Additional functions are provided that allow plotting of the grids and associated data, the interaction of the grids with other raster and vector objects, and treating the grids as a graphs.

Maintained by Adam T. Kocsis. Last updated 8 months ago.

grid cpp

4 stars 5.41 score 65 scripts

robinhankin

frab:How to Add Two R Tables

Methods to "add" two R tables; also an alternative interpretation of named vectors as generalized R tables, so that c(a=1,b=2,c=3) + c(b=3,a=-1) will return c(b=5,c=3). Uses 'disordR' discipline (Hankin, 2022, <doi:10.48550/arXiv.2210.03856>). Extraction and replacement methods are provided. The underlying mathematical structure is the Free Abelian group, hence the name. To cite in publications please use Hankin (2023) <doi:10.48550/arXiv.2307.13184>.

Maintained by Robin K. S. Hankin. Last updated 8 days ago.

cpp

1 stars 5.22 score 1 dependents