Showing 200 of total 3251 results (show query)

r-dbi

odbc:Connect to ODBC Compatible Databases (using the DBI Interface)

A DBI-compatible interface to ODBC databases.

Maintained by Hadley Wickham. Last updated 4 days ago.

databaseodbcunixodbccpp

396 stars 16.31 score 2.9k scripts 23 dependents

wesm

feather:R Bindings to the Feather 'API'

Read and write feather files, a lightweight binary columnar data store designed for maximum speed.

Maintained by Hadley Wickham. Last updated 4 years ago.

2.7k stars 12.61 score 3.9k scripts 5 dependents

r-dbi

bigrquery:An Interface to Google's 'BigQuery' 'API'

Easily talk to Google's 'BigQuery' database from R.

Maintained by Hadley Wickham. Last updated 1 months ago.

bigquerydatabasecpp

520 stars 12.47 score 1.8k scripts 4 dependents

truecluster

ff:Memory-Efficient Storage of Large Data on Disk and Fast Access Functions

The ff package provides data structures that are stored on disk but behave (almost) as if they were in RAM by transparently mapping only a section (pagesize) in main memory - the effective virtual memory consumption per ff object. ff supports R's standard atomic data types 'double', 'logical', 'raw' and 'integer' and non-standard atomic types boolean (1 bit), quad (2 bit unsigned), nibble (4 bit unsigned), byte (1 byte signed with NAs), ubyte (1 byte unsigned), short (2 byte signed with NAs), ushort (2 byte unsigned), single (4 byte float with NAs). For example 'quad' allows efficient storage of genomic data as an 'A','T','G','C' factor. The unsigned types support 'circular' arithmetic. There is also support for close-to-atomic types 'factor', 'ordered', 'POSIXct', 'Date' and custom close-to-atomic types. ff not only has native C-support for vectors, matrices and arrays with flexible dimorder (major column-order, major row-order and generalizations for arrays). There is also a ffdf class not unlike data.frames and import/export filters for csv files. ff objects store raw data in binary flat files in native encoding, and complement this with metadata stored in R as physical and virtual attributes. ff objects have well-defined hybrid copying semantics, which gives rise to certain performance improvements through virtualization. ff objects can be stored and reopened across R sessions. ff files can be shared by multiple ff R objects (using different data en/de-coding schemes) in the same process or from multiple R processes to exploit parallelism. A wide choice of finalizer options allows to work with 'permanent' files as well as creating/removing 'temporary' ff files completely transparent to the user. On certain OS/Filesystem combinations, creating the ff files works without notable delay thanks to using sparse file allocation. Several access optimization techniques such as Hybrid Index Preprocessing and Virtualization are implemented to achieve good performance even with large datasets, for example virtual matrix transpose without touching a single byte on disk. Further, to reduce disk I/O, 'logicals' and non-standard data types get stored native and compact on binary flat files i.e. logicals take up exactly 2 bits to represent TRUE, FALSE and NA. Beyond basic access functions, the ff package also provides compatibility functions that facilitate writing code for ff and ram objects and support for batch processing on ff objects (e.g. as.ram, as.ff, ffapply). ff interfaces closely with functionality from package 'bit': chunked looping, fast bit operations and coercions between different objects that can store subscript information ('bit', 'bitwhich', ff 'boolean', ri range index, hi hybrid index). This allows to work interactively with selections of large datasets and quickly modify selection criteria. Further high-performance enhancements can be made available upon request.

Maintained by Jens Oehlschlägel. Last updated 3 months ago.

cpp

27 stars 12.01 score 764 scripts 71 dependents

guido-s

netmeta:Network Meta-Analysis using Frequentist Methods

A comprehensive set of functions providing frequentist methods for network meta-analysis (Balduzzi et al., 2023) <doi:10.18637/jss.v106.i02> and supporting Schwarzer et al. (2015) <doi:10.1007/978-3-319-21416-0>, Chapter 8 "Network Meta-Analysis": - frequentist network meta-analysis following Rücker (2012) <doi:10.1002/jrsm.1058>; - additive network meta-analysis for combinations of treatments (Rücker et al., 2020) <doi:10.1002/bimj.201800167>; - network meta-analysis of binary data using the Mantel-Haenszel or non-central hypergeometric distribution method (Efthimiou et al., 2019) <doi:10.1002/sim.8158>, or penalised logistic regression (Evrenoglou et al., 2022) <doi:10.1002/sim.9562>; - rankograms and ranking of treatments by the Surface under the cumulative ranking curve (SUCRA) (Salanti et al., 2013) <doi:10.1016/j.jclinepi.2010.03.016>; - ranking of treatments using P-scores (frequentist analogue of SUCRAs without resampling) according to Rücker & Schwarzer (2015) <doi:10.1186/s12874-015-0060-8>; - split direct and indirect evidence to check consistency (Dias et al., 2010) <doi:10.1002/sim.3767>, (Efthimiou et al., 2019) <doi:10.1002/sim.8158>; - league table with network meta-analysis results; - 'comparison-adjusted' funnel plot (Chaimani & Salanti, 2012) <doi:10.1002/jrsm.57>; - net heat plot and design-based decomposition of Cochran's Q according to Krahn et al. (2013) <doi:10.1186/1471-2288-13-35>; - measures characterizing the flow of evidence between two treatments by König et al. (2013) <doi:10.1002/sim.6001>; - automated drawing of network graphs described in Rücker & Schwarzer (2016) <doi:10.1002/jrsm.1143>; - partial order of treatment rankings ('poset') and Hasse diagram for 'poset' (Carlsen & Bruggemann, 2014) <doi:10.1002/cem.2569>; (Rücker & Schwarzer, 2017) <doi:10.1002/jrsm.1270>; - contribution matrix as described in Papakonstantinou et al. (2018) <doi:10.12688/f1000research.14770.3> and Davies et al. (2022) <doi:10.1002/sim.9346>; - subgroup network meta-analysis.

Maintained by Guido Schwarzer. Last updated 10 days ago.

meta-analysisnetwork-meta-analysisrstudio

33 stars 11.84 score 199 scripts 10 dependents

bioc

bumphunter:Bump Hunter

Tools for finding bumps in genomic data

Maintained by Tamilselvi Guharaj. Last updated 5 months ago.

dnamethylationepigeneticsinfrastructuremultiplecomparisonimmunooncology

16 stars 11.61 score 210 scripts 43 dependents

projectmosaic

ggformula:Formula Interface to the Grammar of Graphics

Provides a formula interface to 'ggplot2' graphics.

Maintained by Randall Pruim. Last updated 1 years ago.

38 stars 11.55 score 1.7k scripts 25 dependents

bioc

annotate:Annotation for microarrays

Using R enviroments for annotation.

Maintained by Bioconductor Package Maintainer. Last updated 5 months ago.

annotationpathwaysgo

11.41 score 812 scripts 239 dependents

bioc

genefilter:genefilter: methods for filtering genes from high-throughput experiments

Some basic functions for filtering genes.

Maintained by Bioconductor Package Maintainer. Last updated 5 months ago.

microarrayfortrancpp

11.11 score 2.4k scripts 143 dependents

covid19datahub

COVID19:COVID-19 Data Hub

Unified datasets for a better understanding of COVID-19.

Maintained by Emanuele Guidotti. Last updated 1 months ago.

2019-ncovcoronaviruscovid-19covid-datacovid19-data

252 stars 11.08 score 265 scripts

milesmcbain

datapasta:R Tools for Data Copy-Pasta

RStudio addins and R functions that make copy-pasting vectors and tables to text painless.

Maintained by Miles McBain. Last updated 3 years ago.

addinclipboardcopypasteexceltibble

899 stars 10.32 score 290 scripts 2 dependents

geoffjentry

twitteR:R Based Twitter Client

Provides an interface to the Twitter web API.

Maintained by Jeff Gentry. Last updated 9 years ago.

254 stars 10.12 score 2.0k scripts 1 dependents

fmmattioni

downloadthis:Implement Download Buttons in 'rmarkdown'

Implement download buttons in HTML output from 'rmarkdown' without the need for 'runtime:shiny'.

Maintained by Felipe Mattioni Maturana. Last updated 6 months ago.

146 stars 9.63 score 856 scripts 1 dependents

e-sensing

sits:Satellite Image Time Series Analysis for Earth Observation Data Cubes

An end-to-end toolkit for land use and land cover classification using big Earth observation data, based on machine learning methods applied to satellite image data cubes, as described in Simoes et al (2021) <doi:10.3390/rs13132428>. Builds regular data cubes from collections in AWS, Microsoft Planetary Computer, Brazil Data Cube, Copernicus Data Space Environment (CDSE), Digital Earth Africa, Digital Earth Australia, NASA HLS using the Spatio-temporal Asset Catalog (STAC) protocol (<https://stacspec.org/>) and the 'gdalcubes' R package developed by Appel and Pebesma (2019) <doi:10.3390/data4030092>. Supports visualization methods for images and time series and smoothing filters for dealing with noisy time series. Includes functions for quality assessment of training samples using self-organized maps as presented by Santos et al (2021) <doi:10.1016/j.isprsjprs.2021.04.014>. Includes methods to reduce training samples imbalance proposed by Chawla et al (2002) <doi:10.1613/jair.953>. Provides machine learning methods including support vector machines, random forests, extreme gradient boosting, multi-layer perceptrons, temporal convolutional neural networks proposed by Pelletier et al (2019) <doi:10.3390/rs11050523>, and temporal attention encoders by Garnot and Landrieu (2020) <doi:10.48550/arXiv.2007.00586>. Supports GPU processing of deep learning models using torch <https://torch.mlverse.org/>. Performs efficient classification of big Earth observation data cubes and includes functions for post-classification smoothing based on Bayesian inference as described by Camara et al (2024) <doi:10.3390/rs16234572>, and methods for active learning and uncertainty assessment. Supports region-based time series analysis using package supercells <https://jakubnowosad.com/supercells/>. Enables best practices for estimating area and assessing accuracy of land change as recommended by Olofsson et al (2014) <doi:10.1016/j.rse.2014.02.015>. Minimum recommended requirements: 16 GB RAM and 4 CPU dual-core.

Maintained by Gilberto Camara. Last updated 2 months ago.

big-earth-datacbersearth-observationeo-datacubesgeospatialimage-time-seriesland-cover-classificationlandsatplanetary-computerr-spatialremote-sensingrspatialsatellite-image-time-seriessatellite-imagerysentinel-2stac-apistac-catalogcpp

494 stars 9.50 score 384 scripts

usdaforestservice

gdalraster:Bindings to the 'Geospatial Data Abstraction Library' Raster API

Interface to the Raster API of the 'Geospatial Data Abstraction Library' ('GDAL', <https://gdal.org>). Bindings are implemented in an exposed C++ class encapsulating a 'GDALDataset' and its raster band objects, along with several stand-alone functions. These support manual creation of uninitialized datasets, creation from existing raster as template, read/set dataset parameters, low level I/O, color tables, raster attribute tables, virtual raster (VRT), and 'gdalwarp' wrapper for reprojection and mosaicing. Includes 'GDAL' algorithms ('dem_proc()', 'polygonize()', 'rasterize()', etc.), and functions for coordinate transformation and spatial reference systems. Calling signatures resemble the native C, C++ and Python APIs provided by the 'GDAL' project. Includes raster 'calc()' to evaluate a given R expression on a layer or stack of layers, with pixel x/y available as variables in the expression; and raster 'combine()' to identify and count unique pixel combinations across multiple input layers, with optional output of the pixel-level combination IDs. Provides raster display using base 'graphics'. Bindings to a subset of the 'OGR' API are also included for managing vector data sources. Bindings to a subset of the Virtual Systems Interface ('VSI') are also included to support operations on 'GDAL' virtual file systems. These are general utility functions that abstract file system operations on URLs, cloud storage services, 'Zip'/'GZip'/'7z'/'RAR' archives, and in-memory files. 'gdalraster' may be useful in applications that need scalable, low-level I/O, or prefer a direct 'GDAL' API.

Maintained by Chris Toney. Last updated 16 hours ago.

gdalgeospatialrastervectorcpp

41 stars 9.49 score 32 scripts 3 dependents

john-d-fox

Rcmdr:R Commander

A platform-independent basic-statistics GUI (graphical user interface) for R, based on the tcltk package.

Maintained by John Fox. Last updated 5 months ago.

4 stars 9.48 score 636 scripts 38 dependents