Showing 103 of total 103 results (show query)

appsilon

shiny.react:Tools for Using React in Shiny

A toolbox for defining React component wrappers which can be used seamlessly in Shiny apps.

Maintained by Jakub Sobolewski. Last updated 11 months ago.

reactrhinoverseshiny

97 stars 10.20 score 83 scripts 4 dependents

datawookie

emayili:Send Email Messages

A light, simple tool for sending emails with minimal dependencies.

Maintained by Andrew B. Collier. Last updated 2 months ago.

hacktoberfest

180 stars 9.59 score 95 scripts 3 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

insightsengineering

teal.modules.hermes:RNA-Seq Analysis Modules to Add to a Teal Application

RNA-seq analysis teal modules based on the `hermes` package.

Maintained by Daniel Sabanés Bové. Last updated 1 years ago.

modulesrna-seq-analysisshiny

7 stars 5.54 score 32 scripts

ropensci

roreviewapi:Plumber API to report package structure and function

Plumber API to report package structure and function.

Maintained by Mark Padgham. Last updated 1 months ago.

4 stars 5.26 score

ropensci

ReLTER:An Interface for the eLTER Community

ReLTER provides access to DEIMS-SDR (https://deims.org/), and allows interaction with data and software implemented by eLTER Research Infrastructure (RI) thus improving data sharing among European LTER projects. ReLTER uses the R language to access and interact with the DEIMS-SDR archive of information shared by the Long Term Ecological Research (LTER) network. This package grew within eLTER H2020 as a major project that will help advance the development of European Long-Term Ecosystem Research Infrastructures (eLTER RI - https://elter-ri.eu). The ReLTER package functions in particular allow to: - retrieve the information about entities (e.g. sites, datasets, and activities) shared by DEIMS-SDR (see e.g. get_site_info function); - interact with the [ODSEurope](maps.opendatascience.eu) starting with the dataset shared by [DEIMS-SDR](https://deims.org/) (see e.g. [get_site_ODS](https://docs.ropensci.org/ReLTER/reference/get_site_ODS.html) function); - use the eLTER site informations to download and crop geospatial data from other platforms (see e.g. get_site_ODS function); - improve the quality of the dataset (see e.g. get_id_worms). Functions currently implemented are derived from discussions of the needs among the eLTER users community. The ReLTER package will continue to follow the progress of eLTER-RI and evolve, adding new tools and improvements as required.

Maintained by Alessandro Oggioni. Last updated 1 years ago.

biodiversity-informaticsdata-scienceecologyelterresearch-infrastructure

12 stars 3.38 score 4 scripts

nenuial

geographer:Geography Vizualisations

Provides function and objects to establish vizualisations for my Geography lessons.

Maintained by Pascal Burkhard. Last updated 1 months ago.

2 stars 3.08 score

datawookie

filebin:Wrapper for the Filebin File Sharing API

A wrapper for the Filebin API. Filebin implements convenient file sharing on the web.

Maintained by Andrew B. Collier. Last updated 2 years ago.

2 stars 3.00 score 3 scripts

mikejohnson51

climateR.catalogs:

A collection of tools for maniputatling hydrologic and hydraulic networks

Maintained by Mike Johnson. Last updated 1 years ago.

16 stars 2.90 score 1 scripts

nowosad

geostatbook:Geostatystyka w R

Materialy do skryptu Geostatystyka w R.

Maintained by Jakub Nowosad. Last updated 3 years ago.

6 stars 2.88 score 25 scripts

adafede

CentroidR:CentroidR

CentroidR provides the infrastructure to centroid profile spectra.

Maintained by Adriano Rutz. Last updated 4 days ago.

centroidingspectra

2.60 score