An open API service providing mapping between scientific papers and software projects that are mentioned in them.

Papers: 10.1155/2018/6587049

https://doi.org/10.1155/2018/6587049

Data Association Methodology to Improve Spatial Predictions in Alternative Marketing Circuits in Ecuador

Cited by: 1
Author(s): Washington R. Padilla, Juan Francisco Blanco García
Published: over 6 years ago

Software Mentions 16

cran: crs
Categorical Regression Splines
Papers that mentioned: 1
Very Likely Science (89)
cran: GADMTools
Easy Use of 'GADM' Maps
Papers that mentioned: 5
Very Likely Science (75)
cran: gdalUtils
Wrappers for the Geospatial Data Abstraction Library (GDAL) Utilities
Papers that mentioned: 2
Very Likely Science (95)
cran: geoR
Analysis of Geostatistical Data
Papers that mentioned: 102
Very Likely Science (85)
cran: ggmap
Spatial Visualization with ggplot2
Papers that mentioned: 213
Very Likely Science (85)
cran: ggplot2
Create Elegant Data Visualisations Using the Grammar of Graphics
Papers that mentioned: 11,441
Very Likely Science (100)
cran: gstat
Spatial and Spatio-Temporal Geostatistical Modelling, Prediction and Simulation
Papers that mentioned: 138
Very Likely Science (100)
cran: maps
Draw Geographical Maps
Papers that mentioned: 137
Very Likely Science (75)
cran: proj4
A simple interface to the PROJ.4 cartographic projections library
Papers that mentioned: 5
Very Likely Science (75)
cran: raster
Geographic Data Analysis and Modeling
Papers that mentioned: 561
Very Likely Science (85)
cran: readr
Read Rectangular Text Data
Papers that mentioned: 80
Very Likely Science (100)
cran: rgeos
Interface to Geometry Engine - Open Source ('GEOS')
Papers that mentioned: 144
Very Likely Science (75)
cran: tmap
Thematic Maps
Papers that mentioned: 88
Very Likely Science (95)
pypi: crs
Geographic Coordinate Reference System Encapsulation and Conversion
Papers that mentioned: 1
Very Likely Science (65)
pypi: maps
Maps: flavors of Python dictionaries
Papers that mentioned: 137
Very Likely Science (65)
pypi: tmap
A topological data analysis framework implementing the TDA Mapper algorithm for population-scale microbiome data analysis
Papers that mentioned: 88
Very Likely Science (100)