Coder Social home page Coder Social logo

caliver's Introduction

caliver

An R package for the calibration and verification of gridded models

DOI R-CMD-check Codecov test coverage

CRAN Status Badge CRAN Total Downloads CRAN Monthly Downloads

caliver is a package developed for the R programming language. The name stands for calIbration and verification of gridded models. Although caliver was initially designed for wildfire danger models such as GEFF (developed by ECMWF) and RISICO (developed by CIMA Research Foundation), the algorithms can be applied to any gridded model output. Caliver is available with an APACHE-2 license.

For more details, please see the following papers:

  • Vitolo C, Di Giuseppe F, D’Andrea M (2018) Caliver: An R package for CALIbration and VERification of forest fire gridded model outputs. PLOS ONE 13(1): e0189419. https://doi.org/10.1371/journal.pone.0189419 Please note: in the latest version of the caliver package many functionalities described in this paper have become obsolete and deprecated, please refer to the vignette "An introduction to the caliver package" for more details.

  • Vitolo C., Di Giuseppe F., Barnard C., Coughlan R., Krzeminski B., San-Miguel-Ayanz J. ERA5-based global meteorological wildfire danger maps. Sci Data 7, 216 (2020). https://doi.org/10.1038/s41597-020-0554-z

  • Vitolo C., Di Giuseppe F., Krzeminski B., San-Miguel-Ayanz J. A 1980–2018 global fire danger re-analysis dataset for the Canadian Fire Weather Indices, Sci Data 6, 190032 (2019). https://doi.org/10.1038/sdata.2019.32

Installation

The installation of the caliver package depends on the following libraries:

  • Geospatial Data Abstraction Library (GDAL)
  • NetCDF4 (netcdf4)

Make sure you have the above libraries installed before attempting to install caliver. Once all the dependencies are installed, get caliver's development version from github using devtools:

install.packages("remotes")
remotes::install_github("ecmwf/caliver")

Alternatively, the stable version of this package is available on CRAN and can be installed as shown below.

install.packages("caliver")

Load the package:

library("caliver")

Docker

In this repository you find a Dockerfile that contains all the necessary dependencies and the caliver package already installed.

docker build -t ecmwf/caliver:latest -f Dockerfile .

Alternatively, you can use the image we host on docker hub:

docker run -it --rm ecmwf/caliver:latest bash

Meta

  • This package and functions herein are part of an experimental open-source project. They are provided as is, without any guarantee.
  • Contributions are welcome! Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
  • Please report any issues or bugs.
  • License: Apache License 2.0
  • Get citation information for caliver in R doing citation(package = "caliver")

caliver's People

Contributors

cvitolo avatar mirkodandrea avatar carletes avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.