scoringutils-package: scoringutils: Utilities for Scoring and Assessing Predictions
In scoringutils: Utilities for Scoring and Assessing Predictions
scoringutils-package R Documentation
scoringutils: Utilities for Scoring and Assessing Predictions
Description
Provides a collection of metrics and proper scoring rules (Tilmann Gneiting & Adrian E Raftery (2007) doi: 10.1198/016214506000001437, Jordan, A., Krüger, F., & Lerch, S. (2019) doi: 10.18637/jss.v090.i12) within a consistent framework for evaluation, comparison and visualisation of forecasts. In addition to proper scoring rules, functions are provided to assess bias, sharpness and calibration (Sebastian Funk, Anton Camacho, Adam J. Kucharski, Rachel Lowe, Rosalind M. Eggo, W. John Edmunds (2019) doi: 10.1371/journal.pcbi.1006785) of forecasts. Several types of predictions (e.g. binary, discrete, continuous) which may come in different formats (e.g. forecasts represented by predictive samples or by quantiles of the predictive distribution) can be evaluated. Scoring metrics can be used either through a convenient data.frame format, or can be applied as individual functions in a vector / matrix format. All functionality has been implemented with a focus on performance and is robustly tested.
Provides a collection of metrics and proper scoring rules (Tilmann Gneiting & Adrian E Raftery (2007) doi: 10.1198/016214506000001437, Jordan, A., Krüger, F., & Lerch, S. (2019) doi: 10.18637/jss.v090.i12) within a consistent framework for evaluation, comparison and visualisation of forecasts. In addition to proper scoring rules, functions are provided to assess bias, sharpness and calibration (Sebastian Funk, Anton Camacho, Adam J. Kucharski, Rachel Lowe, Rosalind M. Eggo, W. John Edmunds (2019) doi: 10.1371/journal.pcbi.1006785) of forecasts. Several types of predictions (e.g. binary, discrete, continuous) which may come in different formats (e.g. forecasts represented by predictive samples or by quantiles of the predictive distribution) can be evaluated. Scoring metrics can be used either through a convenient data.frame format, or can be applied as individual functions in a vector / matrix format. All functionality has been implemented with a focus on performance and is robustly tested.
Author(s)
Maintainer: Nikos Bosse nikosbosse@gmail.com (ORCID)
Authors:
Sam Abbott contact@samabbott.co.uk (ORCID)
Hugo Gruson hugo.gruson@lshtm.ac.uk (ORCID)
Other contributors:
Johannes Bracher johannes.bracher@kit.edu (ORCID) [contributor]
Sebastian Funk sebastian.funk@lshtm.ac.uk [contributor]
See Also
Useful links:
-
-
Report bugs at https://github.com/epiforecasts/scoringutils/issues
Useful links:
scoringutils documentation built on Feb. 16, 2023, 7:30 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| scoringutils-package | R Documentation |
scoringutils: Utilities for Scoring and Assessing Predictions
Description
Provides a collection of metrics and proper scoring rules (Tilmann Gneiting & Adrian E Raftery (2007) doi: 10.1198/016214506000001437, Jordan, A., Krüger, F., & Lerch, S. (2019) doi: 10.18637/jss.v090.i12) within a consistent framework for evaluation, comparison and visualisation of forecasts. In addition to proper scoring rules, functions are provided to assess bias, sharpness and calibration (Sebastian Funk, Anton Camacho, Adam J. Kucharski, Rachel Lowe, Rosalind M. Eggo, W. John Edmunds (2019) doi: 10.1371/journal.pcbi.1006785) of forecasts. Several types of predictions (e.g. binary, discrete, continuous) which may come in different formats (e.g. forecasts represented by predictive samples or by quantiles of the predictive distribution) can be evaluated. Scoring metrics can be used either through a convenient data.frame format, or can be applied as individual functions in a vector / matrix format. All functionality has been implemented with a focus on performance and is robustly tested.
Provides a collection of metrics and proper scoring rules (Tilmann Gneiting & Adrian E Raftery (2007) doi: 10.1198/016214506000001437, Jordan, A., Krüger, F., & Lerch, S. (2019) doi: 10.18637/jss.v090.i12) within a consistent framework for evaluation, comparison and visualisation of forecasts. In addition to proper scoring rules, functions are provided to assess bias, sharpness and calibration (Sebastian Funk, Anton Camacho, Adam J. Kucharski, Rachel Lowe, Rosalind M. Eggo, W. John Edmunds (2019) doi: 10.1371/journal.pcbi.1006785) of forecasts. Several types of predictions (e.g. binary, discrete, continuous) which may come in different formats (e.g. forecasts represented by predictive samples or by quantiles of the predictive distribution) can be evaluated. Scoring metrics can be used either through a convenient data.frame format, or can be applied as individual functions in a vector / matrix format. All functionality has been implemented with a focus on performance and is robustly tested.
Author(s)
Maintainer: Nikos Bosse nikosbosse@gmail.com (ORCID)
Authors:
Sam Abbott contact@samabbott.co.uk (ORCID)
Hugo Gruson hugo.gruson@lshtm.ac.uk (ORCID)
Other contributors:
Johannes Bracher johannes.bracher@kit.edu (ORCID) [contributor]
Sebastian Funk sebastian.funk@lshtm.ac.uk [contributor]
See Also
Useful links:
Report bugs at https://github.com/epiforecasts/scoringutils/issues
Useful links:
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.