From c06ff2718cf91495bdb9242e55808dbe3e0620b7 Mon Sep 17 00:00:00 2001 From: nikosbosse <37978797+nikosbosse@users.noreply.github.com> Date: Thu, 31 Oct 2024 14:38:24 +0000 Subject: [PATCH] Automatic README update --- README.md | 28 +++++++++++++++------------- 1 file changed, 15 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index eaf1b9a8..9c886442 100644 --- a/README.md +++ b/README.md @@ -31,8 +31,8 @@ version](https://drive.google.com/file/d/1URaMsXmHJ1twpLpMl1sl2HW4lPuUycoj/view? of our [original](https://doi.org/10.48550/arXiv.2205.07090) `scoringutils` paper. -Another good starting point are the vignettes on [Getting -started](https://epiforecasts.io/scoringutils/articles/scoringutils.html), +Another good starting point are the vignettes + [Details on the metrics implemented](https://epiforecasts.io/scoringutils/articles/metric-details.html) and [Scoring forecasts @@ -67,17 +67,19 @@ remotes::install_github("epiforecasts/scoringutils", dependencies = TRUE) ### Forecast types -`scoringutils` currently supports scoring the following forecast -types: - `binary`: a probability for a binary (yes/no) outcome -variable. - `point`: a forecast for a continuous or discrete outcome -variable that is represented by a single number. - `quantile`: a -probabilistic forecast for a continuous or discrete outcome variable, -with the forecast distribution represented by a set of predictive -quantiles. - `sample`: a probabilistic forecast for a continuous or -discrete outcome variable, with the forecast represented by a finite set -of samples drawn from the predictive distribution. - `nominal` -categorical forecast with unordered outcome possibilities -(generalisation of binary forecasts to multiple outcomes) +`scoringutils` currently supports scoring the following forecast types: + +- `binary`: a probability for a binary (yes/no) outcome variable. +- `point`: a forecast for a continuous or discrete outcome variable that + is represented by a single number. +- `quantile`: a probabilistic forecast for a continuous or discrete + outcome variable, with the forecast distribution represented by a set + of predictive quantiles. +- `sample`: a probabilistic forecast for a continuous or discrete + outcome variable, with the forecast represented by a finite set of + samples drawn from the predictive distribution. +- `nominal` categorical forecast with unordered outcome possibilities + (generalisation of binary forecasts to multiple outcomes) ### Input formats and input validation