codecov added

.Rbuildignore

@@ -10,3 +10,4 @@
 ^LICENSE\.md$
 ^CRAN-SUBMISSION$
 ^cran-comments\.md$
+^codecov\.yml$

README.Rmd

@@ -17,6 +17,7 @@ knitr::opts_chunk$set(
 
 <!-- badges: start -->
 [![R-CMD-check](https://github.com/resplab/cumulcalib/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/resplab/cumulcalib/actions/workflows/R-CMD-check.yaml)
+[![Codecov test coverage](https://codecov.io/gh/resplab/cumulcalib/branch/main/graph/badge.svg)](https://app.codecov.io/gh/resplab/cumulcalib?branch=main)
 <!-- badges: end -->
 
 The goal of cumulcalib is to enable the use of the assessment of prediction model calibration using the cumulative calibration methodology. For more information, please refer to the original publication (arxiv version: https://arxiv.org/abs/2307.09713). The package also comes with a tutorial, which you can view after installing the package as

README.md

@@ -6,6 +6,8 @@
 <!-- badges: start -->
 
 [![R-CMD-check](https://github.com/resplab/cumulcalib/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/resplab/cumulcalib/actions/workflows/R-CMD-check.yaml)
+[![Codecov test
+coverage](https://codecov.io/gh/resplab/cumulcalib/branch/main/graph/badge.svg)](https://app.codecov.io/gh/resplab/cumulcalib?branch=main)
 <!-- badges: end -->
 
 The goal of cumulcalib is to enable the use of the assessment of
@@ -49,6 +51,9 @@ summary(res)
 #> Component-wise p-values: mean calibration=0.624632509005787 | Distance (bridged)=0.385979705481866
 #> Combined p-value (Fisher's method): 0.584068794836004
 #> Location of maximum drift: 812  | time value: 0.632911942275094  | predictor value: 0.28191196504736
+```
+
+``` r
 plot(res, draw_sig=F)
 ```
 

codecov.yml

@@ -0,0 +1,14 @@
+comment: false
+
+coverage:
+  status:
+    project:
+      default:
+        target: auto
+        threshold: 1%
+        informational: true
+    patch:
+      default:
+        target: auto
+        threshold: 1%
+        informational: true