Documentation overhaul

.gitignore

@@ -25,5 +25,8 @@ standalone/build/*
 /inst/shiny/DiagnosticsExplorer/rsconnect/*
 /doc/
 /Meta/
+/extras/
+/results/
+/.vscode/
 .project
 .cproject

DESCRIPTION

@@ -1,7 +1,6 @@
 Package: PatientLevelPrediction
 Type: Package
-Title: Developing patient level prediction using data in the OMOP Common Data
-    Model
+Title: Developing Patient Level Prediction Models Using the Observational Medical Outcomes Partnership Common Data Model
 Version: 6.3.9.9999
 Date: 2024-10-10
 Authors@R: c(
@@ -10,12 +9,12 @@ Authors@R: c(
     person("Martijn", "Schuemie", role = c("aut")),
     person("Marc", "Suchard", role = c("aut")),
     person("Patrick", "Ryan", role = c("aut")),
-    person("Peter", "Rijnbeek", role = c("aut"))
-    )
-Description: A user friendly way to create patient level prediction models using the OMOP common data model. Given a
+    person("Peter", "Rijnbeek", role = c("aut")),
+    person("Observational Health Data Science and Informatics", role = c("cph")))
+Description: A user friendly way to create patient level prediction models using the OMOP (ObservationalMedical Outcomes Partnership) common data model. Given a
     cohort of interest and an outcome of interest, the package can use data in the
     OMOP Common Data Model to build a large set of features. These features can then
-    be assessed to fit a predictive model using a number of machine learning algorithms.
+    be used to fit a predictive model with a number of machine learning algorithms.
     Several performance measures are implemented for model evaluation.
 License: Apache License 2.0
 URL: https://ohdsi.github.io/PatientLevelPrediction, https://github.com/OHDSI/PatientLevelPrediction

README.md

@@ -18,11 +18,11 @@ Reps JM, Schuemie MJ, Suchard MA, Ryan PB, Rijnbeek PR. [Design and implementati
 
 The figure below illustrates the prediction problem we address. Among a population at risk, we aim to predict which patients at a defined moment in time (t = 0) will experience some outcome during a time-at-risk. Prediction is done using only information about the patients in an observation window prior to that moment in time.
 
-![](vignettes/Figure1.webp)
+![](vignettes/images/Figure1.avif)
 
 To define a prediction problem we have to define t=0 by a Target Cohort (T), the outcome we like to predict by an outcome cohort (O), and the time-at-risk (TAR). Furthermore, we  have to make design choices for the model we like to develop, and determine the observational datasets to perform internal and external validation. This conceptual framework works for all type of prediction problems, for example those presented below (T=green, O=red).
 
-![](vignettes/problems.webp)
+![](vignettes/images/problems.avif)
 
 Features
 ========
@@ -51,11 +51,11 @@ Screenshots
 <tr valign="bottom">
 <td width = 50%>
 
-<img src="https://github.com/OHDSI/PatientLevelPrediction/raw/main/vignettes/sparseCalibration.webp" alt="Calibration plot" title="Calibration plot" />
+<img src="vignettes/images/sparseCalibration.avif" alt="Calibration plot" title="Calibration plot" />
 
 </td>
 <td width = 50%>
-<img src="https://github.com/OHDSI/PatientLevelPrediction/raw/main/vignettes/sparseROC.webp" alt="ROC plot" title="ROC plot" />
+<img src="vignettes/images/sparseROC.avif" alt="ROC plot" title="ROC plot" />
 
 </td>
 </tr><tr>

_pkgdown.yml

@@ -1,6 +1,8 @@
 template:
+  bootstrap: 5
   params:
     bootswatch: cosmo
+  light-switch: true
 
 development:
   mode: auto
@@ -16,10 +18,8 @@ navbar:
     left:
     - home
     - intro
-    - videos
     - reference
     - articles
-    - tutorial
     - benchmarks
     - predictors
     - bestpractice