add competing risks info in the README

Author: bblodfon

README.Rmd

@@ -25,12 +25,12 @@ Probabilistic Supervised Learning for **[mlr3](https://github.com/mlr-org/mlr3/)
 
 ## What is mlr3proba?
 
-`mlr3proba` is a machine learning toolkit for making probabilistic predictions within the **[mlr3](https://github.com/mlr-org/mlr3)** ecosystem.
-It currently supports the following tasks:
+`mlr3proba`  is a machine learning toolkit for **probabilistic supervised learning** within the **[mlr3](https://github.com/mlr-org/mlr3)** ecosystem.
 
-1. **Predictive survival analysis**: survival analysis where individual hazards and survival distributions can be queried.
-2. **Unconditional distribution estimation**: main returned output is the distribution.
-Sub-cases are density estimation and unconditional survival estimation.
+It currently supports:
+
+1. **Predictive survival analysis**: with support for right-censoring single-event and competing risks.
+2. **Unconditional distribution estimation**: including density and unconditional survival estimation.
 3. **Probabilistic supervised regression**: Supervised regression with a predictive distribution as the return type.
 
 The survival analysis part is considered in a mature state, the rest are in early stages of development.
@@ -39,7 +39,7 @@ The survival analysis part is considered in a mature state, the rest are in earl
 
 Key features of `mlr3proba` focus on survival analysis and are:
 
-- Task frameworks for survival analysis (`TaskSurv`)
+- Task frameworks for survival analysis (`TaskSurv`, `TaskCompetingRisks`)
 - A comprehensive **selection of survival learners** (mostly via
   [mlr3extralearners](https://github.com/mlr-org/mlr3extralearners/))
 - A unified `$train()`/`$predict()` model interface to any probabilistic predictive model (frequentist, Bayesian, Deep Learning, or other)
@@ -69,16 +69,17 @@ remotes::install_github("mlr-org/mlr3proba")
 
 ## Learners
 
-- [Core learners](https://mlr3proba.mlr-org.com/reference/index.html#survival-learners) are implemented in `mlr3proba` and include the Kaplan-Meier Estimator, the Cox Proportional Hazards model and the Survival Tree learner.
-- In [mlr3extralearners](https://github.com/mlr-org/mlr3extralearners) we have interfaced several advanced ML survival learners.
+- [Core learners](https://mlr3proba.mlr-org.com/reference/index.html#survival-learners) are implemented in `mlr3proba` and include the Kaplan-Meier Estimator, the Cox Proportional Hazards model, the Survival Tree learner and the Aalen-Johansen estimator for competing risks.
+- In [mlr3extralearners](https://github.com/mlr-org/mlr3extralearners) we have interfaced several more advanced ML learners.
 Use the [interactive search table](https://mlr-org.com/learners.html) to search for the available survival learners and see the [learner status page](https://mlr3extralearners.mlr-org.com/articles/learner_status.html) for their live status.
 
 ## Measures
 
 For density estimation and probabilistic regression only the **log-loss** is currently implemented.
-For survival analysis, see full list [here](https://mlr3proba.mlr-org.com/reference/index.html#survival-measures).
+For competing risk, see list [here](https://mlr3proba.mlr-org.com/reference/index.html#competing-risk-measures).
+For survival analysis, see list [here](https://mlr3proba.mlr-org.com/reference/index.html#survival-measures).
 
-Some commonly used measures are the following:
+Some commonly used measures for right-censored single-event tasks are the following:
 
 | ID | Measure | Package | Category | Prediction Type
 | :--| :------ | :------ | :------ | :------- |

README.md

@@ -18,15 +18,16 @@ Discussions](https://img.shields.io/github/discussions/mlr-org/mlr3proba?logo=gi
 
 ## What is mlr3proba?
 
-`mlr3proba` is a machine learning toolkit for making probabilistic
-predictions within the **[mlr3](https://github.com/mlr-org/mlr3)**
-ecosystem. It currently supports the following tasks:
-
-1.  **Predictive survival analysis**: survival analysis where individual
-    hazards and survival distributions can be queried.
-2.  **Unconditional distribution estimation**: main returned output is
-    the distribution. Sub-cases are density estimation and unconditional
-    survival estimation.
+`mlr3proba` is a machine learning toolkit for **probabilistic supervised
+learning** within the **[mlr3](https://github.com/mlr-org/mlr3)**
+ecosystem.
+
+It currently supports:
+
+1.  **Predictive survival analysis**: with support for right-censoring
+    single-event and competing risks.
+2.  **Unconditional distribution estimation**: including density and
+    unconditional survival estimation.
 3.  **Probabilistic supervised regression**: Supervised regression with
     a predictive distribution as the return type.
 
@@ -37,7 +38,8 @@ in early stages of development.
 
 Key features of `mlr3proba` focus on survival analysis and are:
 
-- Task frameworks for survival analysis (`TaskSurv`)
+- Task frameworks for survival analysis (`TaskSurv`,
+  `TaskCompetingRisks`)
 - A comprehensive **selection of survival learners** (mostly via
   [mlr3extralearners](https://github.com/mlr-org/mlr3extralearners/))
 - A unified `$train()`/`$predict()` model interface to any probabilistic
@@ -81,9 +83,10 @@ remotes::install_github("mlr-org/mlr3proba")
 - [Core
   learners](https://mlr3proba.mlr-org.com/reference/index.html#survival-learners)
   are implemented in `mlr3proba` and include the Kaplan-Meier Estimator,
-  the Cox Proportional Hazards model and the Survival Tree learner.
+  the Cox Proportional Hazards model, the Survival Tree learner and the
+  Aalen-Johansen estimator for competing risks.
 - In [mlr3extralearners](https://github.com/mlr-org/mlr3extralearners)
-  we have interfaced several advanced ML survival learners. Use the
+  we have interfaced several more advanced ML learners. Use the
   [interactive search table](https://mlr-org.com/learners.html) to
   search for the available survival learners and see the [learner status
   page](https://mlr3extralearners.mlr-org.com/articles/learner_status.html)
@@ -92,11 +95,13 @@ remotes::install_github("mlr-org/mlr3proba")
 ## Measures
 
 For density estimation and probabilistic regression only the
-**log-loss** is currently implemented. For survival analysis, see full
-list
+**log-loss** is currently implemented. For competing risk, see list
+[here](https://mlr3proba.mlr-org.com/reference/index.html#competing-risk-measures).
+For survival analysis, see list
 [here](https://mlr3proba.mlr-org.com/reference/index.html#survival-measures).
 
-Some commonly used measures are the following:
+Some commonly used measures for right-censored single-event tasks are
+the following:
 
 | ID | Measure | Package | Category | Prediction Type |
 |:---|:---|:---|:---|:---|