add book to main page

mlr-org/_quarto.yml

@@ -32,6 +32,7 @@ website:
       - text: "Overview"
         menu:
            - ecosystem.qmd
+           - dependencies.qmd
            - text: "---"
            - tasks.qmd
            - learners.qmd

mlr-org/dependencies.qmd

@@ -0,0 +1,46 @@
+---
+title: Dependencies
+sidebar: false
+toc: false
+---
+
+`mlr3` tries to be light on dependencies.
+The following packages are at runtime:
+
+*   [`parallelly`](https://cran.r-project.org/package=parallelly):
+    Helper functions for parallelization. No extra recursive
+    dependencies.
+*   [`future.apply`](https://cran.r-project.org/package=future.apply):
+    Resampling and benchmarking is parallelized with the
+    [`future`](https://cran.r-project.org/package=future)
+    abstraction interfacing many parallel backends.
+*   [`backports`](https://cran.r-project.org/package=backports):
+    Ensures backward compatibility with older R releases. Developed
+    by members of the `mlr` team. No recursive dependencies.
+*   [`checkmate`](https://cran.r-project.org/package=checkmate):
+    Fast argument checks. Developed by members of the `mlr` team. No
+    extra recursive dependencies.
+*   [`mlr3misc`](https://cran.r-project.org/package=mlr3misc):
+    Miscellaneous functions used in multiple mlr3 [extension
+    packages](https://mlr-org.com/ecosystem.html). Developed by the
+    `mlr` team.
+*   [`paradox`](https://cran.r-project.org/package=paradox):
+    Descriptions for parameters and parameter sets. Developed by the
+    `mlr` team. No extra recursive dependencies.
+*   [`R6`](https://cran.r-project.org/package=R6): Reference class
+    objects. No recursive dependencies.
+*   [`data.table`](https://cran.r-project.org/package=data.table):
+    Extension of R’s `data.frame`. No recursive dependencies.
+*   [`digest`](https://cran.r-project.org/package=digest) (via
+    `mlr3misc`): Hash digests. No recursive dependencies.
+*   [`uuid`](https://cran.r-project.org/package=uuid): Create unique
+    string identifiers. No recursive dependencies.
+*   [`lgr`](https://cran.r-project.org/package=lgr): Logging
+    facility. No extra recursive dependencies.
+*   [`mlr3measures`](https://cran.r-project.org/package=mlr3measures):
+    Performance measures. No extra recursive dependencies.
+*   [`mlbench`](https://cran.r-project.org/package=mlbench): A
+    collection of machine learning data sets. No dependencies.
+*   [`palmerpenguins`](https://cran.r-project.org/package=palmerpenguins):
+    A classification data set about penguins, used on examples and
+    provided as a toy task. No dependencies.

mlr-org/ecosystem.qmd

@@ -18,7 +18,7 @@ The dot next to the package name indicates the [lifecycle stage](https://lifecyc
 - Experimental packages are marked with an orange dot <i class="bi-circle-fill status-experimental" title = "[status-experimental]"></i>.
 - Planned packages are marked with a red dot <i class="bi-circle-fill status-planned" title = "[status-planned]"></i>.
 
-![Graph of Extension Packages](https://raw.githubusercontent.com/mlr-org/mlr3/master/man/figures/mlr3verse.svg?sanitize=true)
+![Graph of Extension Packages](images/mlr3_ecosystem.png)
 
 If you use our packages in your research, please cite our articles on mlr3 [@mlr3], mlr3proba [@mlr3proba] or mlr3pipelines [@mlr3pipelines].
 To get the citation information of other packages, call

mlr-org/index.qmd

@@ -9,6 +9,8 @@ page-layout: article
 <div class="center-text">
 # mlr3: Machine Learning in R
 </div>
+The mlr3 ecosystem is the framework for machine learning in R.
+
 An open-source collection of R packages providing a unified interface for machine learning in the R language.
 Successor of [mlr](https://mlr.mlr-org.com/).
 
@@ -18,10 +20,10 @@ Successor of [mlr](https://mlr.mlr-org.com/).
 
 content = rowwise_table(
     ~icon,          ~text,                                                    ~link,
-    "code-square",  "A scientifically designed and easy to learn interface.", "index.html",
+    "code-square",  "A scientifically designed and easy to learn interface.", "https://mlr3book.mlr-org.com/",
     "pen",          "More than 100 connected machine learning algorithms.",   "learners.html",
-    "box",          "Light on dependencies.",                                 "index.html",
-    "stack",        "Convenient parallelization with the future package.",    "https://futureverse.org/",
+    "box",          "Light on dependencies.",                                 "dependencies.html",
+    "stack",        "Convenient parallelization with the future package.",    "https://mlr3book.mlr-org.com/chapters/chapter10/advanced_technical_aspects_of_mlr3.html#sec-parallelization",
     "sliders",      "State-of-the-art optimization algorithms.",              "tuners.html",
     "share",        "Dataflow programming with pipelines.",                   "pipeops.html"
 )
@@ -41,7 +43,8 @@ div(class = "feature-grid",
 
 ## Get Started
 
-Install the full mlr3 universe at once.
+There are many packages in the mlr3 [ecosystem](ecosystem.html) that you may want to use.
+You can install the full mlr3 universe at once with:
 
 ```{r}
 #| eval: false
@@ -50,7 +53,21 @@ install.packages("mlr3verse")
 ```
 
 You can also use our [Docker images](https://hub.docker.com/u/mlrorgdocker).
-Read our [book](book.html) or start with a [gallery post](gallery.html).
+
+## Resources
+
+<img align="right" src="images/book.jpeg" width="30%">
+
+Our [book](https://mlr3book.mlr-org.com/) “Applied Machine Learning Using mlr3 in R” is the central entry point to mlr3 ecosystem.
+This essential guide covers key aspects of machine learning, from building and evaluating predictive models to advanced techniques like hyperparameter tuning for peak performance.
+It delves into constructing comprehensive machine learning pipelines, encompassing data pre-processing, modeling, and prediction aggregation.
+
+The book is primarily aimed at researchers, practitioners, and graduate students who use machine learning or who are interested in using it. It can be used as a textbook for an introductory or advanced machine learning class that uses R, as a reference for people who work with machine learning methods, and in industry for exploratory experiments in machine learning.
+
+In addition to the book, there are many other resources to learn more about mlr3.
+The [gallery](gallery.html) contains a collection of case studies that demonstrate the functionality of mlr3.
+The [cheatsheets](resources.html#cheat-sheets) provide a quick overview of the most important functions.
+The [resources](resources.html) section contains links to talks, courses, and other material.
 
 ## Examples