Update landing page

README.Rmd

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-# _slendr_: Population Genetic Simulations in R
+# *slendr*: Population Genetic Simulations in R
 
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@@ -26,21 +26,19 @@ set.seed(314159)
 
 ### *Overview* <a href='https://www.slendr.net'></a>
 
-*slendr* is an R package toolbox for defining population genetic models and simulating genomic data entirely from R. It has been originally conceived as a framework for simulating spatially-explicit genomic data on real geographic landscapes but it has grown to be much more than that.
+*slendr* is an R package toolbox for defining population genetic models and simulating genomic data from them entirely in R. It has been originally conceived as a framework for [simulating spatially-explicit genomic data](https://www.slendr.net/articles/vignette-01-tutorial.html) on real geographic landscapes but it has grown to be much more than that: *slendr* can now simulate data from [traditional, non-spatial demographic models](https://www.slendr.net/articles/vignette-04-nonspatial-models.html) using SLiM and *msprime*, and even supports [selection](https://www.slendr.net/articles/vignette-11-extensions.html) scenarios. In addition to model definition and simulation, *slendr* also provides a set of function for efficient [analysis of tree-sequence genomic data](https://www.slendr.net/articles/vignette-05-tree-sequences.html), utilizing the [*tskit*](https://tskit.dev) module for underlying computation.
 
-This page briefly summarizes *slendr*'s most important features. A a much detailed description of the *slendr* architecture and an extensive set of practical code examples can be found in our [paper](https://peercommunityjournal.org/articles/10.24072/pcjournal.354/) in the PCI journal and on our [website](https://www.slendr.net).
+This page briefly summarizes *slendr*'s most important features. A much detailed description of the *slendr* architecture and an extensive set of practical code examples can be found in our [paper](https://peercommunityjournal.org/articles/10.24072/pcjournal.354/) in the PCI journal and on our [website](https://www.slendr.net).
 
 ------------------------------------------------------------------------
 
-### Citing _slendr_
+### Citing *slendr*
 
-The _slendr_ paper is now [published in the Peer Community Journal](https://peercommunityjournal.org/articles/10.24072/pcjournal.354/)!
+The *slendr* paper is now [published in the Peer Community Journal](https://peercommunityjournal.org/articles/10.24072/pcjournal.354/)!
 
-If you use _slendr_ in your work, please cite it as:
+If you use *slendr* in your work, please cite it as:
 
-> Petr, Martin; Haller, Benjamin C.; Ralph, Peter L.; Racimo, Fernando.
-> slendr: a framework for spatio-temporal population genomic simulations on geographic landscapes.
-> Peer Community Journal, Volume 3 (2023), article no. e121. doi : 10.24072/pcjournal.354.
+> Petr, Martin; Haller, Benjamin C.; Ralph, Peter L.; Racimo, Fernando. slendr: a framework for spatio-temporal population genomic simulations on geographic landscapes. Peer Community Journal, Volume 3 (2023), article no. e121. doi : 10.24072/pcjournal.354.
 
 Citations help me justify further development and fixing bugs! Thank you! ❤️
 
@@ -56,14 +54,16 @@ Here is a brief summary of *slendr*'s most important features. The R package all
 
 -   **Load, process, and analyse tree-sequence outputs** via *slendr*'s built-in R interface to the tree-sequence library [*tskit*](https://tskit.dev/tskit/docs/)*.* You can compute many [population genetic statistics](https://www.slendr.net/reference/index.html#tree-sequence-statistics) in R immediately after the simulation finishes [directly on the output tree sequences](https://www.slendr.net/articles/vignette-05-tree-sequences.html#calculating-f-statistics), without having to convert files to other formats (VCF, EIGENSTRAT) for analysis in different software.
 
+-   Although it originally assumed neutrality of simulated models, *slendr* now provides a simple extension mechanism for [customization of SLiM-based models using user-defined SLiM code](https://www.slendr.net/articles/vignette-11-extensions.html). Simulations of selection models of arbitrary complexity is now entirely possible.
+
+-   [**Schedule sampling events**](https://www.slendr.net/articles/vignette-05-tree-sequences.html#scheduling-of-sampling-events) which specify how many individuals' genomes, from which populations, and at which times (optionally, at which locations) should be recorded by the simulation engine (SLiM or *msprime*) in the output tree-sequence files.
+
 -   **Encode complex models of population movements on a landscape** (see a brief example of such model [here](https://www.slendr.net/articles/vignette-06-locations.html#model-specification), and a more extended explanation in [this tutorial](https://www.slendr.net/articles/vignette-01-tutorial.html)). No knowledge of cartographic or geospatial analysis concepts is needed.
 
 -   **Simulate these dynamic spatial demographic models using SLiM's continuous-space simulation capabilities directly in R** **(again, no SLiM programming required).** The outputs from such simulations are saved as tree sequences and can be analysed using [standard R geospatial data analysis libraries](https://www.slendr.net/articles/vignette-09-paper.html#example-4-figure-5). This is because *slendr* performs the conversion of tree sequence tables to the appropriate spatial R data type automatically.
 
 -   Specify within-population individual [dispersal dynamics](https://www.slendr.net/articles/vignette-03-interactions.html) from the R interface by leveraging SLiM's individual interaction parameters implemented in the SLiM back-end script.
 
--   [**Schedule sampling events**](https://www.slendr.net/articles/vignette-05-tree-sequences.html#scheduling-of-sampling-events) which specify how many individuals' genomes, from which populations, and at which times (optionally, at which locations) should be recorded by the simulation engine (SLiM or *msprime*) in the output tree-sequence files.
-
 **Utilizing the flexibility of R with its wealth of libraries for statistics, geospatial analysis and graphics, and combining it with the power of population genetic simulation frameworks SLiM and *msprime*, the *slendr* R package makes it possible to write entire simulation and analytic pipelines without the need to leave the R environment.**
 
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@@ -88,9 +88,7 @@ If you would like to test the latest features of the software (perhaps because y
 
 ⚠️⚠️⚠️
 
-**Note:** the default _slendr_ installation no longer has a hard dependency on geospatial R packages _sf_, _stars_, and _rnaturalearth_. If you indend to use _slendr_ for spatial genomic simulations and data analysis, you have to run `install.packages(c("sf", "stars", "rnaturalearth"))` first.
-
-We made this change to ease the installation burden for researchers who might only need _slendr_'s support for traditional, non-spatial simulations (which represent the majority of use-cases in population genetics in general). The geospatial R packages imply a non-negligible amount of software dependencies themselves, which has proven to be an unnecessary hurdle for many users who might not need them (at least not at first).
+**Note:** the default *slendr* installation no longer has a hard dependency on geospatial R packages *sf*, *stars*, and *rnaturalearth*. If you indend to use *slendr* for spatial genomic simulations and data analysis, you have to run `install.packages(c("sf", "stars", "rnaturalearth"))` first.
 
 ⚠️⚠️⚠️
 
@@ -275,11 +273,11 @@ explore_model(model)
 
 The function has two modes:
 
-a) Plotting (and "playing") spatial map dynamics:
+a)  Plotting (and "playing") spatial map dynamics:
 
 ![](man/figures/shiny_maps.jpg)
 
-b) Displaying the demographic history graph (splits and gene-flow events) embedded in the specified model:
+b)  Displaying the demographic history graph (splits and gene-flow events) embedded in the specified model:
 
 ![](man/figures/shiny_graph.jpg)
 
@@ -324,6 +322,8 @@ The example above provides only a very brief and incomplete overview of the full
 
 -   You can use *slendr* to program non-spatial models, which means that any conceivable traditional, random-mating demographic model can be simulated with only a few lines of R code. You can learn more in [this vignette](https://www.slendr.net/articles/vignette-04-nonspatial-models.html) (and in more detail in [this vignette](https://www.slendr.net/articles/vignette-05-tree-sequences.html)). Because SLiM simulations can be often quite slow compared to their coalescent counterparts, we also provide functionality allowing to simulate *slendr* models (without any change!) using a built-in *msprime* back end script. See [this vignette](https://www.slendr.net/articles/vignette-07-backends.html) for a tutorial on how this works.
 
+-   You can extend traditional, non-spatial SLiM-based *slendr* models with customized SLiM code and [simulate various selection scenarios](https://www.slendr.net/articles/vignette-11-extensions.html) supported by SLiM while still leveraging *slendr*'s easy-to-use interface for programming demographic models.
+
 -   You can build complex spatial models which are still abstract (not assuming any real geographic location), including traditional simulations of demes in a lattice structure. A complete example is shown [this vignette](https://www.slendr.net/articles/vignette-02-grid-model.html).
 
 -   Because *slendr* & SLiM save data in a tree-sequence file format, thanks to the R package [*reticulate*](https://rstudio.github.io/reticulate/index.html) for interfacing with Python code, we have the full power of [*tskit*](https://tskit.dev/tskit/docs/stable/) and [*pyslim*](https://tskit.dev/pyslim/docs/latest/introduction.html) for manipulating tree-sequence data right at our fingertips, all within the convenient environment of R. An extended example can be found in [this vignette](https://www.slendr.net/articles/vignette-05-tree-sequences.html).