Introduction | Disclaimers and caveats | slendr / SLiM "API" | Referring to populations: population() Eidos function | Referring to times: tick() Eidos function | Referring to model times: model_time() Eidos function | Logging: write_log() Eidos function | Saving and re-starting simulation state: save_state() and reset_state() | Global constants | Practical examples | Putting it all together: running a customized slendr simulation | Putting it all together: parametrizing a customized slendr simulation | Programming slendr/SLiM extension snippets directly in R scripts | Customizing genomic architecture (selective sweep simulations)

Quick installation | Software dependencies | sf, stars, rnaturalearth | macOS | Linux | SLiM | Adding SLiM to the $PATH | Fallback options | Python | Failing conda? | Information for Python experts | ModuleNotFoundError: No module named 'tskit' error

Motivation | Geospatial data | Installation and setup | Defining the overall world map | Plotting geographic features and population ranges | Defining smaller geographic regions | Defining spatial population boundaries | Polygon population ranges | Circular population ranges | Population movement across a landscape | Spatial population expansion | Plotting multiple slendr objects | Defining gene flow events | Compile the whole model and load it in SLiM | Visualize the entire history of splits and gene flow | Interactive exploration of spatio-temporal models | Running the simulation | More information

Introduction | Model specification | Scheduling sampling events and simulation | Extracting spatial tree sequence information | Using the simple features interface | Plotting locations of simulated sampled individuals | Extracting spatio-temporal ancestral relationships | Calculating distances and other statistics using the sf package

SLiM tree sequences | msprime tree sequences | Spatial SLiM tree sequences | Conclusion

Extracting parameters from a model or tree sequences | Named samples

Setting up Python environment | Model of Neanderthal introgression into Eurasians | Scheduling of sampling events | Named samples | R interface for tskit and pyslim | Loading and processing tree-sequence output files | Visualisation of trees and tree-sequences | Accessing tskit functionality directly | Calculating f-statistics | Estimating Neanderthal ancestry proportions | ADMIXTOOLS analyses | VCF output | Other statistics | $F_{st}$ | Tajima's $D$ | Diversity | Divergence | More information

Simple two-dimensional grid model | Population grid on a real geographic landscape | More customized spatial model

Demographic model | Tree sequence simulation | Extracting ancestry tracts | Summaries of ancestral proportions | "Chromosome painting" of ancestry tracts | Average tract lengths: | Distribution of ancestry tract lengths | Pure msprime tree sequence

Detecting gene flow from msprime and SLiM tree sequences | Defining a model | Compiling the model and simulating data | Comparing the outputs of msprime and SLiM runs of a slendr model | Comparing results of population genetics statistics | Computing allele frequency spectra | Model definition | Conclusion

Introduction | Installation | A note about EIGENSTRAT format | ind file | snp file (first 3 lines) | geno file (first 3 lines) | Philosophy of admixr | Internal representation of EIGENSTRAT data | D statistic | f4 statistic | f4-ratio statistic | f3 statistic | qpWave and qpAdm | qpWave | qpAdm | Grouping samples | Counting present/missing SNPs | Data filtering | Filtering based on a BED file | Filtering out potential ancient DNA damage SNPs | Merging EIGENSTRAT datasets | Examining log information

Introduction | qpAdm "rotation" | Implementation in admixr | Concrete example | Performing exhaustive search by rotating sources/outgroups | Examining and filtering fitted models | Conclusions | Final remarks