slimr

slimr integrates SLiM 3.0 with R to run population genomic simulations informed by empirical genetic and ecological data.

Key Features:

• Seamless Integration: Connects SLiM 3.0 with the R environment to execute simulations and exchange data between SLiM and R.
• Data Handling: Supports reading and cleaning empirical datasets in R and constructing empirically based parameters and initial conditions for simulations.
• Simulation Capabilities: Leverages SLiM 3.0 to simulate complex evolutionary processes across space and time.
• Result Retrieval and Analysis: Retrieves SLiM output in formats suitable for comparison with empirical data and enables analysis and visualization using R.
• Pipeline Construction: Enables building integrated workflows that combine data preparation, simulation execution, and result analysis within R.

Scientific Applications:

• Evolutionary and Ecological Inference: Integrates genetic and ecological data with simulations to study interactions between ecological and evolutionary processes.
• Scenario Exploration: Enables exploration of scenarios that are difficult or impossible to observe directly in nature by running empirically informed simulations.
• Landscape Population Genomics: Applied in landscape population genomics studies, including analyses such as those on desert mammals.

Methodology:

Reading and cleaning empirical data within R; using empirical data to construct simulation parameters and initial conditions; running simulations via SLiM 3.0 from R; employing R's analysis and visualization tools to compare simulation outputs with empirical datasets.