• slendr’s tree-sequence R interface to the tskit Python module has been generalized to load, process, and analyze tree sequences from non-slendr models! This means that users can use the slendr R package even for analyzing tree sequences coming from standard msprime and SLiM scripts, including all spatial capabilities that have been only available for slendr tree sequences so far. Please note that this generalization is still rather experimental and there might be corner cases where a tree sequence from your msprime or SLiM script does not load properly or leads to other errors. If this happens, please open a GitHub issue with the script in question attached. (PR #91)

  • Removed functions and some function arguments originally deprecated during the renaming phase of the pre-preprint refactoring. This affects compile, boundary, dispersal, expand, geneflow, plot.slendr, plot_graph, read, sampling, and shrink. Similarly, deprecated dir argument of the compile_model is now path, geneflow argument of compile_model is now gene_flow, and the _dist suffix was removed from competition_dist, mate_dist, and dispersal_dist. If you get an error about a missing function or a function argument in code which used to work in an ancient version of slendr, this is why. (#985b451)

  • When setting up an isolated Python environment using setup_env(), slendr now makes a decision whether to install Python dependencies using pip (critical on osx-arm64 for which the conda msprime/tskit are unfortunately currently broken) or with conda (every other platform). This can be still influenced by the user using the pip = <TRUE|FALSE> argument, but we now change the default behavior on ARM64 Mac. (#54a413d)

  • The name of the default slendr Python environment is now shortened even more, and the redundant _pandas prefix is now dropped. Users will be notified upon calling library(slendr) that a new environment should be created. This is OK, it’s not a bug. (#54a413d)

  • The format of the default slendr Python environment is now msprime-<version>_tskit-<version>_pyslim-<version>_pandas, dropping the slendr_ prefix. This paves the way towards a future non-slendr tskit R package, which will share the same Python environment with slendr (because both R packages will go hand in hand). This isn’t really a user-facing change, except that calling setup_env() will suggests creating a new Python environment and library(slendr) will appear as if a slendr environment is not yet present. Calling setup_env() and creating a new Python environment from scratch will solve the problem. (#eb05180)

  • xrange and yrange parameters of world() are now enforced to be two-dimensional numeric vectors, avoiding unnecessary issues with misspecified longitude/latitude (#df95369)

  • The automated setup_env() function for creating dedicated mini Python environments for slendr now installs packages using pip by default. Reason: The rate of conda failures and dependency conflicts (even in the trivial case of installing nothing more than msprime + tskit + pyslim + pandas) is too high to rely on it. The option to use conda for package installations with setup_env() is still there, but the users must explicitly call setup_env(pip = FALSE) to get this behavior. Note that conda is still used as a means to install Python itself! This change only affects the way how Python modules are installed into a dedicated slendr Python environment, not the installation of Python itself. (#81be1a7)

  • The name of the automatically created slendr-specific Python environment is now composed from the names and versions of Python modules installed. This makes it possible to naturally upgrade both slendr and its Python dependencies in case the tskit / msprime / pyslim folks ugprade some of those packages. In that case, if a slendr user upgrades the slendr package (and that new version requires newer versions of Python modules), slendr will simply recommend to create a new Python environment without additional effort on our part. (#81be1a7)

  • The code of setup_env() was simplified to bare essentials. Now it only serves as a way to auto-setup a dedicated, isolated Python installation and slendr environment. The interface to install Python modules into custom-defined Python environment created outside R has been removed because this functionality is not necessary – these custom environments can be easily activated by calling reticulate::use_virtualenv or reticulate::use_condaenv. (#30f24b9)

  • If some Python users want to use custom Python environments with msprime, tskit, and pyslim, they can silence the suggestion to use setup_env() printed by the library(slendr) call by setting options(slendr.custom_env = TRUE). (#30f24b9)