slendr 0.5.0

• Minor breaking change! Python environments of slendr are no longer automatically activated upon calling library(slendr)! Using the coalescent msprime back end and slendr's tree-sequence functions now requires making an explicit call to a new function init_env() after library(slendr) is executed. (PR #102)

Motivation for the change: A small proportion of users have been experiencing issues with broken conda environments and various other issues with Python virtual environments in general. It's hard to guess how frequent this has been, but experience from workshops and courses suggests perhaps 1 in 20 of users experiencing Python issues which hindered their ability to use slendr .(Fun fact: the first user-submitted GitHub issue upon releasing the first version of the slendr R package was... a Python virtual environment issue).

Explanation: Activating Python environments automatically upon calling library(slendr) has been a popular feature because it hid away most of the complexities of the R-Python interface that powers slendr's tree-sequence functionality. This was particularly convenient for many slendr users, particularly those who have no experience with Python at all.

Unfortunately, in cases where a Python virtual environments with tskit/msprime/pyslim on a user's system ended up corrupted (or if anything else at the Python level got broken), the automatic Python environment activation performed by the library(slendr) call failed and slendr was not even loaded. Sadly, this completely pulled the rug from under slendr and there was nothing that could be done about it from its perspective (the issue happened at a low-level layer of embedded-Python before slendr could've been loaded into R). Solving these issues was not difficult for experienced users, but many slendr users have no experience with Python at all, they have never used conda, they don't understand the concept of "Python virtual environments" or how the R-Python interface works. And nor should they! After all, slendr is an R package.

Splitting the Python virtual environment activation step into its own init_env() function means that library(slendr) now always succeeds (regardless of potential underlying Python issues on a user's sytem), making it much easier to diagnose and fix Python problems from R once the package is loaded.

So, to recap: library(slendr) no longer activates slendr's isolated Python virtual environment. In order to simulate tree sequences and analyse them using its interface to tskit, it is necessary to call init_env(). This function performs the same Python-activation steps that library(slendr) used to call automagically in earlier slendr versions. No other change to your scripts is necessary.

• Related to the previous point: slendr now requires Python 3.11, msprime 1.2.0, tskit 0.5.4, and pyslim 1.0.1, to keep up with recent releases of its Python dependencies. Again, this presents no hassle to the user, and the only thing required is re-running setup_env(). (PR #112).

• When a named list is provided as a sample_sets = argument to a oneway statistic function, the names are used in a set column of the resulting data frame even if only single samples were used. (#2a6781)

• It is now possible to have non-spatial populations in an otherwise spatial model. Of course, when plotting such models on a map, only spatial components of the model will be plotted and slendr will give a warning. To be absolutely sure that users intends to do this, slendr will also give a warning when running compile_model() on models like this. Please consider this option experimental for the time-being as it is hard to predict which edge cases might break because of this (all unit tests and documentation tests are passing though). Feedback is more than welcome. (PR #112).

• It is now possible to label groups of samples in slendr's tskit interface functions which should make data frames with statistics results more readable. As an example, running ts_f3(ts, A = c("p1_1", "p1_2", "p1_3"), B = c("p2_1", "p2_3"), C = c("p3_1", "p3_2", "p3_")) resulted in a following data-frame output:

> ts_f3(ts, A = c("p1_1", "p1_2", "p1_3", "p1_4", "p1_5"),
            B = c("p2_1", "p2_2", "p2_3"),
            C = c("p3_1", "p3_2", "p3_3", "p3_4"))

# A tibble: 1 × 4
  A                        B              C                         f3
  <chr>                    <chr>          <chr>                  <dbl>
1 p1_1+p1_2+p1_3+p1_4+p1_5 p2_1+p2_2+p2_3 p3_1+p3_2+p3_3+p3_4 0.000130

This gets unwieldy rather quickly, especially when dozens or hundreds of samples are grouped together as populations. The new syntax allows the following shortcut via customised group names leveraging the standard named list functionality in R:

> ts_f3(ts, A = list(group_one = c("p1_1", "p1_2", "p1_3", "p1_4", "p1_5")),
            B = list(group_two = c("p2_1", "p2_2", "p2_3")),
            C = list(group_three = c("p3_1", "p3_2", "p3_3", "p3_4")))
# A tibble: 1 × 4
  A         B         C                 f3
  <chr>     <chr>     <chr>          <dbl>
1 group_one group_two group_three 0.000130

This is more readable and in line with some other tskit-interface functions of slendr which used this functionality via their sample_sets = argument (ts_divergence(), ts_diversity(), etc.). (#ac5e484)

• The default state of the parent = argument of population() is now NULL instead of "ancestor". This prevents silly surprising clashes in situation where some population's name really is "ancestor". The only change internally is that for populations which are ancestral, the splits data frame element of a slendr model object which includes this population carries a formal "ancestral parent population" as "__pop_is_ancestor" instead of just "ancestor". Note that this is an internal implementation detail and not something that particularly has to involve the user. Still, if you have been somehow using slendr's internal data structures, keep this in mind. (#f8a39a2)

slendr 0.4.0

• The msprime() function now makes sure that a given slendr model can fully coalesce to a single common ancestor population. Previously, having multiple ancestral populations created with parent = "ancestor" would cause an infinite simulation when plugged into the msprime() backend. (#095b124)

• The initial size of a population which emerges from a split from another population is now printed in a population history summary in the R console. (#6525bf3)

• A couple of fixes to support loading, processing, and plotting of "manually" created tree sequences have been implemented (see this). Not sure how practically useful, but it's important to be able to load even "pure" tree sequences which are not from simulators such as SLiM and msprime. A set of unit tests has been added, making sure that a minimalist nodes & edges table can be loaded, as well as nodes & edges & individuals, plus tables of populations and sites & mutations. PRs with more extensive unit tests and bug reports of tree sequences which are failing to load would be appreciated! The code for handling cases of "manually-created" tree sequences which have missing individual table, missing populations table, etc. seems especially brittle at the moment (#79adf14).

• The -1 value as a missing value indicator used in tskit is now replaced with the more R-like NA in various tree-sequence tables (annotated by slendr or original through tskit itself) (#79adf14).

• Relative paths are now expanded in ts_save() (#382e0b7).

• slendr models can now be optionally compiled without serialization to disk. This only works with the msprime() coalescent back end but will be much faster in cases where a huge number of simulations needs to be run because for non-serialized models, msprime() now calls the back end engine directly through the R-Python interface (rather than on the command line) and output tree sequences are not saved to disk, rather than passed through the Python-R interface directly in memory (PR #112).

• Deprecated argument sampling = of the functions slim() and msprime() has now been permanently removed in favour of the samples = argument (#0757b6e).

• One-way and multi-way statistics results are now returned as simple numerical vectors. Previously, results were returned as a type array despite "looking" as vectors (this is how values are returned to R from the reticulate-Python layer), which caused unnecessary annoyances and type-conversions on the R side of things and was not even intended (#403df3b).

• Computing population genetic statistics on named samples that are not present in a tree sequence (most likely typos) is now correctly caught and reported as an error (#da7e0bb).

slendr 0.3.0

• SLiM 4.0 is now required for running simulations with the slim() engine. If you want to run slendr simulations with SLiM (spatial or non-spatial), you will need to upgrade you SLiM installation. SLiM 3.7.1 version is no longer supported as the upcoming new slendr spatial features will depend on SLiM 4.x and maintaining two functionally identical yet syntactically different back ends is not feasible (PR #104).

• At the same time as the SLiM 4.0 release, new versions of Python modules msprime, tskit and pyslim have also been released. In fact, to be able to work with SLiM 4.0 tree sequences properly, those Python modules must be upgraded as well. Next time you load library(slendr), you will be prompted to setup a new updated Python environment which you can do easily by running setup_env().

• Experimental support for running coalescent msprime simulations and analysing tree-sequence data using tskit on the Windows platform has now been implemented (PR #102).

slendr 0.2.0

• slendr is now on CRAN!

• Big changes to the way tree-sequence outputs are handled by slendr by default. See this comment for an extended description and examples of the change. (PR #100). Briefly, simulation functions slim() and msprime() now return a tree-sequence object by default (can be switched off by setting load = FALSE), avoiding the need to always run ts <- ts_load(model) as previously. At the same time, a parameter output = can be now used in slim() and msprime() to specify the location where a tree-sequence file should be saved (temporary file by default).

• 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 argument sampling = in slim() and msprime() is now renamed to samples = (#adf4e0d).

• 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 upgrade 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)

• The argument sim_length = is now renamed to simulation_length = . Both are accepted for the moment and using the old name will simply inform the user of the future deprecation. (#56491fb)

• Extensive set of runnable examples including figures and a built-in pre-compiled example model have been added to the documentation. (#395df62c)

slendr 0.1.0

• First numbered version of slendr to celebrate its bioRxiv preprint. 🥳 🎉