3.4.0

Version 3.4.0 uses a separate calculation for 1 - E, e.g. one minus the local extinction rates to avoid numerical abberations. These are only used in the CLA calculations.

3.3.0

Version 3.3.0 normalizes evaluation of the loglikelihood at every integration step. This strongly reduces any numerical abberations that could previously occur. These mainly occured along long branches, where values could become so close to zero that they became too close to numerical precision. If absolutely desired, this normalization can be disabled by setting ‘use_normalization’ to FALSE.

3.2.0

• Added support for integration along the root edge
• Added support for single-branch trees
• Added function to calculate the loglikelihood as calculated along a single branch
• Added support for calculating the likelihood across multiple phylogenies, assuming that all phylogenies are subject to the same diversification regime.
• Added support for performing Maximum Likelihood across multiple phylogenies
• Reduced the number of warnings displayed during LL and ML calculation.
• Changed ape::ultrametric checking to use the variance (option = 2), to reduce this falsely triggering.

3.1.2

Added the function ‘plot_idparslist’, which provides code to visualize the rates in the idparslist setup, using ggplot.

3.1.0

Version 3.1.0 fixes a bug in the simulation code that caused trait changes during speciation to not be tracked appropriately. This could, for instance, interfere with conditioning and this bug especially impacted ClaSSE-type simulations.

Minor changes

• Added explicit check of the order of states, states are no longer assumed to be numerically or alphabetically sorted.
• Added multiple additional simulation checks

3.0.1

Version 3.0.1 patches some inaccuracies in simulation functions, and deprecates expand_q_matrix, as this was making some incorrect assumptions.

Breaking changes

• The function expand_q_matrix is now deprecated, please use q_doubletrans

Minor changes

• Added conditioning of simulation of complete trees on complete tree size
• Fixed some issues with setting pool_init_states
• Added option to return a histogram of simulated tree sizes, across all successful and failed trees.

3.0.0

Version 3.0.0 extends the C++ code base used for the standard likelihood to the “cla_” likelihood, harnessing the same computation improvement.

Breaking changes

• Function name changes:
  • create_lambda_matrices() is now called create_lambda_list()
  • create_transition_matrix() is now called create_q_matrix()
  • create_mus() is now called create_mu_vector()
  • create_default_q_list() is now called create_default_shift_matrix()
  • create_default_lambda_list () is now called create_default_lambda_transition_matrix()
  • create_default_q_list() is now called create_default_shift_matrix()
• Package data files renamed:
  • phylo_Vign is now called phylo_vignette
  • traitinfo is now called traits
  • phy is now called example_phy_GeoSSE
• plot_state_exact() argument steps renamed to num_steps and argument focal_tree renamed to phy for consistency with other functions.

Major changes

• Vastly improve the computational speed of “cla_” likelihood calculation.
• Optimization of parallelization resulting in better scaling with more threads and faster run time for standard secsse and cla_secsse likelihood calculations.

Minor changes

• Added a NEWS.md file to track changes to the package.
• Documentation reworked into default_params_doc().
• Several documentation formatting improvements and linking. Documentation now follows and allows for roxygen2 markdown.
• A new vignette:
  • Using secsse with complete phylogenies (with extinction) vignette("complete_tree", package = "secsse")
• A new pkgdown website!
  • It contains all the documentation and vignettes of the package, along with additional interesting information like the Secsse versions article with details on performance and the development history of secsse.
• Revise, combine and simplify the Using SecSSE ML search and Setting up a secsse analysis into the Starting secsse vignette vignette("starting_secsse", package = "secsse").
• secsse_sim() argument conditioning now defaults to "obs_states" from "none".
• No longer Import package ‘stringr’ and Suggest package ‘testit’.
• New organisation of code in .R, .cpp and .h files. (Developer side).
• Start archiving in Zenodo, with new .zenodo.json metadata file.

Bug fixes

• secsse_sim() fix bug causing error when simulating trees with extinct species.

2.6.0

Major changes

• C++ code base for the standard likelihood, making smarter use of parallelization, this marks another 10-fold increase in speed.

Minor changes

• Add a number of helper functions: fill_in(), create_default_q_list(), create_default_transition_list(), create_mus()
• Implemented necessary changes to comply with CRAN clang16 build and solve issue with the boost odeint library uninitialized variable (see https://github.com/boostorg/odeint/issues/59 and more details at https://github.com/rsetienne/DAISIE/pull/158)
• Updated Copyright license to the Boost Software License, Version 1.0 for included C++ code (R code remains GPL>=3).

Bug fixes

• Fix memory leaks

2.5.0

Version 2.5.0 appeared in 2021 on GitHub and was published in May 2023 on CRAN. Version 2.5.0 marks the first version using C++ to perform the integration, and it used tbb (from the RcppParallel package) to perform multithreading. This marks a ten fold increase in speed over previous versions. Secondly, 2.5.0 introduces the function secsse_sim() to simulate a diversification process using the (cla) secsse framework. Lastly, in version 2.5.0 functions were added to allow visualisation of inferred rates of speciation across the tree (e.g. plot_state_exact() and secsse_loglik_eval()).

2.0.0

Version 2.0.0 appeared in June of 2019 on CRAN and extended the package with the cla framework, e.g. including state shifts during speciation / asymmetric inheritance during speciation.

1.0.0

The first version of secsse appeared in January of 2019 on CRAN. It used the package deSolve to solve all integrations, and could switch between either using a fully R based evaluation, or use FORTRAN to speed up calculations. Furthermore, using the foreach package, within-R parallelization was implemented. However, parallelization only situationally improved computation times, and generally, computation was relatively slow.