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What is SecSSE?

SecSSE is an R package designed for multistate data sets under a concealed state and speciation (hisse) framework. In this sense, it is parallel to the ‘MuSSE’ functionality implemented in diversitree, but it accounts for finding possible spurious relationships between traits and diversification rates (“false positives”, Rabosky & Goldberg 2015) by testing against a “hidden trait” (Beaulieu et al. 2013), which is responsible for more variation in diversification rates than the trait being investigated.

Installing secsse

The secsse package has a stable version on CRAN and a development version on GitHub.

From CRAN

From within R, do:

From GitHub

Install secsse from this GitHub repository by running:

install.packages("remotes")
remotes::install_github("rsetienne/secsse")

Using secsse as a package dependency

From CRAN

To your DESCRIPTION file, add secsse as any normal package.

If your package directly uses secsse:

Imports:
  secsse

If your package uses secsse in its peripherals (e.g. vignettes and tests):

Suggests:
  secsse

From GitHub

Remotes:
  rsetienne/secsse

Cite

If you use secsse in your publications, please cite:

  • Herrera-Alsina, Leonel et al. “Detecting the Dependence of Diversification on Multiple Traits from Phylogenetic Trees and Trait Data.” Systematic biology vol. 68,2 (2019): 317-328. https://doi.org/10.1093/sysbio/syy057

References

  • Beaulieu, Jeremy M et al. “Identifying hidden rate changes in the evolution of a binary morphological character: the evolution of plant habit in campanulid angiosperms.” Systematic biology vol. 62,5 (2013): 725-37. https://doi.org/.1093/sysbio/syt034

  • Beaulieu, Jeremy M, and Brian C O’Meara. “Detecting Hidden Diversification Shifts in Models of Trait-Dependent Speciation and Extinction.” Systematic biology vol. 65,4 (2016): 583-601. https://doi.org/10.1093/sysbio/syw022

  • Rabosky, Daniel L., and Emma E. Goldberg. “Model inadequacy and mistaken inferences of trait-dependent speciation.” Systematic biology 64.2 (2015): 340-355. https://doi.org/10.1093/sysbio/syu131