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“TreeSearch” (Smith 2023) is an R package that contains a “shiny” user interface for interactive tree search and exploration of results, including character visualization, rogue taxon detection (Smith 2022a), tree space mapping (Smith 2022b), and cluster consensus trees.

Inapplicable character states are handled using the algorithm of Brazeau, Guillerme and Smith (2019) using the “Morphy” C library (Brazeau et al. 2017). Implied weighting (Goloboff, 1993), Profile Parsimony (Faith and Trueman, 2001) and Successive Approximations (Farris, 1969) are implemented; custom optimality criteria and search approaches can also be defined.

Installing in R

Full installation instructions, including notes on installing R, are available in a vignette.

Install and load the stable version from CRAN as follows:

# Launch the Shiny App with:

Install and load the development version of “TreeSearch” with:

if(!require("curl")) install.packages("curl")
if(!require("remotes")) install.packages("remotes")

Installing stand-alone application

The TreeSearch user interface can be run as a stand-alone application without installing R. Download the latest release for your platform. If your preferred platform is not supported, please contact the maintainer.

Installation on Windows

You may need to obtain the ffmpeg library before you can run TreeSearch.

This is best installed using ‘Chocolatey’.

Once chocolatey is installed, open a PowerShell window with administrative privileges, and type choco install ffmpeg; then restart your computer.

Quick start

Launch a graphical user interface by typing TreeSearch::EasyTrees() in the R console.

For more control over search settings, see ?MaximizeParsimony().

Flow charts listing common actions facilitated by TreeSearch
Flow charts listing common actions facilitated by TreeSearch


‘TreeSearch’ uses semantic versioning. Please note that the ‘TreeSearch’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.


Brazeau M. D., Smith M. R., Guillerme T. (2017). MorphyLib: a library for phylogenetic analysis of categorical trait data with inapplicability. doi: 10.5281/zenodo.815372.

Brazeau, M. D., Guillerme, T. and Smith, M. R. (2019). An algorithm for morphological phylogenetic analysis with inapplicable data. Systematic Biology, 68(4), 619-631. doi: 10.1093/sysbio/syy083.

Faith D. P., Trueman J. W. H. (2001). Towards an inclusive philosophy for phylogenetic inference. Systematic Biology, 50(3), 331–350. doi: 10.1080/10635150118627.

Farris, J. S. (1969). A successive approximations approach to character weighting. Systematic Biology, 18(4), 374–385. doi: 10.2307/2412182.

Goloboff, P. A. (1993). Estimating character weights during tree search. Cladistics, 9(1), 83–91. doi: 10.1111/j.1096-0031.1993.tb00209.x.

Goloboff, P. A., Torres, A., Arias, J. S. (2018). Weighted parsimony outperforms other methods of phylogenetic inference under models appropriate for morphology. Cladistics, 34(4), 407–437. doi: 10.1111/cla.12205.

Smith, M. R. (2022a). Using information theory to detect rogue taxa and improve phylogenetic trees. Systematic Biology, 71(5), 1088–1094. doi: 10.1093/sysbio/syab099

Smith, M. R. (2022b). Robust analysis of phylogenetic tree space. Systematic Biology, 71(5), 1255–1270. doi: 10.1093/sysbio/syab100

Smith, M. R. (2023). TreeSearch: morphological phylogenetic analysis in R. R Journal, 14(4), 305-315. doi: 10.32614/RJ-2023-019