'Quartet' is an R package that calculates the quartet distance between two trees (Estabrook et al. 1985), a measure of their similarity based on the number of shared four-taxon subtrees.
The quartet distance outperforms a number of widely used tree distances
(e.g. the Robinson–Foulds, path, and rearrangement distances) against a number
theoretical and practical measures (Steel & Penny 1993; Smith 2020),
and is particularly valuable in the construction of tree spaces (Smith 2021).
It can also be used to produce consensus trees that display more resolution than
standard Robinson–Foulds-based majority-rule trees (via QuartetConsensus())
(Takazawa et al. 2026).
'Quartet' uses the 'tqDist' algorithm (Brodal et al. 2004; Sand et al. 2014)
to compute quartet distances, and using the CPDT algorithm of Jansson & Rajaby
(2017) to compute triplet distances (via TripletDistance()).
Unlike many other implementations, it distinguishes between quartets that are
contradicted by one tree,
and quartets that are simply absent due to a lack of resolution (i.e.
the presence of polytomies; see Smith 2019).
'Quartet' makes this distinction in both the quartet metric (function
QuartetStatus()) and the partition metric (i.e. Robinson-Foulds distance;
function SplitStatus()).
Install and load the library from CRAN as follows:
install.packages('Quartet')
library('Quartet')
For the latest features, install the development version:
if(!require("curl")) install.packages("curl")
if(!require("remotes")) install.packages("remotes")
remotes::install_github("ms609/Quartet")You will need Rtools installed in order to build the development version from source.
View the function reference and basic usage instructions.
Quartet supports trees with up to 477 leaves. Larger trees contain more
quartets than can be represented by R's signed 32-bit integers.
The underlying 'tqDist' library may handle trees with up to 568 leaves,
and 64-bit integer representations could increase this number further.
Making either of these improvements within the R package
would require substantial additional work, but could be implemented -- do
file an issue if this would be
useful to you.
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Brodal G.S., Fagerberg R., Pedersen C.N.S. 2004. Computing the quartet distance between evolutionary trees in time O(n log n). Algorithmica 38:377–395.
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Estabrook G.F., McMorris F.R., Meacham C.A. 1985. Comparison of undirected phylogenetic trees based on subtrees of four evolutionary units. Syst. Zool. 34:193–200.
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Jansson J., Rajaby R. 2017. A more practical algorithm for the rooted triplet distance. J. Comput. Biol. 24:106–126. https://doi.org/10.1089/cmb.2016.0185
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Sand A., Holt M.K., Johansen J., Brodal G.S., Mailund T., Pedersen C.N.S. 2014. tqDist: a library for computing the quartet and triplet distances between binary or general trees. Bioinformatics 30:2079–2080. https://doi.org/10.1093/bioinformatics/btu157
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Smith, M.R. 2019. Bayesian and parsimony approaches reconstruct informative trees from simulated morphological datasets. Biol. Lett. 15:20180632. https://doi.org/10.1098/rsbl.2018.0632
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Smith, M.R. 2020. Information theoretic generalized Robinson–Foulds metrics for comparing phylogenetic trees. Bioinformatics, 36:5007–5013. https://dx.doi.org/10.1093/bioinformatics/btaa614
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Smith, M.R. 2022. Robust analysis of phylogenetic tree space. Systematic Biology, syab100. https://dx.doi.org/10.1093/sysbio/syab100
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Steel, M. and Penny, D. 1993. Distributions of tree comparison metrics: some new results. Syst. Biol. 42: 126-141. https://doi.org/10.1093/sysbio/42.2.126
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Takazawa Y., Takeda A., Hayamizu M., Gascuel O. 2026. Outperforming the majority-rule consensus tree using fine-grained dissimilarity measures. bioRxiv. https://doi.org/10.64898/2026.03.16.712085
Please note that the 'Quartet' project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.