Visualizing cuts for either axis-align or non axis-align tree methods (e.g. decision tree, random tessellation process).

Trajectory Presence and Heterogeneity in Multivariate Data
c(person("Lovemore", "Tenha", role = "aut",
comment = c(ORCID = "0000-0001-9705-2023"),
email = "ltenha@nmsu.edu"),
person("Joe", "Song", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-6883-6547"),
email = "joemsong@cs.nmsu.edu"))
Testing on multivariate data for trajectory presence
and heterogeneity. Two statistical methods (Tenha & Song,
provisionally accepted) are implemented. The tree dimension test
quantifies the statistical evidence for trajectory presence. The
subset specificity measure summarizes pattern heterogeneity using the
minimum subtree cover. There is no user tunable parameters for either
method. Examples are included to illustrate how to use the methods on
single-cell data for studying gene and pathway expression dynamics and
pathway expression specificity.
fitdistrplus, igraph, nFactors, Rcpp (>= 1.0.2), RColorBrewer,
Rdpack

c(
person("Lovemore", "Tenha", , "ltenha@nmsu.edu", role = "aut",
comment = c(ORCID = "0000-0001-9705-2023")),
person("Joe", "Song", , "joemsong@cs.nmsu.edu", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-6883-6547"))
)
Testing for trajectory presence and heterogeneity on
multivariate data. Two statistical methods (Tenha & Song 2022)
<doi:10.1371/journal.pcbi.1009829> are implemented. The tree dimension
test quantifies the statistical evidence for trajectory presence. The
subset specificity measure summarizes pattern heterogeneity using the
minimum subtree cover. There is no user tunable parameters for either
method. Examples are included to illustrate how to use the methods on
single-cell data for studying gene and pathway expression dynamics and
pathway expression specificity.

c(person("Martin R.", 'Smith',
email="martin.smith@durham.ac.uk",
role=c("aut", "cre", "cph", "prg"),
comment=c(ORCID = "0000-0001-5660-1727")),
person("Roy", "Jonker", email="roy_jonker@magiclogic.com",
role=c("prg", "cph")),
person("Yong", "Yang", email="yongyanglink@gmail.com",
role=c("ctb", "cph")),
person("Yi", "Cao", role=c("ctb", "cph"))
)
Implements measures of tree similarity, including
information-based generalized Robinson-Foulds distances
(Phylogenetic Information Distance, Clustering Information Distance,
Matching Split Information Distance; Smith, 2020)
<doi:10.1093/bioinformatics/btaa614>;
Jaccard-Robinson-Foulds distances (Bocker et al. 2013)
<doi:10.1007/978-3-642-40453-5_13>,
including the Nye et al. (2006) metric <doi:10.1093/bioinformatics/bti720>;
the Matching Split Distance (Bogdanowicz & Giaro 2012)
<doi:10.1109/TCBB.2011.48>;
Maximum Agreement Subtree distances;
the Kendall-Colijn (2016) distance <doi:10.1093/molbev/msw124>, and the
Nearest Neighbour Interchange (NNI) distance, approximated per Li et al.
(1996) <doi:10.1007/3-540-61332-3_168>.
Calculates the median of a set of trees under any distance metric.
Incorporates Jonker-Volgenant Linear Assignment Problem
implementation by Roy Jonker, modified by Yong Yang after Yi
Cao.
ape (>= 5.0), colorspace, memoise, phangorn (>= 2.2.1),
TreeTools (>= 1.0.0)
bookdown, cluster, kdensity, knitr, MASS, Quartet, rmarkdown,
Rcpp, Rdpack, testthat, Ternary (>= 1.1.2), TreeDistData (>
0.1.0), TreeSearch, vdiffr
Martin R. Smith [aut, cre, cph, prg]
(<https://orcid.org/0000-0001-5660-1727>),
Roy Jonker [prg, cph],
Yong Yang [ctb, cph],
Yi Cao [ctb, cph]