Combined with 'RRphylo', this package provides a powerful tool to analyse and visualise 3d models (surfaces and meshes) in a phylogenetically explicit context (Melchionna et al., 2024 <doi:10.1038/s42003-024-06710-8>).

Functions for creating and manipulating RNA secondary structure plots.

Calculate RNNI distance between and manipulate with ranked trees. RNNI stands for Ranked Nearest Neighbour Interchange and is an extension of the classical NNI space (space of trees created by the NNI moves) to ranked trees, where internal nodes are ordered according to their heights (usually assumed to be times). The RNNI distance takes the tree topology into account, as standard NNI does, but also penalizes changes in the order of internal nodes, i.e. changes in the order of times of evolutionary events. For more information about the RNNI space see: Gavryushkin et al. (2018) <doi:10.1007/s00285-017-1167-9>, Collienne & Gavryushkin (2021) <doi:10.1007/s00285-021-01567-5>, Collienne et al. (2021) <doi:10.1007/s00285-021-01685-0>, and Collienne (2021) <http://hdl.handle.net/10523/12606>.

Computation of the International Roughness Index (IRI) given a
    longitudinal road profile. The IRI can be calculated for a single road segment
    or for a sequence of segments with a fixed length (e. g. 100m). For the latter,
    an overlap of the segments can be selected. The IRI and likewise the algorithms
    for its determination are defined in Sayers, Michael W; Gillespie, Thomas D;
    Queiroz, Cesar A.V. 1986. The International Road Roughness Experiment (IRRE) :
    establishing correlation and a calibration standard for measurements. World
    Bank technical paper; no. WTP 45. Washington, DC : The World Bank. (ISBN
    0-8213-0589-1) available from <http://documents.worldbank.org/curated/en/326081468740204115>.

Multivariate regression methodologies including
    classical reduced-rank regression (RRR)
    studied by Anderson (1951) <doi:10.1214/aoms/1177729580> and
    Reinsel and Velu (1998) <doi:10.1007/978-1-4757-2853-8>,
    reduced-rank regression via adaptive nuclear norm penalization
    proposed by Chen et al. (2013) <doi:10.1093/biomet/ast036> and
    Mukherjee et al. (2015) <doi:10.1093/biomet/asx080>,
    robust reduced-rank regression (R4) proposed by
    She and Chen (2017) <doi:10.1093/biomet/asx032>,
    generalized/mixed-response reduced-rank regression (mRRR) proposed by
    Luo et al. (2018) <doi:10.1016/j.jmva.2018.04.011>,
    row-sparse reduced-rank regression (SRRR) proposed by
    Chen and Huang (2012) <doi:10.1080/01621459.2012.734178>,
    reduced-rank regression with a sparse singular value decomposition (RSSVD)
    proposed by Chen et al. (2012) <doi:10.1111/j.1467-9868.2011.01002.x>
    and sparse and orthogonal factor regression (SOFAR) proposed by
    Uematsu et al. (2019) <doi:10.1109/TIT.2019.2909889>.