User-friendly analysis of hierarchical multinomial processing tree (MPT) 
    models that are often used in cognitive psychology. Implements the latent-trait 
    MPT approach (Klauer, 2010) <DOI:10.1007/s11336-009-9141-0> and the beta-MPT 
    approach (Smith & Batchelder, 2010) <DOI:10.1016/j.jmp.2009.06.007> to model 
    heterogeneity of participants. MPT models are conveniently specified by an
    .eqn-file as used by other MPT software and data are provided by a .csv-file 
    or directly in R. Models are either fitted by calling JAGS or by an MPT-tailored 
    Gibbs sampler in C++ (only for nonhierarchical and beta MPT models). Provides 
    tests of heterogeneity and MPT-tailored summaries and plotting functions.
    A detailed documentation is available in Heck, Arnold, & Arnold (2018) 
    <DOI:10.3758/s13428-017-0869-7> and a tutorial on MPT modeling can be found 
    in Schmidt, Erdfelder, & Heck (2023) <DOI:10.1037/met0000561>.

Bootstrapped response and correlation functions,
    seasonal correlations and evaluation of reconstruction
    skills for use in dendroclimatology and dendroecology,
    see Zang and Biondi (2015) <doi:10.1111/ecog.01335>.

Create a measure of inter-point dissimilarity useful 
 for clustering mixed data, and, optionally, perform the clustering.

Performs sparse discriminant analysis on a combination of
    node and leaf predictors when the predictor variables are
    structured according to a tree, as described in Fukuyama et
    al. (2017) <doi:10.1371/journal.pcbi.1005706>.

An implementation that combines trait data and a phylogenetic tree (or trees) into a 
    single object of class 'treedata.table'. The resulting object can be easily 
    manipulated to simultaneously change the trait- and tree-level sampling.
    Currently implemented functions allow users to use a 'data.table' syntax when
    performing operations on the trait dataset within the 'treedata.table' object.
    For more details see Roman-Palacios et al. (2021) <doi:10.7717/peerj.12450>.