Fits a structural equation multidimensional scaling (SEMDS) model for asymmetric and three-way input dissimilarities. It assumes that the dissimilarities are measured with errors. The latent dissimilarities are estimated as factor scores within an SEM framework while the objects are represented in a low-dimensional space as in MDS.

Automatically calculate direct, indirect, and total effects for 
    piecewise structural equation models, comprising lists of fitted models 
    representing structured equations (Lefcheck, 2016 <doi:10/f8s8rb>). 
    Confidence intervals are provided via bootstrapping.

Provides standardized effect decomposition (direct, indirect, and total effects) for three major structural equation modeling frameworks: 
	'lavaan', 'piecewiseSEM', and 'plspm'. Automatically handles zero-effect variables, generates publication-ready 'ggplot2' visualizations, and returns 
	both wide-format and long-format effect tables. Supports effect filtering, multi-model object inputs, and customizable visualization parameters.
	For a general overview of the methods used in this package, see Rosseel (2012) <doi:10.18637/jss.v048.i02> and Lefcheck (2016) <doi:10.1111/2041-210X.12512>.

Sensitivity analysis in structural equation modeling using
    influence measures and diagnostic plots. Support leave-one-out casewise
    sensitivity analysis presented by Pek and MacCallum (2011)
    <doi:10.1080/00273171.2011.561068> and approximate casewise influence
    using scores and casewise likelihood.

A framework for extracting semantic motifs around entities in textual data. It implements an entity-centered semantic grammar that distinguishes six classes of motifs: actions of an entity, treatments of an entity, agents acting upon an entity, patients acted upon by an entity, characterizations of an entity, and possessions of an entity. Motifs are identified by applying a set of extraction rules to a parsed text object that includes part-of-speech tags and dependency annotations - such as those generated by 'spacyr'. For further reference, see: Stuhler (2022) <doi: 10.1177/00491241221099551>.