Implements named semaphores from the 'boost' 'C++' library
    <https://www.boost.org/> for interprocess communication. Multiple 'R' 
    sessions on the same host can block (with optional timeout) on a semaphore
    until it becomes positive, then atomically decrement it and unblock. Any
    session can increment the semaphore.

A collection of helper functions for forming
  bootstrapping confidence intervals and examining bootstrap
  estimates in structural equation modelling,
  introduced in Yang and Cheung (2026) <doi: 10.3758/s13428-025-02911-z>
  The function currently
  support models fitted by the 'lavaan' package by
  Rosseel (2012) <doi:10.18637/jss.v048.i02>.

Training and validation of a custom (or data-driven) Structural
    Equation Models using Deep Neural Networks or Machine Learning algorithms, which
	extend the fitting procedures of the 'SEMgraph' R package <doi:10.32614/CRAN.package.SEMgraph>.

Interactive 'shiny' application for working with Structural Equation Modelling technique. Runtime examples are provided in the package function as well as at  <https://kartikeyab.shinyapps.io/semwebappk/> .

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>.