This wrapper houses PathLit API endpoints for R. The usage of
    these endpoints require the use of an API key which can be obtained at
    <https://www.pathlit.io/docs/cli/>.

Functions for computing fit indices for 
    evaluating the path component of latent variable structural equation models. 
    Available fit indices include RMSEA-P and NSCI-P originally presented and evaluated 
    by Williams and O'Boyle (2011) <doi:10.1177/1094428110391472> and demonstrated by 
    O'Boyle and Williams (2011) <doi:10.1037/a0020539> and Williams, O'Boyle, & Yu (2020) 
    <doi:10.1177/1094428117736137>. Also included are fit indices described by 
    Hancock and Mueller (2011) <doi:10.1177/0013164410384856>.

In causal mediation analysis with multiple causally ordered mediators, a set of path-specific
  effects are identified under standard ignorability assumptions. This package implements an imputation
  approach to estimating these effects along with a set of bias formulas for conducting sensitivity analysis
  (Zhou and Yamamoto <doi:10.31235/osf.io/2rx6p>). It contains two main functions: paths() for estimating 
  path-specific effects and sens() for conducting sensitivity analysis. Estimation uncertainty is quantified 
  using the nonparametric bootstrap.

Tools to import, clean, and visualize movement data,
    particularly from motion capture systems such as Optitrack's 
    'Motive', the Straw Lab's 'Flydra', or from other sources. We provide 
    functions to remove artifacts, standardize tunnel position and tunnel 
    axes, select a region of interest, isolate specific trajectories, fill
    gaps in trajectory data, and calculate 3D and per-axis velocity. For 
    experiments of visual guidance, we also provide functions that use 
    subject position to estimate perception of visual stimuli.

For a given graph containing vertices, edges, and a signal associated with the vertices, the 'PathwaySpace' package performs a convolution operation, which involves a weighted combination of neighboring vertices and their associated signals. The package then uses a decay function to project these signals, creating geodesic paths on a 2D-image space. 'PathwaySpace' could have various applications, such as visualizing and analyzing network data in a graphical format that highlights the relationships and signal strengths between vertices. It can be particularly useful for understanding the influence of signals through complex networks. By combining graph theory, signal processing, and visualization, the 'PathwaySpace' package provides a novel way of representing and analyzing graph data.