Functions for fitting general linear structural
    equation models (with observed and latent variables) using the RAM approach, 
    and for fitting structural equations in observed-variable models by two-stage least squares.

Style sheets and JavaScript assets for 'shiny.semantic' package.

It offers functions for creating dashboard with Fomantic UI.

We present a rank-based Mercer kernel to compute a pair-wise similarity metric corresponding to informative representation of data. We tailor the development of a kernel to encode our prior knowledge about the data distribution over a probability space. The philosophical concept behind our construction is that objects whose feature values fall on the extreme of that feature’s probability mass distribution are more similar to each other, than objects whose feature values lie closer to the mean. Semblance emphasizes features whose values lie far away from the mean of their probability distribution. The kernel relies on properties empirically determined from the data and does not assume an underlying distribution. The use of feature ranks on a probability space ensures that Semblance is computational efficacious, robust to outliers, and statistically stable, thus making it widely applicable algorithm for pattern analysis. The output from the kernel is a square, symmetric matrix that gives proximity values between pairs of observations.

Training and validation of a custom (or data-driven) Structural
    Equation Models using layer-wise Deep Neural Networks or node-wise
	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.