'SEM Shiny'

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/> .

Average and Conditional Effects

Use structural equation modeling to estimate average and conditional effects of a treatment variable on an outcome variable, taking into account multiple continuous and categorical covariates.

Conduct Additional Modeling and Analysis for 'seminr'

Supplemental functions for estimating and analysing structural equation models including Cross Validated Prediction and Testing (CVPAT, Liengaard et al., 2021 ).

Model BIC Posterior Probability

Fits the neighboring models of a fitted structural equation model and assesses the model uncertainty of the fitted model based on BIC posterior probabilities, using the method presented in Wu, Cheung, and Leung (2020) .

Fit Measure Cutoffs in SEM

Calculate cutoff values for model fit measures used in structural equation modeling (SEM) by simulating and testing data sets (cf. Hu & Bentler, 1999 ) with the same parameters (population model, number of observations, etc.) as the model under consideration.

Ancestor Regression

Causal discovery in linear structural equation models (Schultheiss, and Bühlmann (2023) ) and vector autoregressive models (Schultheiss, Ulmer, and Bühlmann (2025) ) with explicit error control for false discovery, at least asymptotically.

Social Relations Analyses with Roles ("Family SRM")

Social Relations Analysis with roles ("Family SRM") are computed, using a structural equation modeling approach. Groups ranging from three members up to an unlimited number of members are supported and the mean structure can be computed. Means and variances can be compared between different groups of families and between roles.

Graphical Extension with Accuracy in Parameter Estimation (GAIPE)

Implements graphical extension with accuracy in parameter estimation (AIPE) on RMSEA for sample size planning in structural equation modeling based on Lin, T.-Z. & Weng, L.-J. (2014) . And, it can also implement AIPE on RMSEA and power analysis on RMSEA.

Computationally Efficient Maximum Likelihood Identification of Linear Dynamical Systems

Provides implementations of computationally efficient maximum likelihood parameter estimation algorithms for models that represent linear dynamical systems. Currently, one such algorithm is implemented for the one-dimensional cumulative structural equation model with shock-error output measurement equation and assumptions of normality and independence. The corresponding scientific paper is yet to be published, therefore the relevant reference will be provided later.

Regularized Mediation Analysis

Mediation analysis for multiple mediators by penalized structural equation models with different types of penalties depending on whether there are multiple mediators and only one exposure and one outcome variable (using sparse group lasso) or multiple exposures, multiple mediators, and multiple outcome variables (using lasso, L1, penalties).