plspm

plspm is a Python library designed for estimating Partial Least Squares Path Models (PLS-PM). PLS-PM is a statistical technique used to analyze complex relationships between latent variables, particularly in situations where traditional methods like Structural Equation Modeling (SEM) might struggle due to sample size limitations or non-normal data. The library aims to provide a user-friendly and efficient implementation of PLS-PM estimation.

The core functionality of plspm revolves around building and evaluating a path model. Users define a model by specifying the relationships between manifest variables (observed indicators) and latent variables, as well as the causal links between these latent variables. The library then applies the PLS algorithm to estimate the parameters of this model. This includes estimating the loadings (how well manifest variables represent latent variables) and the path coefficients (the strength and direction of relationships between latent variables).

plspm also offers tools for model evaluation. This typically involves assessing the reliability and validity of the latent variables, such as calculating Cronbach's alpha, composite reliability, and Average Variance Extracted (AVE). Furthermore, it provides measures to evaluate the quality of the model fit and the predictive power of the model. The library is intended for researchers and practitioners in fields such as marketing, management, social sciences, and information systems who utilize PLS-PM for their analytical needs.