Distributed-Lag Structural Equation Modelling with Second-Order
Polynomial Lag Shapes

Inference functionalities for structural equation models with second-order polynomial and gamma lag shapes.

Inference functionalities for structural equation models with constrained lag shapes.

Distributed-Lag Linear Structural Equation Modelling
Inference functionalities for distributed-lag linear structural equation models with constrained lag shapes. Endpoint-constrained quadratic, quadratic decreasing and gamma lag shapes are available.

Inference functionalities for distributed-lag linear structural equation models. Endpoint-constrained quadratic, quadratic decreasing and gamma lag shapes are available.

Distributed-Lag Linear Structural Equation Models
Inference functionalities for distributed-lag linear structural equation models (DLSEMs). DLSEMs were recently proposed by Magrini et al. (2016) <ISBN:978-88-619-7061-8> as an extension of linear structural causal models (Pearl, 2000) <ISBN:978-0-521-89560-6>, where each factor of the joint probability distribution is a distributed-lag linear regression with constrained lag shapes. Endpoint-constrained quadratic, quadratic decreasing and gamma lag shapes are available.

Inference functionalities for distributed-lag linear structural equation models (DLSEMs). DLSEMs are an extension of linear structural causal models (Pearl, 2000, Chapter 5) <ISBN:978-0-521-89560-6>, where each factor of the joint probability distribution is a distributed-lag linear regression with constrained lag shapes (Judge et al., 1985, Chapters 9-10) <ISBN: 978-0-471-89530-5>. Endpoint-constrained quadratic, quadratic decreasing and gamma lag shapes are available.

Inference functionalities for distributed-lag linear structural equation models (DLSEMs). DLSEMs are Markovian structural causal models (Pearl, 2000, Chapter 5) <ISBN:978-0-521-89560-6>, where each factor of the joint probability distribution is a distributed-lag linear regression model (Judge et al., 1985, Chapters 9-10) <ISBN: 978-0-471-89530-5>. DLSEMs account for temporal delays in the dependence relationships among the variables and allow to perform dynamic causal inference by assessing causal effects at different time lags. Endpoint-constrained quadratic, quadratic decreasing and gamma lag shapes are available.