# Dynamic factor analysis vs state space model

The MARSS package in R offers function for dynamic factor analysis. In this package, the dynamic factor model is written as a special form of state space model and they assume the common trends follow AR(1) process. As I am not very familiar with those two methods, I come with two questions:

Is the Dynamic Factor Analysis a special form of State Space Model? What is the difference between those two methods?

In addition, the Dynamic Factor Analysis does not necessary assume the common trends as AR(1) process. Is there any package that allows the the common trends as seasonal ARIMA (or some other) process?

The "factors" may have any time dynamics. Several R packages, if you use R, will let you specify a general dynamic factor analysis model, including for instance dlm or KFAS.