Reference

Information Criteria Choose the number of factors and the lag-order of the factor VAR.
ICr() print(<ICr>) plot(<ICr>) screeplot(<ICr>)	Information Criteria to Determine the Number of Factors (r)
Fit a Dynamic Factor Model DFM estimation via the EM algorithm and PCA, and various methods inspect the model and extract results.
DFM()	Estimate a Dynamic Factor Model
print(<dfm>) summary(<dfm>) print(<dfm_summary>)	DFM Summary Methods
plot(<dfm>) screeplot(<dfm>)	Plot DFM
as.data.frame(<dfm>)	Extract Factor Estimates in a Data Frame
residuals(<dfm>) fitted(<dfm>)	DFM Residuals and Fitted Values
Forecasting Forecast both the factors and the data, and methods to visualize forecasts and extract results.
predict(<dfm>) print(<dfm_forecast>) plot(<dfm_forecast>) as.data.frame(<dfm_forecast>)	DFM Forecasts
Fast Stationary Kalman Filtering and Smoothing Optimized Armadillo C++ implementations of the stationary Kalman Filter and Smoother.
SKF()	(Fast) Stationary Kalman Filter
FIS()	(Fast) Fixed-Interval Smoother (Kalman Smoother)
SKFS()	(Fast) Stationary Kalman Filter and Smoother
Helper Functions Fast VAR, matrix inverses, imputation/removal of missing values in multivariate time series, and convergence check for EM algorithm.
.VAR()	(Fast) Barebones Vector-Autoregression
tsnarmimp()	Remove and Impute Missing Values in a Multivariate Time Series
ainv() apinv()	Armadillo's Inverse Functions
em_converged()	Convergence Test for EM-Algorithm
Data Euro area macroeconomic data from Banbura and Modugno (2014), and 3 DFM specifications considered in their paper.
BM14_Models BM14_M BM14_Q	Euro Area Macroeconomic Data from Banbura and Modugno 2014