_pkgdown.yml

destination: docs

home:
  title: Dynamic Factor Models for R

# template:
#  bootstrap: 5

articles:
  - title: Introduction to dfms
    desc: Provides a walk-through of all main features
    contents:
    - introduction

right:
   - icon: fa-github
     href: https://github.com/SebKrantz/dfms

reference:
- title: "Information Criteria"
  desc: "Choose the number of factors and the lag-order of the factor VAR."
- contents:
  - ICr
- title: "Fit a Dynamic Factor Model"
  desc: "DFM estimation via the EM algorithm and PCA, and various methods inspect the model and extract results."
- contents:
  - DFM
  - print.dfm
  - summary.dfm
  - plot.dfm
  - as.data.frame.dfm
  - residuals.dfm
- title: "Forecasting"
  desc: "Forecast both the factors and the data, and methods to visualize forecasts and extract results."
- contents:
  - predict.dfm
- title: "Fast Stationary Kalman Filtering and Smoothing"
  desc: "Optimized Armadillo C++ implementations of the stationary Kalman Filter and Smoother."
- contents:
  - SKF
  - FIS
  - SKFS
- title: "Helper Functions"
  desc: "Fast VAR, matrix inverses, imputation/removal of missing values in multivariate time series, and convergence check for EM algorithm."
- contents:
  - .VAR
  - tsnarmimp
  - ainv
  - em_converged
- title: "Data"
  desc: "Euro area macroeconomic data from Banbura and Modugno (2014), and 3 DFM specifications considered in their paper."
- contents:
  - BM14_Models