Better package page.

Author: SebKrantz

R/dfms.R

@@ -1,55 +1,60 @@
 #' Dynamic Factor Models
 #'
-#' *dfms* provides efficient estimation of Dynamic Factor Models via the EM Algorithm.
+#' @description
 #'
-#' Estimation can be done in 3 different ways following:
+#' *dfms* provides efficient estimation of Dynamic Factor Models via the EM Algorithm --- following Doz, Giannone & Reichlin (2011, 2012) and Banbura & Modugno (2014). The package has the following contents:
 #'
-#'  - Doz, C., Giannone, D., & Reichlin, L. (2011). A two-step estimator for large approximate dynamic factor models based on Kalman filtering. *Journal of Econometrics, 164*(1), 188-205. <doi:10.1016/j.jeconom.2011.02.012>
+#' **Information Criteria**
 #'
-#'  - Doz, C., Giannone, D., & Reichlin, L. (2012). A quasi-maximum likelihood approach for large, approximate dynamic factor models. *Review of Economics and Statistics, 94*(4), 1014-1024. <doi:10.1162/REST_a_00225>
-#'
-#'  - Banbura, M., & Modugno, M. (2014). Maximum likelihood estimation of factor models on datasets with arbitrary pattern of missing data. *Journal of Applied Econometrics, 29*(1), 133-160. <doi:10.1002/jae.2306>
-#'
-#'  The default is `em.method = "auto"`, which chooses `"BM"` following Banbura & Modugno (2014) with missing data or mixed frequency, and `"DGR"` following Doz, Giannone & Reichlin (2012) otherwise. Using `em.method = "none"` generates Two-Step estimates following Doz, Giannone & Reichlin (2011). This is extremely efficient on bigger datasets. PCA and Two-Step estimates are also reported in EM-estimation. All methods support missing data, but `em.method = "DGR"` does not model them in EM iterations.
-#'
-#' @section Package Contents:
-#'
-#' **Functions to Specify/Estimate Model and Key Methods**
-#'
-#'  \code{\link[=ICr]{ICr()}} --- Information Criteria\cr
+#'  \code{\link[=ICr]{ICr()}}\cr
 #'
 #'  - \code{\link[=plot.ICr]{plot(<ICr>)}}\cr
 #'  - \code{\link[=screeplot.ICr]{screeplot(<ICr>)}}\cr
 #'
-#'  \code{\link[=DFM]{DFM()}} --- Estimate the Model\cr
+#' **Fit a Dynamic Factor Model**
+#'
+#'  \code{\link[=DFM]{DFM()}}\cr
 #'
 #'  - \code{\link[=summary.dfm]{summary(<dfm>)}}\cr
 #'  - \code{\link[=plot.dfm]{plot(<dfm>)}}\cr
 #'  - \code{\link[=as.data.frame.dfm]{as.data.frame(<dfm>)}}\cr
 #'  - \code{\link[=residuals.dfm]{residuals(<dfm>)}}\cr
 #'  - \code{\link[=fitted.dfm]{fitted(<dfm>)}}
 #'
-#'  \code{\link[=predict.dfm]{predict(<dfm>)}} --- Generate Forecasts\cr
+#' **Generate Forecasts**
+#'
+#'  \code{\link[=predict.dfm]{predict(<dfm>)}}\cr
 #'
 #'  - \code{\link[=plot.dfm_forecast]{plot(<dfm_forecast>)}}\cr
 #'  - \code{\link[=as.data.frame.dfm_forecast]{as.data.frame(<dfm_forecast>)}}\cr
 #'
-#' **Auxiliary Functions**
+#' **Fast Stationary Kalman Filtering and Smoothing**
 #'
-#'  \code{\link[=.VAR]{.VAR()}} --- Estimate Vector Autoregression\cr
 #'  \code{\link[=SKF]{SKF()}} --- Stationary Kalman Filter\cr
 #'  \code{\link[=FIS]{FIS()}} --- Fixed Interval Smoother\cr
 #'  \code{\link[=SKFS]{SKFS()}} --- Stationary Kalman Filter + Smoother\cr
+#'
+#'  **Helper Functions**
+#'
+#'  \code{\link[=.VAR]{.VAR()}} --- (Fast) Barebones Vector-Autoregression\cr
+#'  \code{\link[=ainv]{ainv()}} --- Armadillo's Inverse Function\cr
+#'  \code{\link[=apinv]{apinv()}} --- Armadillo's Pseudo-Inverse Function\cr
 #'  \code{\link[=tsnarmimp]{tsnarmimp()}} --- Remove and Impute Missing Values in a Multivariate Time Series\cr
-#'  \code{\link[=ainv]{ainv()}} --- Rcpp Armadillo's Inverse Function\cr
-#'  \code{\link[=apinv]{apinv()}} --- Rcpp Armadillo's Pseudo-Inverse Function\cr
+#'  \code{\link[=em_converged]{em_converged()}} --- Convergence Test for EM-Algorithm\cr
 #'
 #' **Data**
 #'
 #'  \code{\link{BM14_M}} --- Monthly Series by Banbura and Modugno (2014)\cr
 #'  \code{\link{BM14_Q}} --- Quarterly Series by Banbura and Modugno (2014)\cr
 #'  \code{\link{BM14_Models}} --- Series Metadata + Small/Medium/Large Model Specifications\cr
 #'
+#' @references
+#' Doz, C., Giannone, D., & Reichlin, L. (2011). A two-step estimator for large approximate dynamic factor models based on Kalman filtering. *Journal of Econometrics, 164*(1), 188-205. <doi:10.1016/j.jeconom.2011.02.012>
+#'
+#' Doz, C., Giannone, D., & Reichlin, L. (2012). A quasi-maximum likelihood approach for large, approximate dynamic factor models. *Review of Economics and Statistics, 94*(4), 1014-1024. <doi:10.1162/REST_a_00225>
+#'
+#' Banbura, M., & Modugno, M. (2014). Maximum likelihood estimation of factor models on datasets with arbitrary pattern of missing data. *Journal of Applied Econometrics, 29*(1), 133-160. <doi:10.1002/jae.2306>
+#'
 #' @docType package
 #' @name dfms-package
 #' @aliases dfms