Merge pull request #39 from SebKrantz/development

Adding CRAN badges + minor improvements in description and documentat…

Author: SebKrantz

DESCRIPTION

@@ -4,18 +4,18 @@ Title: Dynamic Factor Models
 Authors@R: c(person("Sebastian", "Krantz", role = c("aut", "cre"), email = "[email protected]"),
              person("Rytis", "Bagdziunas", role = "aut"))
 Description: Efficient estimation of Dynamic Factor Models using the Expectation Maximization (EM) algorithm 
-  or Two-Step (2S) estimation, supporting datasets with missing data. The implementation follows advances in the 
-  econometric literature: estimation can be done either by running the Kalman Filter and Smoother once with initial 
-  values from PCA - 2S estimation following Doz, Giannone and Reichlin (2011) <doi:10.1016/j.jeconom.2011.02.012> - 
-  or via iterated Kalman Filtering and Smoothing until EM convergence - following Doz, Giannone and Reichlin (2012) 
+  or Two-Step (2S) estimation, supporting datasets with missing data. The estimation options follow advances in the 
+  econometric literature: either running the Kalman Filter and Smoother once with initial values from PCA - 
+  2S estimation as in Doz, Giannone and Reichlin (2011) <doi:10.1016/j.jeconom.2011.02.012> - or via iterated 
+  Kalman Filtering and Smoothing until EM convergence - following Doz, Giannone and Reichlin (2012) 
   <doi:10.1162/REST_a_00225> - or using the adapted EM algorithm of Banbura and Modugno (2014) <doi:10.1002/jae.2306>, 
   allowing arbitrary patterns of missing data. The implementation makes heavy use of the 'Armadillo' 'C++' library and 
   the 'collapse' package, providing for particularly speedy estimation. A comprehensive set of methods supports 
   interpretation and visualization of the model as well as forecasting. Information criteria to choose the number 
   of factors are also provided - following Bai and Ng (2002) <doi:10.1111/1468-0262.00273>.
 URL: https://sebkrantz.github.io/dfms/
 BugReports: https://github.com/SebKrantz/dfms/issues
-Depends: R (>= 3.0.0)
+Depends: R (>= 3.3.0)
 Imports: Rcpp (>= 1.0.1), collapse (>= 1.8.0)
 LinkingTo: Rcpp, RcppArmadillo
 Suggests: xts, vars, magrittr, testthat (>= 3.0.0), knitr, rmarkdown, covr

R/methods.R

@@ -569,7 +569,7 @@ plot.dfm_forecast <- function(x,
     F_fcst <- x$F_fcst[, factors, drop = FALSE]
     if(scale.factors) {
       fcstat <- qsu(Fa)
-      F_fcst <- setop(TRA.matrix(F_fcst, fcstat[, "Mean"], "-"), "/", fcstat[, "SD"], rowwise = TRUE)
+      F_fcst <- setop(TRA.matrix(F_fcst, fcstat[, "Mean"], "-"), "/", fcstat[, "SD"], rowwise = TRUE) # Unscale ??
       Fa <- fscale(Fa)
     }
     if(nyliml) Fr <- frange(Fa)
@@ -706,6 +706,9 @@ as.data.frame.dfm_forecast <- function(x, ...,
 #' plot(ics)
 #' screeplot(ics)
 #'
+#' # Optimal lag-order with 6 factors chosen
+#' VARselect(ics$F_pca[, 1:6])
+#'
 #' @references
 #' Bai, J., Ng, S. (2002). Determining the Number of Factors in Approximate Factor Models. \emph{Econometrica, 70}(1), 191-221. \doi{10.1111/1468-0262.00273}
 #'

README.md

@@ -1,7 +1,13 @@
 # **dfms**: Dynamic Factor Models for R
 <!-- badges: start -->
 [![R-CMD-check](https://github.com/SebKrantz/dfms/workflows/R-CMD-check/badge.svg)](https://github.com/SebKrantz/dfms/actions)
+[![CRAN status](https://www.r-pkg.org/badges/version/dfms)](https://cran.r-project.org/package=dfms) 
+[![cran checks](https://cranchecks.info/badges/worst/dfms)](https://cran.r-project.org/web/checks/check_results_dfms.html)
+![downloads per month](http://cranlogs.r-pkg.org/badges/dfms?color=blue)
+![downloads](http://cranlogs.r-pkg.org/badges/grand-total/dfms?color=blue)
 [![Codecov test coverage](https://codecov.io/gh/SebKrantz/dfms/branch/main/graph/badge.svg)](https://app.codecov.io/gh/SebKrantz/dfms?branch=main)
+[![minimal R version](https://img.shields.io/badge/R%3E%3D-3.3.0-6666ff.svg)](https://cran.r-project.org/)
+[![status](https://tinyverse.netlify.com/badge/dfms)](https://CRAN.R-project.org/package=dfms)
 [![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
 <!-- badges: end -->
 

docs/articles/introduction.html

@@ -3,5 +3,5 @@ pkgdown: 2.0.6
 pkgdown_sha: ~
 articles:
   introduction: introduction.html
-last_built: 2022-10-10T22:13Z
+last_built: 2022-10-12T14:48Z