Merge branch 'develop'

DESCRIPTION

@@ -1,12 +1,17 @@
 Package: rjd3nowcasting
 Type: Package
 Title: Nowcasting with 'JDemetra+ 3.0'
-Version: 2.0.2
-Authors@R: 
+Version: 3.0.3
+Authors@R: c(
     person(given = "Corentin",
            family = "Lemasson",
            role = c("aut", "cre"),
-           email = "[email protected]")
+           email = "[email protected]"),
+    person(given = "Tanguy", 
+           family = "Barthelemy", 
+           role = c("ctb", "art"),
+           email ="[email protected]")
+    )
 Description: Interface around 'JDemetra+ 3.x' 
              (<https://github.com/jdemetra/jdplus-nowcasting>), TSACE project.
 	     It defines and estimates Dynamic Factor Models with the
@@ -16,8 +21,8 @@ Depends:
 Imports:
   rJava (>= 1.0-6),
   rjd3toolkit (>= 3.2.2)
-Remotes:
-  github::rjdverse/rjd3toolkit@*release
+Remotes: 
+    github::rjdverse/rjd3toolkit
 SystemRequirements: Java (>= 17)
 License: EUPL
 URL: https://github.com/rjdverse/rjd3nowcasting,

NEWS.md

@@ -8,6 +8,9 @@ to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
 ## [Unreleased]
 
+## [3.0.3] - 2024-07-12
+
+
 ## [2.0.0] - 2024-06-04
 
 ## [1.0.0] - 2023-12-21
@@ -16,6 +19,7 @@ to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
 * update version number
 
-[Unreleased]: https://github.com/rjdverse/rjd3nowcasting/compare/v2.0.0...HEAD
+[Unreleased]: https://github.com/rjdverse/rjd3nowcasting/compare/v3.0.3...HEAD
+[3.0.3]: https://github.com/rjdverse/rjd3nowcasting/compare/v2.0.0...v3.0.3
 [2.0.0]: https://github.com/rjdverse/rjd3nowcasting/compare/v1.0.0...v2.0.0
 [1.0.0]: https://github.com/rjdverse/rjd3nowcasting/releases/tag/v1.0.0

R/estimation.R

@@ -161,9 +161,9 @@ create_model<-function(nfactors, nlags, factors_type, factors_loading, var_init
   jvar_coefficients <- rjd3toolkit::.r2jd_matrix(var_coefficients)
   jvar_errors_variance <- rjd3toolkit::.r2jd_matrix(var_errors_variance)
   jmeasurement_coefficients <- rjd3toolkit::.r2jd_matrix(measurement_coefficients)
-  if(is.null(measurement_errors_variance)){
+  if (is.null(measurement_errors_variance)){
     jmeasurement_errors_variance <- .jnull("[D")
-  }else{
+  } else {
     jmeasurement_errors_variance <- .jarray(as.numeric(measurement_errors_variance))
   }
 
@@ -226,14 +226,14 @@ create_model<-function(nfactors, nlags, factors_type, factors_loading, var_init
 estimate_pca<-function(dfm, data, standardized = FALSE,
                        input_standardization = NULL, re_estimate = TRUE){
 
-  if(re_estimate){
+  if (re_estimate){
     jdfm<-.r2jd_dfm(dfm)
     freq<-stats::frequency(data)
     start<-start(data)
     jdata<-rjd3toolkit::.r2jd_matrix(data)
-    if(is.null(input_standardization)){
+    if (is.null(input_standardization)){
       standardization_mean <- standardization_stdev <- .jnull(class = "[D")
-    } else{
+    } else {
       standardization_mean <- input_standardization[,1]
       standardization_stdev <- input_standardization[,2]
     }
@@ -261,8 +261,8 @@ estimate_pca<-function(dfm, data, standardized = FALSE,
     hessian<-rjd3toolkit::result(jest,"hessian")
     has_converged<-rjd3toolkit::result(jest,"has_converged")
 
-  }else{
-    if(!standardized && is.null(input_standardization)){
+  } else {
+    if (!standardized && is.null(input_standardization)){
       stop("Since you chose not to re-estimate your model, you must also
            fill the argument 'input_standardization' with the original mean
            and standard deviation that was previously used to standardize your
@@ -340,14 +340,14 @@ estimate_em<-function(dfm, data, standardized = FALSE, input_standardization = N
                       pca_init = TRUE, max_iter = 100, eps = 1e-9,
                       re_estimate = TRUE){
 
-  if(re_estimate){
+  if (re_estimate){
     jdfm<-.r2jd_dfm(dfm)
     freq<-stats::frequency(data)
     start<-start(data)
     jdata<-rjd3toolkit::.r2jd_matrix(data)
-    if(is.null(input_standardization)){
+    if (is.null(input_standardization)){
       standardization_mean <- standardization_stdev <- .jnull(class = "[D")
-    } else{
+    } else {
       standardization_mean <- input_standardization[,1]
       standardization_stdev <- input_standardization[,2]
     }
@@ -378,8 +378,8 @@ estimate_em<-function(dfm, data, standardized = FALSE, input_standardization = N
     hessian<-rjd3toolkit::result(jest,"hessian")
     has_converged<-rjd3toolkit::result(jest,"has_converged")
 
-  }else{
-    if(!standardized && is.null(input_standardization)){
+  } else {
+    if (!standardized && is.null(input_standardization)){
       stop("Since you chose not to re-estimate your model, you must also
            fill the argument 'input_standardization' with the original mean
            and standard deviation that was previously used to standardize your
@@ -479,14 +479,14 @@ estimate_ml<-function(dfm, data, standardized = FALSE, input_standardization = N
                       simpl_model_iter = 15, independent_var_shocks = FALSE,
                       mixedEstimation = TRUE, eps=1e-9, re_estimate = TRUE){
 
-  if(re_estimate){
+  if (re_estimate){
     jdfm<-.r2jd_dfm(dfm)
     freq<-stats::frequency(data)
     start<-start(data)
     jdata<-rjd3toolkit::.r2jd_matrix(data)
-    if(is.null(input_standardization)){
+    if (is.null(input_standardization)){
       standardization_mean <- standardization_stdev <- .jnull(class = "[D")
-    } else{
+    } else {
       standardization_mean <- input_standardization[,1]
       standardization_stdev <- input_standardization[,2]
     }
@@ -524,8 +524,8 @@ estimate_ml<-function(dfm, data, standardized = FALSE, input_standardization = N
     hessian<-rjd3toolkit::result(jest,"hessian")
     has_converged<-rjd3toolkit::result(jest,"has_converged")
 
-  }else{
-    if(!standardized && is.null(input_standardization)){
+  } else {
+    if (!standardized && is.null(input_standardization)){
       stop("Since you chose not to re-estimate your model, you must also
            fill the argument 'input_standardization' with the original mean
            and standard deviation that was previously used to standardize your

R/news.R

@@ -24,17 +24,17 @@ DFMNEWS<-'JD3_DfmNews'
   colnames(fcsts)<-fcst_periods
   rownames(fcsts)<-c("old_forecasts", "revised_forecasts", "new_forecasts")
 
-  if(target_factor_type == "Q"){
+  if (target_factor_type == "Q"){
     nc<-ncol(fcsts)
     for(j in nc:1){
       nkj<-nchar(fcst_periods[j])
       mth<-as.numeric(substr(fcst_periods[j],1,nkj-5))
-      if(mth%%3 == 0){
+      if (mth%%3 == 0){
         yr_str<-substr(fcst_periods[j],nkj-4,nkj)
         colnames(weights)[j+5]<-paste0("weights(Q", mth/3, yr_str,")")
         colnames(impacts)[j+5]<-paste0("Impacts(Q", mth/3, yr_str,")")
         colnames(fcsts)[j]<-paste0("Q", mth/3, yr_str)
-      }else{
+      } else {
         weights<-weights[,-(j+5)]
         impacts<-impacts[,-(j+5)]
         fcsts<-fcsts[,-j,drop=FALSE]
@@ -82,23 +82,23 @@ get_news<-function(dfm_estimates, new_data, target_series = NULL, n_fcst = 3){
   old_data<-dfm_estimates$data
   series_names<-colnames(old_data)
 
-  if(!is.null(target_series)){
+  if (!is.null(target_series)){
     target_series_index<-which(colnames(old_data) == target_series) - 1
 
-    if(length(target_series_index) == 0){
+    if (length(target_series_index) == 0){
       target_series_index<-0
       warning("Target series not found. The first series was considered by default instead.")
     }
-  }else{
+  } else {
     target_series<-series_names[1]
     target_series_index<-0
   }
 
-  if(n_fcst < 1){
+  if (n_fcst < 1){
     n_fcst<-3
   }
   target_factor_type<-dfm_estimates$dfm$factors_type[target_series_index + 1]
-  if(target_factor_type == "Q"){
+  if (target_factor_type == "Q"){
     n_fcst <- n_fcst*3
   }
 
@@ -153,9 +153,9 @@ plot.JD3_DfmNews <- function(x, ...){
   impacts_tot<-as.numeric(x_impacts[nr,start_i:(start_i+nc_fcst-1)])
   colnames(impacts)<-colnames(x_fcsts)
   rownames(impacts)<-x_impacts[-nr,1]
-  if(nr <= 10){
+  if (nr <= 10){
     plot_colors<-grDevices::palette.colors(nr-1)
-  }else{
+  } else {
     plot_colors<-grDevices::colorRampPalette(grDevices::palette.colors(8))(nr-1)
   }
   legend_txt<-paste0(x_impacts[-nr,1], " (", x_impacts[-nr,2], ")")

R/results.R

@@ -7,9 +7,9 @@ DFMFORECASTS<-'JD3_DfmForecasts'
   freq<-stats::frequency(data)
   start<-start(data)
   jdata<-rjd3toolkit::.r2jd_matrix(data)
-  if(is.null(input_standardization)){
+  if (is.null(input_standardization)){
     standardization_mean <- standardization_stdev <- .jnull(class = "[D")
-  } else{
+  } else {
     standardization_mean <- input_standardization[,1]
     standardization_stdev <- input_standardization[,2]
   }
@@ -54,9 +54,9 @@ DFMFORECASTS<-'JD3_DfmForecasts'
   freq <- stats::frequency(data)
   start <- start(data)
   jdata <- rjd3toolkit::.r2jd_matrix(data)
-  if(is.null(input_standardization)){
+  if (is.null(input_standardization)){
     standardization_mean <- standardization_stdev <- .jnull(class = "[D")
-  } else{
+  } else {
     standardization_mean <- input_standardization[,1]
     standardization_stdev <- input_standardization[,2]
   }
@@ -124,7 +124,7 @@ DFMFORECASTS<-'JD3_DfmForecasts'
   k <- 1
   for(i in 1:nlags){
     for(j in 1:nfactors){
-      if(i == 1) var_coef_rnames[j]<-paste0("F",j)
+      if (i == 1) var_coef_rnames[j]<-paste0("F",j)
       var_coef_cnames[k]<-paste0("F",j,"[",-i,"]")
       k<-k+1
     }
@@ -279,7 +279,7 @@ print.JD3_DfmResults <- function(x, ...){
 #'
 get_forecasts <- function(dfm_estimates, n_fcst = 3){
 
-  if(n_fcst < 1) n_fcst<-1
+  if (n_fcst < 1) n_fcst<-1
   data<-dfm_estimates$data
   dfm<-dfm_estimates$dfm
   is_standardized<-dfm_estimates$is_standardized
@@ -317,10 +317,10 @@ get_forecasts <- function(dfm_estimates, n_fcst = 3){
   fcsts_only<-stats::window(fcsts, start = c(strt_yr, strt_mth))
   fcsts_only_stderr<-stats::window(fcsts_stderr, start = c(strt_yr, strt_mth))
 
-  if(nf_max > 0){
+  if (nf_max > 0){
     for(j in 1:nc){
       n_na<-nf_max-nf[j]
-      if(n_na>0) fcsts_only[1:n_na,j]<-fcsts_only_stderr[1:n_na,j]<-NA
+      if (n_na>0) fcsts_only[1:n_na,j]<-fcsts_only_stderr[1:n_na,j]<-NA
     }
   }
 
@@ -360,16 +360,16 @@ plot.JD3_DfmForecasts <- function(x, series_name = NULL, ...){
   fcst_stdev<-x$forecasts_stdev
   fcst_only<-x$forecasts_only
 
-  if(is.null(series_name)){
+  if (is.null(series_name)){
     series_name<-colnames(fcst)[1]
   }
 
-  if(series_name %in% colnames(fcst)){
+  if (series_name %in% colnames(fcst)){
     s<-fcst[,series_name]
     s_lb<-s - 1.28 * fcst_stdev[,series_name]
     s_ub<-s + 1.28 * fcst_stdev[,series_name]
     sf<-fcst_only[,series_name]
-  }else{
+  } else {
     stop("series name not found!")
   }
 
@@ -378,7 +378,3 @@ plot.JD3_DfmForecasts <- function(x, series_name = NULL, ...){
   graphics::axis(1, at=seq(stats::start(s)[1], stats::end(s)[1], by = 1), las=2)
   graphics::legend("topleft", legend=c("series", "forecasts", "80% PI"), col=c("black","red","orange"), lty=c(1, 1, 3), cex=0.8)
 }
-
-
-
-