Merge pull request #5 from TanguyBarthelemy/develop

add LICENSE file and update README and description file

.Rbuildignore

@@ -1,2 +1,5 @@
 ^rjd3nowcasting\.Rproj$
 ^\.Rproj\.user$
+^\.github$
+^LICENSE$
+^README\.Rmd$

DESCRIPTION

@@ -18,12 +18,12 @@ Imports:
   rjd3toolkit (>= 3.2.1)
 Remotes:
   github::rjdemetra/rjd3toolkit
-SystemRequirements: Java JRE 17 or higher
+SystemRequirements: Java (>= 17)
 License: EUPL
 Encoding: UTF-8
 Collate: 
     'estimation.R'
     'news.R'
     'results.R'
     'zzz.R'
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1

README.Rmd

@@ -0,0 +1,112 @@
+---
+output: github_document
+---
+
+<!-- README.md is generated from README.Rmd. Please edit that file -->
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  fig.path = "man/figures/README-",
+  out.width = "100%", 
+  fig.align = "center",
+  fig.dim = c(7,4) * 1.4
+)
+```
+
+# rjd3nowcasting
+
+<!-- badges: start -->
+<!-- badges: end -->
+
+## Overview
+
+Nowcasting is often defined as the prediction of the present, the very near future and the very recent past. 
+
+rjd3nowcasting provides helps to operationalize the process of nowcasting. This first version can be used to specify and estimate dynamic factor models. 
+A later version is expected to include the concept of "news" similar to the [Nowcasting plugin](https://github.com/nbbrd/jdemetra-nowcasting/tree/master) 
+of the Graphical User Interface of JDemetra+ v2.
+
+## Installation
+
+To get the current stable version (from the latest release):
+
+```{r, echo = TRUE, eval = FALSE}
+# install.packages("remotes")
+remotes::install_github("rjdemetra/rjd3toolkit@*release")
+remotes::install_github("rjdemetra/rjd3nowcasting@*release")
+```
+
+To get the current development version from GitHub:
+
+```{r, echo = TRUE, eval = FALSE}
+# install.packages("remotes")
+remotes::install_github("rjdemetra/rjd3nowcasting")
+```
+
+
+## Usage
+
+```{r, echo = TRUE, eval = TRUE}
+library("rjd3nowcasting")
+```
+
+
+### Input
+
+```{r, echo = TRUE, eval = TRUE}
+set.seed(100)
+data <- ts(
+    data = matrix(rnorm(500), 100, 5), 
+    frequency = 12, 
+    start = c(2010, 1)
+)
+data[100, 1] <- data[99:100, 2] <- data[(1:100)[-seq(3, 100, 3)], 5] <- NA
+```
+
+
+### Model
+
+```{r, echo = TRUE, eval = TRUE}
+dfm_model <- model(
+    nfactors = 2,
+    nlags = 2,
+    factors_type = c("M", "M", "YoY", "M", "Q"),
+    factors_loading = matrix(data = TRUE, 5, 2),
+    var_init = "Unconditional"
+)
+```
+
+
+### Estimation
+
+```{r, echo = TRUE, eval = TRUE}
+rslt_ml <- estimate_ml(dfm_model, data)
+# or rslt_em<-estimate_em(dfm_model, data)
+# or rslt_pca<-estimate_pca(dfm_model, data)
+```
+
+
+### Results
+
+```{r, echo = TRUE, eval = TRUE}
+fcst <- get_forecasts(rslt_ml, nf = 2, forecasts_only = TRUE)
+params <- get_parameters(rslt_ml)
+factors <- get_factors(rslt_ml)
+# ...
+
+print(rslt_ml)
+summary(rslt_ml)
+plot(rslt_ml)
+```
+
+
+## Contributing
+
+Any contribution is welcome and should be done through pull requests and/or issues.
+
+
+## Licensing
+
+The code of this project is licensed under the [European Union Public Licence (EUPL)](https://joinup.ec.europa.eu/page/eupl-text-11-12).

README.md

@@ -1,10 +1,22 @@
+
+<!-- README.md is generated from README.Rmd. Please edit that file -->
+
+# rjd3nowcasting
+
+<!-- badges: start -->
+<!-- badges: end -->
+
 ## Overview
 
-Nowcasting is often defined as the prediction of the present, the very near future and the very recent past. 
+Nowcasting is often defined as the prediction of the present, the very
+near future and the very recent past.
 
-rjd3nowcasting provides helps to operationalize the process of nowcasting. This first version can be used to specify and estimate dynamic factor models. 
-A later version is expected to include the concept of "news" similar to the [Nowcasting plugin](https://github.com/nbbrd/jdemetra-nowcasting/tree/master) 
-of the Graphical User Interface of JDemetra+ v2.
+rjd3nowcasting provides helps to operationalize the process of
+nowcasting. This first version can be used to specify and estimate
+dynamic factor models. A later version is expected to include the
+concept of “news” similar to the [Nowcasting
+plugin](https://github.com/nbbrd/jdemetra-nowcasting/tree/master) of the
+Graphical User Interface of JDemetra+ v2.
 
 ## Installation
 
@@ -25,38 +37,91 @@ remotes::install_github("rjdemetra/rjd3nowcasting")
 
 ## Usage
 
+``` r
+library("rjd3nowcasting")
+```
+
 ### Input
+
 ``` r
 set.seed(100)
-data<-ts(matrix(rnorm(500), 100, 5), frequency = 12, start = c(2010,1))
-data[100,1]<-data[99:100,2]<-data[(1:100)[-seq(3,100,3)],5]<-NA
+data <- ts(
+    data = matrix(rnorm(500), 100, 5), 
+    frequency = 12, 
+    start = c(2010, 1)
+)
+data[100, 1] <- data[99:100, 2] <- data[(1:100)[-seq(3, 100, 3)], 5] <- NA
 ```
 
 ### Model
+
 ``` r
-dfm_model <- rjd3nowcasting::model(nfactors=2,
-                                   nlags=2,
-                                   factors_type = c("M", "M", "YoY", "M", "Q"),
-                                   factors_loading = matrix(data=TRUE, 5, 2),
-                                   var_init = "Unconditional")
+dfm_model <- model(
+    nfactors = 2,
+    nlags = 2,
+    factors_type = c("M", "M", "YoY", "M", "Q"),
+    factors_loading = matrix(data = TRUE, 5, 2),
+    var_init = "Unconditional"
+)
 ```
 
 ### Estimation
+
 ``` r
-rslt_ml<-rjd3nowcasting::estimate_ml(dfm_model, data)
-# or rslt_em<-rjd3nowcasting::estimate_em(dfm_model, data)
-# or rslt_pca<-rjd3nowcasting::estimate_pca(dfm_model, data)
+rslt_ml <- estimate_ml(dfm_model, data)
+# or rslt_em<-estimate_em(dfm_model, data)
+# or rslt_pca<-estimate_pca(dfm_model, data)
 ```
 
 ### Results
+
 ``` r
-fcst<-rjd3nowcasting::get_forecasts(rslt_ml,nf = 2,forecasts_only = TRUE)
-params<-rjd3nowcasting::get_parameters(rslt_ml)
-factors<-rjd3nowcasting::get_factors(rslt_ml)
-#...
+fcst <- get_forecasts(rslt_ml, nf = 2, forecasts_only = TRUE)
+params <- get_parameters(rslt_ml)
+factors <- get_factors(rslt_ml)
+# ...
 
 print(rslt_ml)
+#> Measurement:
+#>          Sample mean Sample Stdev Coeff. of normalized factor F1
+#> Series 1     0.01480      1.01376                        0.11234
+#> Series 2     0.01656      0.79045                       -0.07066
+#> Series 3     0.01279      1.03407                        0.00171
+#> Series 4    -0.08325      1.07544                       -0.00624
+#> Series 5    -0.21205      1.06909                       -0.00302
+#>          Coeff. of normalized factor F2 Idiosyncratic variance
+#> Series 1                        0.55341                0.41091
+#> Series 2                       -0.06925                0.93488
+#> Series 3                        0.04849                0.98831
+#> Series 4                        0.03340                0.99792
+#> Series 5                       -0.33934                0.00000
+#> 
+#> State:
+#> VAR coefficients:
+#>      F1[-1]   F2[-1]   F1[-2]   F2[-2]
+#> F1  1.44836 -0.97181 -0.76117  1.23216
+#> F2 -0.02442 -0.59011 -0.02834 -0.09459
+#> 
+#> Innovative variance:
+#>    F1 F2
+#> F1  1 -1
+#> F2 -1  1
 summary(rslt_ml)
+#> Nowcasted values (only):
+#>           Series 1     Series 2 Series 3 Series 4  Series 5
+#> Mar 2018        NA 0.3198988965       NA       NA        NA
+#> Apr 2018 0.1827459 0.0001724063       NA       NA -2.427725
 plot(rslt_ml)
 ```
 
+<img src="man/figures/README-unnamed-chunk-8-1.png" width="100%" style="display: block; margin: auto;" />
+
+## Contributing
+
+Any contribution is welcome and should be done through pull requests
+and/or issues.
+
+## Licensing
+
+The code of this project is licensed under the [European Union Public
+Licence (EUPL)](https://joinup.ec.europa.eu/page/eupl-text-11-12).