version 11.11.23

Author: JobNmadu

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: Dyn4cast
 Title: Dynamic Modeling and Machine Learning Environment
-Version: 11.11.10.9000
+Version: 11.11.23.9000
 Authors@R: 
     person(given = "Job",
            family = "Nmadu",
@@ -23,8 +23,6 @@ Imports:
     ggplot2,
     magrittr,
     formattable,
-    xlsx,
-    readxl,
     utils,
     lifecycle,
     zoo,
@@ -33,9 +31,12 @@ Imports:
     qpcR,
     dplyr,
     modelsummary,
-    kableExtra,
-    knitr,
-    timeDate
+    timeDate,
+    caret,
+    corrplot,
+    ggtext,
+    marginaleffects,
+    tibble
 Depends: 
     tidyverse,
     R (>= 2.10)
@@ -44,6 +45,10 @@ Suggests:
     rmarkdown,
     covr,
     qpdf,
+    xlsx,
+    readxl,
+    kableExtra,
+    knitr,
     spelling
 Config/testthat/edition: 3
 URL: https://github.com/JobNmadu/Dyn4cast

NAMESPACE

@@ -6,7 +6,9 @@ export(MLMetrics)
 export(MallowsCp)
 export(Percent)
 export(constrainedforecast)
+export(corplot)
 export(data_transform)
+export(estimate_plot)
 export(formattedcut)
 export(invscaledlogit)
 export(quicksummary)
@@ -54,30 +56,41 @@ importFrom(ModelMetrics,specificity)
 importFrom(ModelMetrics,tnr)
 importFrom(ModelMetrics,tpr)
 importFrom(broom,augment)
+importFrom(caret,varImp)
+importFrom(corrplot,corrplot.mixed)
 importFrom(dplyr,mutate)
 importFrom(dplyr,select_if)
 importFrom(forecast,auto.arima)
 importFrom(forecast,forecast)
 importFrom(formattable,comma)
+importFrom(ggplot2,.data)
 importFrom(ggplot2,aes)
+importFrom(ggplot2,element_blank)
 importFrom(ggplot2,facet_wrap)
+importFrom(ggplot2,geom_col)
+importFrom(ggplot2,geom_jitter)
 importFrom(ggplot2,geom_line)
+importFrom(ggplot2,geom_point)
+importFrom(ggplot2,geom_text)
+importFrom(ggplot2,geom_vline)
 importFrom(ggplot2,ggplot)
 importFrom(ggplot2,ggsave)
+importFrom(ggplot2,guides)
 importFrom(ggplot2,labs)
 importFrom(ggplot2,scale_color_hue)
 importFrom(ggplot2,scale_fill_hue)
+importFrom(ggplot2,scale_y_continuous)
+importFrom(ggplot2,stat_summary)
 importFrom(ggplot2,theme)
 importFrom(ggplot2,theme_bw)
 importFrom(ggplot2,theme_minimal)
 importFrom(ggplot2,vars)
-importFrom(kableExtra,kable_styling)
-importFrom(knitr,kable)
+importFrom(ggtext,element_markdown)
 importFrom(lifecycle,badge)
 importFrom(magrittr,"%>%")
+importFrom(marginaleffects,avg_slopes)
 importFrom(modelsummary,modelsummary)
 importFrom(qpcR,PRESS)
-importFrom(readxl,read_excel)
 importFrom(splines,bs)
 importFrom(stats,AIC)
 importFrom(stats,BIC)
@@ -91,10 +104,10 @@ importFrom(stats,quantile)
 importFrom(stats,sd)
 importFrom(stats,smooth.spline)
 importFrom(stats,var)
+importFrom(tibble,rownames_to_column)
 importFrom(tidyr,pivot_longer)
 importFrom(timeDate,kurtosis)
 importFrom(timeDate,skewness)
 importFrom(utils,globalVariables)
-importFrom(xlsx,write.xlsx2)
 importFrom(zoo,as.Date)
 importFrom(zoo,yearmon)

NEWS.md

@@ -1,9 +1,19 @@
 
+# Dyn4cast 11.11.23 2023-07-07
+
+The new version is coming with a huge improvement. The number of functional forms of the linear regression has increased from seven to 14 based on the Taylor Series expansion of the Box-Cox transformations. In addition, two new plots are added to the suite i.e. fitted and effects plots. Also, Tables of marginal effects are now produced for fast inference from the estimated coefficients. This new version also comes with 47 metrics for assessing model performance and carry out model diagnostics. This new version also comes with the capability of making prediction about each of the functional forms.
+
+There are two new functions coming along with this new version. `corplot` for plotting the correlation matrix of the variables included in the model. `estimate_plot` is for plotting the estimated coefficients in their order of significance in the estimated model. In addition to a new plot, `visual means` it has become lots easier to understand the nature of the distribution of the variables, digest and make inference about the model.
+
+Even with this huge improvements, the principle of one line of code is maintained. It is easy and can been implemented with less headaches once the data has been cleaned. The structure is `Linearsystems(y, x, mod, limit, Test)`. The argument `Test` is optional, if omitted, the prediction is made with `NULL` data.
+
+As always, comments, questions and suggestions are welcome.
+
 # Dyn4cast 11.11.10-kappa 2023-04-02
 
-`quicksummary` now returs a data frame rather a `kable` Table.
+`quicksummary` now returns a data frame rather a `kable` Table.
 
-The internal argument `digits` is remooved due to observed exageration of the final rates
+The internal argument `digits` in `Percent` is removed due to observed exaggeration of the final rates
 
 Final formatting of data frames in the `Linearsystems` package was removed. Now, the estimates can display in the `console`.
 

R/DynamicForecast.R

@@ -25,8 +25,6 @@
 #' @importFrom ggplot2 aes
 #' @importFrom ggplot2 labs
 #' @importFrom ggplot2 scale_color_hue
-#' @importFrom xlsx write.xlsx2
-#' @importFrom readxl read_excel
 #' @importFrom magrittr %>%
 #' @importFrom formattable comma
 #' @importFrom forecast auto.arima