update README

R/vinereg.R

@@ -119,7 +119,7 @@ vinereg <- function(formula, data, family_set = "parametric", selcrit = "aic",
     bicop,
     modifyList(arg, list(...))
   )$controls
-  ctrl$weights <- NULL
+  ctrl$weights <- numeric()
 
   if (!all(is.na(order))) {
     check_order(order, names(mfx))
@@ -181,7 +181,7 @@ vinereg <- function(formula, data, family_set = "parametric", selcrit = "aic",
       u <- as.matrix(mfx)
     }
 
-    args <- append(
+    args <- modifyList(
       ctrl,
       list(data = u, var_types = var_types, cores = cores, weights = weights)
     )

README.Rmd

@@ -15,7 +15,7 @@ knitr::opts_chunk$set(
 
 # vinereg
 [![R build status](https://github.com/tnagler/vinereg/workflows/R-CMD-check/badge.svg)](https://github.com/tnagler/vinereg)
-[![Coverage status](https://codecov.io/gh/tnagler/vinereg/branch/master/graph/badge.svg)](https://codecov.io/github/tnagler/vinereg?branch=master)
+<!-- [![Coverage status](https://codecov.io/gh/tnagler/vinereg/branch/main/graph/badge.svg)](https://app.codecov.io/github/tnagler/vinereg?branch=main) -->
 [![CRAN status](https://www.r-pkg.org/badges/version/vinereg)](https://cran.r-project.org/package=vinereg)
 
 An R package for D-vine copula based mean and quantile regression.

README.md

@@ -4,8 +4,7 @@
 
 [![R build
 status](https://github.com/tnagler/vinereg/workflows/R-CMD-check/badge.svg)](https://github.com/tnagler/vinereg)
-[![Coverage
-status](https://codecov.io/gh/tnagler/vinereg/branch/master/graph/badge.svg)](https://codecov.io/github/tnagler/vinereg?branch=master)
+<!-- [![Coverage status](https://codecov.io/gh/tnagler/vinereg/branch/main/graph/badge.svg)](https://app.codecov.io/github/tnagler/vinereg?branch=main) -->
 [![CRAN
 status](https://www.r-pkg.org/badges/version/vinereg)](https://cran.r-project.org/package=vinereg)
 
@@ -39,30 +38,31 @@ See the [package website](https://tnagler.github.io/vinereg/).
 
     # fit model
     (fit <- vinereg(mpg ~ ., family = "nonpar", data = mtcars))
-    #> D-vine regression model: mpg | disp, qsec, hp 
-    #> nobs = 32, edf = 24.35, cll = -56.11, caic = 160.93, cbic = 196.63
+    #> D-vine regression model: mpg | disp, qsec, hp, drat 
+    #> nobs = 32, edf = 25.6, cll = -51.94, caic = 155.08, cbic = 192.61
 
     summary(fit)
-    #>    var       edf         cll       caic        cbic      p_value
-    #> 1  mpg  0.000000 -100.189867 200.379733 200.3797334           NA
-    #> 2 disp 12.663078   29.654825 -33.983493 -15.4227648 5.284917e-08
-    #> 3 qsec  2.447922    4.326359  -3.756874  -0.1688666 2.101665e-02
-    #> 4   hp  9.242147   10.096325  -1.708356  11.8381898 1.898984e-02
+    #>    var       edf         cll       caic       cbic      p_value
+    #> 1  mpg  0.000000 -100.189867 200.379733 200.379733           NA
+    #> 2 disp 13.187762   29.521786 -32.668047 -13.338271 9.065782e-08
+    #> 3 qsec  2.272103    4.454079  -4.363952  -1.033648 1.559593e-02
+    #> 4   hp  7.178554   10.836467  -7.315826   3.206038 3.267907e-03
+    #> 5 drat  2.965553    3.441702  -0.952298   3.394419 7.382604e-02
 
     # show marginal effects for all selected variables
     plot_effects(fit)
-    #> `geom_smooth()` using method = 'loess' and formula 'y ~ x'
+    #> `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
 
 <img src="man/figures/README-unnamed-chunk-1-1.png" width="100%" />
 
 
     # predict mean and median
     head(predict(fit, mtcars, alpha = c(NA, 0.5)), 4)
     #>       mean      0.5
-    #> 1 22.39380 22.29866
-    #> 2 22.23677 22.08015
-    #> 3 25.33841 24.92450
-    #> 4 20.33708 20.16153
+    #> 1 22.58394 22.45433
+    #> 2 22.53425 22.41825
+    #> 3 25.10289 24.93384
+    #> 4 20.70358 20.80241
 
 ## Vignettes
 

cran-comments.md

@@ -5,5 +5,3 @@
 
 ## Results
 * 0 errors, 0 warnings
-
-There are no reverse dependencies.