fixed link paths

README.Rmd

@@ -131,7 +131,7 @@ Note that, depending on the value of the skewness parameter, the normal latent d
 
 ## Further Reading
 
-* For more detailed information and practical examples, please refer to the package [vignette](https://lalovic.io/latent2likert/articles/intro_to_latent2likert.html).
+* For more detailed information and practical examples, please refer to the package [vignette](https://lalovic.io/latent2likert/articles/using_latent2likert.html).
 * The implemented algorithms are described in the functions [reference](https://lalovic.io/latent2likert/reference/index.html).
 
 ## Related R Packages

README.md

@@ -6,8 +6,7 @@
 <!-- badges: start -->
 
 [![R-CMD-check](https://github.com/markolalovic/latent2likert/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/markolalovic/latent2likert/actions/workflows/R-CMD-check.yaml)
-[![Codecov test
-coverage](https://codecov.io/gh/markolalovic/latent2likert/branch/main/graph/badge.svg)](https://app.codecov.io/gh/markolalovic/latent2likert?branch=main)
+[![codecov](https://codecov.io/gh/markolalovic/latent2likert/branch/main/graph/badge.svg?token=HZTG6RUB2J)](https://codecov.io/gh/markolalovic/latent2likert)
 <!-- badges: end -->
 
 ## Overview
@@ -65,7 +64,7 @@ scale, use:
 ``` r
 #library(latent2likert)
 rlikert(size = 10, n_items = 1, n_levels = 5)
-#>  [1] 3 2 3 4 3 4 3 4 4 1
+#>  [1] 2 4 3 4 5 3 3 3 4 3
 ```
 
 To generate responses to multiple items with specified parameters:
@@ -78,16 +77,16 @@ rlikert(size = 10,
         sd   = c(0.8, 1, 1),
         corr = 0.5)
 #>       Y1 Y2 Y3
-#>  [1,]  2  1  4
-#>  [2,]  4  2  3
-#>  [3,]  2  2  2
-#>  [4,]  2  2  2
-#>  [5,]  2  1  3
-#>  [6,]  2  2  3
-#>  [7,]  3  1  2
-#>  [8,]  2  3  4
-#>  [9,]  3  5  4
-#> [10,]  3  1  5
+#>  [1,]  3  3  5
+#>  [2,]  2  1  1
+#>  [3,]  2  2  3
+#>  [4,]  3  2  5
+#>  [5,]  2  2  5
+#>  [6,]  3  2  4
+#>  [7,]  1  1  3
+#>  [8,]  3  1  4
+#>  [9,]  2  2  4
+#> [10,]  2  2  4
 ```
 
 You can also provide a correlation matrix:
@@ -111,9 +110,9 @@ these estimates are typically lower:
 ``` r
 cor(data)
 #>            Y1         Y2         Y3
-#> Y1  1.0000000 -0.5486188 -0.3592762
-#> Y2 -0.5486188  1.0000000  0.4172253
-#> Y3 -0.3592762  0.4172253  1.0000000
+#> Y1  1.0000000 -0.5271298 -0.3544365
+#> Y2 -0.5271298  1.0000000  0.4404984
+#> Y3 -0.3544365  0.4404984  1.0000000
 ```
 
 ## Using `estimate_params`
@@ -123,9 +122,9 @@ Given the data, you can estimate the values of latent parameters using:
 ``` r
 estimate_params(data, n_levels = c(4, 5, 6), skew = 0)
 #>          items
-#> estimates           Y1           Y2           Y3
-#>      mean  0.014663647 -1.009504893  0.004589209
-#>      sd    0.775035961  0.955310905  1.018213994
+#> estimates          Y1          Y2          Y3
+#>      mean  0.02434619 -1.01561017  0.08093401
+#>      sd    0.77743368  0.97395691  1.05227866
 ```
 
 ## Transformation
@@ -163,7 +162,7 @@ distribution is used. The value of skewness is restricted to the range
 
 - For more detailed information and practical examples, please refer to
   the package
-  [vignette](https://lalovic.io/latent2likert/articles/intro_to_latent2likert.html).
+  [vignette](https://lalovic.io/latent2likert/articles/using_latent2likert.html).
 - The implemented algorithms are described in the functions
   [reference](https://lalovic.io/latent2likert/reference/index.html).
 

docs/pkgdown.yml

@@ -3,7 +3,7 @@ pkgdown: 2.0.9
 pkgdown_sha: ~
 articles:
   using_latent2likert: using_latent2likert.html
-last_built: 2024-06-20T22:23Z
+last_built: 2024-06-20T22:27Z
 urls:
   reference: https://markolalovic.github.io/latent2likert/reference
   article: https://markolalovic.github.io/latent2likert/articles