comments fixed

README.Rmd

@@ -30,14 +30,9 @@ The R package **latent2likert** is designed to effectively simulate the discreti
 You can install the development version from GitHub with:
 ```{r, eval = FALSE}
 # install.packages("devtools")
-#devtools::install_github("markolalovic/latent2likert")
+devtools::install_github("markolalovic/latent2likert")
 ```
 
-```{r, include = FALSE}
-devtools::load_all("/Users/markolalovic/dev/latent2likert/")
-```
-
-
 ## Dependencies
 
 To keep the package lightweight, **latent2likert** only imports [mvtnorm](https://cran.r-project.org/package=mvtnorm), along with the standard R packages stats and graphics, which are typically included in R releases. An additional suggested dependency is the [sn package](https://cran.r-project.org/package=sn), which is required only for generating random responses from correlated Likert items based on a multivariate skew normal distribution. The package prompts the user to install this dependency during interactive sessions if needed.
@@ -62,7 +57,7 @@ To keep the package lightweight, **latent2likert** only imports [mvtnorm](https:
 To generate a sample of random responses to one item on a 5-point Likert scale, use:
 
 ```{r}
-#library(latent2likert)
+library(latent2likert)
 rlikert(size = 10, n_items = 1, n_levels = 5)
 ```
 

README.md

@@ -25,7 +25,7 @@ You can install the development version from GitHub with:
 
 ``` r
 # install.packages("devtools")
-#devtools::install_github("markolalovic/latent2likert")
+devtools::install_github("markolalovic/latent2likert")
 ```
 
 ## Dependencies
@@ -62,9 +62,9 @@ To generate a sample of random responses to one item on a 5-point Likert
 scale, use:
 
 ``` r
-#library(latent2likert)
+library(latent2likert)
 rlikert(size = 10, n_items = 1, n_levels = 5)
-#>  [1] 2 4 3 4 5 3 3 3 4 3
+#>  [1] 4 2 3 2 1 3 2 3 2 3
 ```
 
 To generate responses to multiple items with specified parameters:
@@ -77,16 +77,16 @@ rlikert(size = 10,
         sd   = c(0.8, 1, 1),
         corr = 0.5)
 #>       Y1 Y2 Y3
-#>  [1,]  3  3  5
-#>  [2,]  2  1  1
+#>  [1,]  3  2  6
+#>  [2,]  2  2  5
 #>  [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
+#>  [4,]  3  1  3
+#>  [5,]  3  1  5
+#>  [6,]  3  3  6
+#>  [7,]  4  3  3
+#>  [8,]  3  3  6
+#>  [9,]  2  3  3
+#> [10,]  3  2  3
 ```
 
 You can also provide a correlation matrix:
@@ -110,9 +110,9 @@ these estimates are typically lower:
 ``` r
 cor(data)
 #>            Y1         Y2         Y3
-#> Y1  1.0000000 -0.5271298 -0.3544365
-#> Y2 -0.5271298  1.0000000  0.4404984
-#> Y3 -0.3544365  0.4404984  1.0000000
+#> Y1  1.0000000 -0.5634259 -0.3760927
+#> Y2 -0.5634259  1.0000000  0.4362989
+#> Y3 -0.3760927  0.4362989  1.0000000
 ```
 
 ## Using `estimate_params`
@@ -122,9 +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  0.02434619 -1.01561017  0.08093401
-#>      sd    0.77743368  0.97395691  1.05227866
+#> estimates            Y1            Y2            Y3
+#>      mean -0.0415071755 -0.9654381812  0.0004640853
+#>      sd    0.8131470573  0.9995025670  1.0275232490
 ```
 
 ## Transformation

docs/pkgdown.yml

@@ -3,7 +3,7 @@ pkgdown: 2.0.9
 pkgdown_sha: ~
 articles:
   using_latent2likert: using_latent2likert.html
-last_built: 2024-06-20T22:27Z
+last_built: 2024-06-20T22:53Z
 urls:
   reference: https://markolalovic.github.io/latent2likert/reference
   article: https://markolalovic.github.io/latent2likert/articles