fix invalid URLs

Author: strengejacke

NEWS.md

@@ -294,7 +294,7 @@ Parts of the **parameter** package are restructured and functions focussing on a
 - `standard_error()` for mixed models gets an `effects` argument, to return standard errors for random effects.
 - The `method`-argument for `ci()` gets a new option, `"robust"`, to compute confidence intervals based on robust standard errors. Furthermore, `ci_wald()` gets a `robust`-argument to do the same.
 - `format_p()` gets a `digits`-argument to set the amount of digits for p-values.
-- `model_parameters()` now accepts (non-documented) arguments `digits`, `ci_digits` and `p_digits` to change the amount and style of formatting values. See [examples in `model_parameters.lm()`](https://easystats.github.io/parameters/reference/model_parameters.lm.html).
+- `model_parameters()` now accepts (non-documented) arguments `digits`, `ci_digits` and `p_digits` to change the amount and style of formatting values. See [examples in `model_parameters.default()`](https://easystats.github.io/parameters/reference/model_parameters.default.html).
 - Improved `print()` method for `model_parameters()` when used with Bayesian models.
 - Added further method (gap-statistic) to `n_clusters()`.
 

vignettes/parameters_reduction.Rmd

@@ -85,7 +85,7 @@ pca <- principal_components(insight::get_predictors(model), n = "auto")
 pca
 ```
 
-The `principal_component()` function automatically selected one component (if the number of components is not specified, this function uses [`n_factors()`](https://easystats.github.io/parameters/articles/n_factors.html) to estimate the optimal number to keep) and returned the **loadings**, i.e., the relationship with all of the original variables. 
+The `principal_component()` function automatically selected one component (if the number of components is not specified, this function uses [`n_factors()`](https://easystats.github.io/parameters/reference/n_factors.html) to estimate the optimal number to keep) and returned the **loadings**, i.e., the relationship with all of the original variables. 
 
 As we can see here, it seems that our new component captured the essence (more than half of the total variance present in the original dataset) of all our other variables together. We can **extract** the values of this component for each of our observation using the `predict()` method and add in the response variable of our initial dataset.