Update env

05-transforms.qmd

@@ -422,7 +422,7 @@ geometric transforms for 2D and 3D space.
 A coordinate transform is a geometric transform that modifies the coordinates
 of all points in the domain without any advanced topological modification (i.e.,
 connectivities are preserved). The most prominent examples of coordinate
-transforms are `Translate`, `Rotate` and `Stretch`.
+transforms are `Translate`, `Rotate` and `Scale`.
 
 Let's load an additional geotable to see these transforms in action:
 
@@ -473,13 +473,13 @@ c = centroid(gt.geometry)
 gt |> Translate(-coordinates(c)...) |> viewer
 ```
 
-and stretch it with a positive factor for each dimension:
+and scale it with a positive factor for each dimension:
 
 ```{julia}
-gt |> Stretch(0.1, 0.2) |> viewer
+gt |> Scale(0.1, 0.2) |> viewer
 ```
 
-The `StdCoords` transform combines `Translate` and `Stretch` to standardize
+The `StdCoords` transform combines `Translate` and `Scale` to standardize
 the coordinates of the domain to the interval `[-0.5, 0.5]`:
 
 ```{julia}

10-correlation.qmd

@@ -477,14 +477,14 @@ To fit a specific theoretical model, we can use the `fit` function with the
 model as the first argument:
 
 ```{julia}
-Variography.fit(SphericalVariogram, g)
+GeoStatsFunctions.fit(SphericalVariogram, g)
 ```
 
 We can also let the framework select the model with minimum weighted least-squares
 error by passing the generic `Variogram` model to the function:
 
 ```{julia}
-γ = Variography.fit(Variogram, g)
+γ = GeoStatsFunctions.fit(Variogram, g)
 ```
 
 ## Remarks

11-interpolation.qmd

@@ -242,7 +242,7 @@ After estimating the empirical variogram, the next step consists of fitting
 a theoretical model. The behavior near the origin resembles a `SphericalVariogram`:
 
 ```{julia}
-γ = Variography.fit(SphericalVariogram, g)
+γ = GeoStatsFunctions.fit(SphericalVariogram, g)
 ```
 
 ```{julia}

12-mining.qmd

@@ -314,7 +314,7 @@ ns = setdiff(names(samples), ["geometry"])
 
 gs = [EmpiricalVariogram(samples, n, estimator = :cressie) for n in ns]
 
-γs = [Variography.fit(Variogram, g, h -> exp(-h/100)) for g in gs]
+γs = [GeoStatsFunctions.fit(Variogram, g, h -> exp(-h/100)) for g in gs]
 ```
 
 ::: {.callout-note}