diff --git a/README.Rmd b/README.Rmd
index 6ff6aca2461928749c93e49f52e17b290c670ea3..579ed0dad1520c55314a8fc0ccb02e617008401d 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -59,14 +59,14 @@ From this point on, what you do depends on which model do you have in mind. Here
 
 A structural analysis can be obtained in the following steps
 ```{r structural}
-## empirical variogram
+## empirical structural function
 vge = variogram(gsm)
 
 ## model specification
 vm = gstat::vgm(model="Sph", range=25, nugget=1, psill=1)
 # you can use gstat specifications!
 
-## variogram fit
+## model fitting
 gsm.f = fit_lmc(v = vge, g = gsm, model = vm)
 
 ## plot
@@ -76,7 +76,9 @@ variogramModelPlot(vge, model = gsm.f)
 This model can then be validated, interpolated and/or simulated. The workflow for each of these tasks is always:
 
 1.- define some method parameters with a tailored function, e.g. `LeaveOneOut()` for validation, `KrigingNeighbourhood()` for cokriging or `SequentialSimulation()` for sequential Gaussian Simulation
+
 2.- if desired, define some new locations where to interpolate or simulate, using `expand.grid()` or `sp::GridTopology()` or similar
+
 3.- call an appropriate function, specifying the model, potential new data, and the parameters created in the preceding steps; e.g. `validate(model, pars)` for validation, or `predict(model, newdata, pars)` for interpolation or validation
 
 More information can be found in [./vignettes/gmGeostats.html](vignette).
\ No newline at end of file
diff --git a/README.md b/README.md
index 362d77f8b741a689353c65cd6c7f559112dda4ca..0cfb7d3cf1502b07f4015110884131a3379bc412 100644
--- a/README.md
+++ b/README.md
@@ -72,14 +72,14 @@ model. See [./vignettes/gmGeostats.html](vignette) for details.
 A structural analysis can be obtained in the following steps
 
 ``` r
-## empirical variogram
+## empirical structural function
 vge = variogram(gsm)
 
 ## model specification
 vm = gstat::vgm(model="Sph", range=25, nugget=1, psill=1)
 # you can use gstat specifications!
 
-## variogram fit
+## model fitting
 gsm.f = fit_lmc(v = vge, g = gsm, model = vm)
 
 ## plot
@@ -93,13 +93,15 @@ workflow for each of these tasks is always:
 
 1.- define some method parameters with a tailored function, e.g.
 `LeaveOneOut()` for validation, `KrigingNeighbourhood()` for cokriging
-or `SequentialSimulation()` for sequential Gaussian Simulation 2.- if
-desired, define some new locations where to interpolate or simulate,
-using `expand.grid()` or `sp::GridTopology()` or similar 3.- call an
-appropriate function, specifying the model, potential new data, and the
-parameters created in the preceding steps; e.g. `validate(model, pars)`
-for validation, or `predict(model, newdata, pars)` for interpolation or
-validation
+or `SequentialSimulation()` for sequential Gaussian Simulation
+
+2.- if desired, define some new locations where to interpolate or
+simulate, using `expand.grid()` or `sp::GridTopology()` or similar
+
+3.- call an appropriate function, specifying the model, potential new
+data, and the parameters created in the preceding steps; e.g.
+`validate(model, pars)` for validation, or `predict(model, newdata,
+pars)` for interpolation or validation
 
 More information can be found in
 [./vignettes/gmGeostats.html](vignette).