Prepare release

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: profoc
 Type: Package
 Title: Probabilistic Forecast Combination Using CRPS Learning
-Version: 1.2.2.9000
-Date: 2023-08-28
+Version: 1.3.0
+Date: 2024-01-09
 Authors@R: c(
     person(given = "Jonathan",
              family = "Berrisch",

R/splines.R

@@ -137,7 +137,7 @@ make_basis_mats <- function(x, # Splines basis
 #' @param nonc Beta distribution noncentrality parameter
 #' @param tailw Tailweight
 #' @param deg Degree of splines
-#' @param ndiff Sets the degree of the differencing matrix for creaing
+#' @param ndiff Sets the degree of the differencing matrix for creating
 #' the penalty
 #' @param lambda Penalty parameter (higher values lead to higher penalty)
 #' @param periodic Create periodic penalty

vignettes/class.Rmd

@@ -19,7 +19,7 @@ vignette: >
 All major parts of `online()` are implemented in C++ for speed. Usually, this
 comes at the cost of flexibility. However, the profoc package exposes a C++ 
 class `conline` that allows you to gain fine grained control over objects.  
-`online()` wrapps this class and provides a convenient interface for the most 
+`online()` wraps this class and provides a convenient interface for the most 
 common use cases. However, if you need to alter object initialization (i.e.
 provide custom basis / hat matrices for smoothing) you can use the C++ class
 directly from R. This vignette shows how to do this.
@@ -75,7 +75,7 @@ tau <- 1:P / (P + 1)
 model$tau <- tau
 ```
 
-The experts array is a bit more complicated. C++ expects us to pass a list of arrays. Thereby, the list itself must have dimension `Tx1` and the elements of the list (the arrays) `D x P x K`. For convenience we can use `init_experts_list()` to create such a list from our experts array. Note that we must pass the true observations as well. They are used to detect wether the data is univariate (`T x 1` matrix) or multivariate (`T x D` matrix).
+The experts array is a bit more complicated. C++ expects us to pass a list of arrays. Thereby, the list itself must have dimension `Tx1` and the elements of the list (the arrays) `D x P x K`. For convenience we can use `init_experts_list()` to create such a list from our experts array. Note that we must pass the true observations as well. They are used to detect whether the data is univariate (`T x 1` matrix) or multivariate (`T x D` matrix).
 
 ```{r}
 experts_list <- init_experts_list(experts, y)