spell check

NEWS.md

@@ -33,7 +33,7 @@ The package now provides some support for spatial regression with raster data, i
  1. `slim = TRUE` The model fitting functions (`stan_glm`, `stan_car`, `stan_sar`, `stan_esf`, `stan_icar`) now provide the option to trim down the parameters for which MCMC samples are collected. For large N and/or many N-length vectors of parameters, this option can speed up sampling considerably and reduce memory usage. The new `drop` argument provides users control over which parameter vectors will be ignored. This functionality may be helpful for any number of purposes, including modeling large data sets, measurement error models, and Monte Carlo studies.
  2. `prep_sar_data2` and `prep_car_data2` These two functions can quickly prepare required data for SAR and CAR models when using raster layers (observations on a regularly spaced grid). The standard and more generally applicable functions `prep_car_data` and `prep_sar_data` are limited in terms of the size of spatial weights matrices they can handle.
 
-These new functions are dicussed in a new vignette titled "Raster regression."
+These new functions are discussed in a new vignette titled "Raster regression."
 
 ## Minor changes
 

R/raster-analysis.R

@@ -45,7 +45,7 @@ eigen_grid <- function(row = 5, col = 5) {
 #'
 #' Prepare input data for the CAR model when your dataset consists of observations on a regular (rectangular) tessellation, such as a raster layer or remotely sensed imagery. The rook criteria is used to determine adjacency. This function uses Equation 5 from Griffith (2000) to generate approximate eigenvalues for a row-standardized spatial weights matrix from a P-by-Q dimension regular tessellation.
 #'
-#' This function can accomodate very large numbers of observations for use with \code{\link[geostan]{stan_car}}; for large N data, it is also recommended to use `slim = TRUE` or the `drop` argument. For more details, see: \code{vignette("raster-regression", package = "geostan")}.
+#' This function can accommodate very large numbers of observations for use with \code{\link[geostan]{stan_car}}; for large N data, it is also recommended to use `slim = TRUE` or the `drop` argument. For more details, see: \code{vignette("raster-regression", package = "geostan")}.
 #' 
 #' @source
 #'
@@ -114,7 +114,7 @@ prep_car_data2 <- function(row = 100, col = 100) {
 #'
 #' Prepare data for the SAR model when your raw dataset consists of observations on a regular tessellation, such as a raster layer or remotely sensed imagery. The rook criteria is used to determine adjacency. This function uses Equation 5 from Griffith (2000) to calculate the eigenvalues for a row-standardized spatial weights matrix of a P-by-Q dimension regular tessellation.
 #'
-#' This function can accomodate very large numbers of observations for use with \code{\link[geostan]{stan_sar}}; for large N data, it is also recommended to use `slim = TRUE` or the `drop` argument. For details, see: \code{vignette("raster-regression", package = "geostan")}.
+#' This function can accommodate very large numbers of observations for use with \code{\link[geostan]{stan_sar}}; for large N data, it is also recommended to use `slim = TRUE` or the `drop` argument. For details, see: \code{vignette("raster-regression", package = "geostan")}.
 #'
 #' @seealso
 #' \code{\link[geostan]{prep_car_data2}}, \code{\link[geostan]{prep_sar_data}}, \code{\link[geostan]{stan_sar}}.