fix typos

NEWS.md

@@ -1,9 +1,9 @@
 # Changes in mirt 1.29
 
-- `mdirt()` gains a `item.Q` list to specify Q-matricies at the item-category level for each item
+- `mdirt()` gains a `item.Q` list to specify Q-matrices at the item-category level for each item
 
-- `createItem()` functions gain an optional argument to the function defintions to allow for
-  list-specified data from functions such as `mirt()` via silenent `mirt(..., customItemsData)`
+- `createItem()` functions gain an optional argument to the function definitions to allow for
+  list-specified data from functions such as `mirt()` via a silent `mirt(..., customItemsData)`
   argument
 
 - lattice `auto.key` default now reports lines rather than points. This is now more consistent
@@ -15,7 +15,7 @@
   for IRT model fitted within the multiple-group estimation framework
 
 - `structure` argument added to `mdirt()` function to allow log-linear models for 
-  simplifiying the profile probability model computations
+  simplifying the profile probability model computations
 
 - fix `survey.weights` input for multiple group models (reported by Leigh Allison)
 

R/mdirt.R

@@ -52,7 +52,7 @@
 #' @param itemtype a vector indicating the itemtype associated with each item.
 #'   For discrete models this is limited to only 'lca' or items defined using a
 #'   \code{\link{createItem}} definition
-#' @param item.Q a list of item-level Q-matricies indicating how the respective categories should be
+#' @param item.Q a list of item-level Q-matrices indicating how the respective categories should be
 #'   modeled by the underlying attributes. Each matrix must represent a \eqn{K_i \times A} matrix,
 #'   where \eqn{K_i} represents the number of categories for the ith item, and \eqn{A} is the number
 #'   of attributes included in the \code{Theta} matrix; otherwise, a value of\code{NULL} will default
@@ -65,7 +65,7 @@
 #'   because it allows customized patterns of latent classes (i.e., defines the possible combinations
 #'   of the latent attribute profile). The default builds the pattern \code{customTheta = diag(model)},
 #'   which is the typical pattern for the traditional latent class analysis whereby class
-#'   membership mutually distinct and exaustive. See \code{\link{thetaComb}} for a quick method
+#'   membership mutually distinct and exhaustive. See \code{\link{thetaComb}} for a quick method
 #'   to generate a matrix with all possible combinations
 #' @param nruns a numeric value indicating how many times the model should be fit to the data
 #'   when using random starting values. If greater than 1, \code{GenRandomPars} is set to true
@@ -241,7 +241,7 @@
 #' coef(CRUM, simplify=TRUE)
 #' summary(CRUM)
 #'
-#' # good fit, but oversaturated (main effects for items 11-15 can be set to 0)
+#' # good fit, but over-saturated (main effects for items 11-15 can be set to 0)
 #' M2(CRUM)
 #'
 #' #------------------

R/mirt.R

@@ -480,7 +480,7 @@
 #'       but with the extrapolation-interpolation method described by Woods (2007). NOTE: to improve stability
 #'       in the presence of extreme response styles (i.e., all highest or lowest in each item) the \code{technical} option
 #'       \code{zeroExtreme = TRUE} may be required to down-weight the contribution of these problematic patterns
-#'     \item \code{'Davidian-#'} estimates semiparametric Davidian curves described by Woods and Lin (2009),
+#'     \item \code{'Davidian-#'} estimates semi-parametric Davidian curves described by Woods and Lin (2009),
 #'       where the \code{#} placeholder represents the number of Davidian parameters to estimate
 #'       (e.g., \code{'Davidian-6'} will estimate 6 smoothing parameters). By default, the number of
 #'       \code{quadpts} is increased to 121, and this method is only applicable for
@@ -1171,7 +1171,7 @@
 #' dat <- key2binary(SAT12,
 #'                    key = c(1,4,5,2,3,1,2,1,3,1,2,4,2,1,5,3,4,4,1,4,3,3,4,1,3,5,1,3,1,5,4,5))
 #' dav <- mirt(dat, 1, dentype = 'Davidian-4') # use four smoothing parameters
-#' plot(dav, type = 'Davidian') # shape of latent trait disribution
+#' plot(dav, type = 'Davidian') # shape of latent trait distribution
 #' coef(dav, simplify=TRUE)
 #'
 #' fs <- fscores(dav) # assume normal prior