Documentation and version bump

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: singleRcapture
 Type: Package
 Title: Single-Source Capture-Recapture Models
-Version: 0.2.1.3
+Version: 0.2.1.4
 Authors@R:
     c(person(given = "Piotr",
              family = "Chlebicki",

R/documentationFiles.R

@@ -267,6 +267,8 @@ NULL
 #' @param model,object an object of \code{singleRStaticCountData} class.
 #' @param dfbeta if \code{dfbeta} was already obtained it is possible to pass 
 #' them into function so that they need not be computed for the second time.
+#' @param do.coef logical indicating if \code{dfbeta} computation for influence 
+#' should be done. \code{FALSE} by default.
 #' @param cores a number of processor cores to be used,
 #' any number greater than 1 activates code designed with \code{doParallel}, 
 #' \code{foreach} and \code{parallel} packages. Note that for now using parallel 
@@ -291,10 +293,11 @@ NULL
 #' for every unit present in the data.\code{dfpopsize} does the same for 
 #' population size estimation utilizing coefficients computed by \code{dfbeta}.
 #' 
-#' 
 #' \code{cooks.distance} is implemented (for now) only for models with a single
 #' linear predictor and works exactly like the method for \code{glm} class.
 #' 
+#' \code{sigma} computes the standard errors of predicted means. Returns a matrix
+#' with two columns first for truncated mean and the other for the non-truncated mean.
 #' 
 #' \code{residuals.singleRStaticCountData} (can be abbreviated to \code{resid})
 #'  works like \code{residuals.glm} with the exception that:
@@ -353,6 +356,8 @@ NULL
 #' matrix constructed using specifications in \code{controlModel} and 
 #' call to \code{estimatePopsize}.
 #'
+#' \code{influence} works like \code{glm} counterpart computing the most important
+#' influence measures.
 #'
 #' @seealso [estimatePopsize()] [stats::hatvalues()] [controlMethod()] [stats::dfbeta()]
 #' [stats::cooks.distance()]

R/methodsNotWorking.R

@@ -8,7 +8,7 @@ add1.singleRStaticCountData <- function(object, scope, ...) {
 #' @importFrom stats profile
 #' @method profile singleRStaticCountData
 #' @exportS3Method 
-profile.singleRStaticCountData <- function(object, scope, ...) {
+profile.singleRStaticCountData <- function(fitted, ...) {
   stop("The profile method for singleRStaticCountData class doesn't work yet.")
 }
 #' @importFrom stats drop1

R/smallMethods.R

@@ -162,13 +162,15 @@ df.residual.singleRStaticCountData <- function(object, ...) {
 
 #' @importFrom stats sigma
 #' @method sigma singleRStaticCountData
+#' @rdname regDiagSingleR
 #' @exportS3Method 
 sigma.singleRStaticCountData <- function(object, ...) {
   predict(object, type = "mean", se = TRUE)[c(3, 4)]
 }
 
 #' @importFrom stats influence
 #' @method influence singleRStaticCountData
+#' @rdname regDiagSingleR
 #' @exportS3Method 
 influence.singleRStaticCountData <- function(model, do.coef = FALSE, ...) {
   res <- list()
@@ -199,6 +201,7 @@ influence.singleRStaticCountData <- function(model, do.coef = FALSE, ...) {
 
 #' @importFrom stats rstudent
 #' @method rstudent singleRStaticCountData
+#' @rdname regDiagSingleR
 #' @exportS3Method 
 rstudent.singleRStaticCountData <- function(model, ...) {
   res <- residuals(model, type = "pearson")[, 1]
@@ -212,6 +215,7 @@ rstudent.singleRStaticCountData <- function(model, ...) {
 
 #' @importFrom stats rstandard
 #' @method rstandard singleRStaticCountData
+#' @rdname regDiagSingleR
 #' @exportS3Method 
 rstandard.singleRStaticCountData <- function(model,
                                              type = c("deviance", "pearson"),