CVarE/DESCRIPTION

@@ -1,22 +1,17 @@
 Package: CVarE
 Type: Package
 Title: Conditional Variance Estimator for Sufficient Dimension Reduction
-Version: 1.1
-Date: 2021-03-09
+Version: 1.0
+Date: 2021-03-05
 Maintainer: Daniel Kapla <daniel@kapla.at>
 Author: Daniel Kapla [aut, cph, cre],
   Lukas Fertl [aut, cph],
   Efstathia Bura [ctb]
-Authors@R: c(
-  person("Daniel", "Kapla", role = c("aut", "cph", "cre"), email = "daniel@kapla.at"),
-  person("Lukas", "Fertl", role = c("aut", "cph")),
-  person("Efstathia", "Bura", role = "ctb")
-  )
-Description: Implementation of the CVE (Conditional Variance Estimation) method
-  proposed by Fertl, L. and Bura, E. (2021) <arXiv:2102.08782> and the ECVE
-  (Ensemble Conditional Variance Estimation) method introduced in
-  Fertl, L. and Bura, E. (2021) <arXiv:2102.13435>.
-  CVE and ECVE are sufficient dimension reduction methods
+Description: Implementation of the Conditional Variance Estimation (CVE)
+  Fertl and Bura (2021) <arXiv:2102.08782> and the Ensemble Conditional Variance
+  Estimation (ECVE) Fertl and Bura (2021) <arXiv:2102.13435> method.
+
+  CVE and ECVE are sufficient dimension reduction (SDR) methods
   in regressions with continuous response and predictors. CVE applies to general
   additive error regression models while ECVE generalizes to non-additive error
   regression models. They operate under the assumption that the predictors can
@@ -29,5 +24,5 @@ Contact: <daniel@kapla.at>
 URL: https://git.art-ist.cc/daniel/CVE
 Encoding: UTF-8
 NeedsCompilation: yes
-Imports: stats, mda
+Imports: stats,mda
 RoxygenNote: 7.0.2

CVarE/R/CVE.R

@@ -37,13 +37,13 @@
 #' @author Daniel Kapla, Lukas Fertl, Bura Efstathia
 #'
 #' @references
-#'    [1] Fertl, L. and Bura, E. (2021) "Conditional Variance
-#'          Estimation for Sufficient Dimension Reduction"
-#'          <arXiv:2102.08782>
+#'    [1] Fertl, L. and Bura, E. (2021), Conditional Variance
+#'          Estimation for Sufficient Dimension Reduction.
+#'          arXiv:2102.08782
 #'
-#'    [2] Fertl, L. and Bura, E. (2021) "Ensemble Conditional Variance
-#'          Estimation for Sufficient Dimension Reduction"
-#'          <arXiv:2102.13435>
+#'    [2] Fertl, L. and Bura, E. (2021), Ensemble Conditional Variance
+#'          Estimation for Sufficient Dimension Reduction.
+#'          arXiv:2102.13435
 #'
 #' @docType package
 #' @useDynLib CVarE, .registration = TRUE
@@ -200,13 +200,13 @@
 #'      \code{\link{formula}}.
 #'
 #' @references
-#'    [1] Fertl, L. and Bura, E. (2021) "Conditional Variance
-#'          Estimation for Sufficient Dimension Reduction"
-#'          <arXiv:2102.08782>
+#'    [1] Fertl, L. and Bura, E. (2021), Conditional Variance
+#'          Estimation for Sufficient Dimension Reduction.
+#'          arXiv:2102.08782
 #'
-#'    [2] Fertl, L. and Bura, E. (2021) "Ensemble Conditional Variance
-#'          Estimation for Sufficient Dimension Reduction"
-#'          <arXiv:2102.13435>
+#'    [2] Fertl, L. and Bura, E. (2021), Ensemble Conditional Variance
+#'          Estimation for Sufficient Dimension Reduction.
+#'          arXiv:2102.13435
 #'
 #' @importFrom stats model.frame
 #' @export

CVarE/R/datasets.R

@@ -10,9 +10,10 @@
 #' @return a \eqn{n\times p}{n x p} matrix with samples in its rows.
 #'
 #' @examples
-#' CVarE:::rmvnorm(20, sigma = matrix(c(2, 1, 1, 2), 2))
-#' CVarE:::rmvnorm(20, mu = c(3, -1, 2))
-#'
+#' \dontrun{
+#' rmvnorm(20, sigma = matrix(c(2, 1, 1, 2), 2))
+#' rmvnorm(20, mu = c(3, -1, 2))
+#' }
 #' @keywords internal
 rmvnorm <- function(n = 1, mu = rep(0, p), sigma = diag(p)) {
     if (!missing(sigma)) {
@@ -24,10 +25,11 @@ rmvnorm <- function(n = 1, mu = rep(0, p), sigma = diag(p)) {
         stop("At least one of 'mu' or 'sigma' must be supplied.")
     }
 
+    # See: https://en.wikipedia.org/wiki/Multivariate_normal_distribution
     return(rep(mu, each = n) + matrix(rnorm(n * p), n) %*% chol(sigma))
 }
 
-#' Multivariate t Distribution.
+#' Multivariate t distribution.
 #'
 #' Random generation from multivariate t distribution (student distribution).
 #'
@@ -42,10 +44,11 @@ rmvnorm <- function(n = 1, mu = rep(0, p), sigma = diag(p)) {
 #' @return a \eqn{n\times p}{n x p} matrix with samples in its rows.
 #'
 #' @examples
-#' CVarE:::rmvt(20, c(0, 1), matrix(c(3, 1, 1, 2), 2), 3)
-#' CVarE:::rmvt(20, sigma = matrix(c(2, 1, 1, 2), 2), df = 3)
-#' CVarE:::rmvt(20, mu = c(3, -1, 2), df = 3)
-#'
+#' \dontrun{
+#' rmvt(20, c(0, 1), matrix(c(3, 1, 1, 2), 2), 3)
+#' rmvt(20, sigma = matrix(c(2, 1, 1, 2), 2), 3)
+#' rmvt(20, mu = c(3, -1, 2), 3)
+#' }
 #' @keywords internal
 rmvt <- function(n = 1, mu = rep(0, p), sigma = diag(p), df = Inf) {
     if (!missing(sigma)) {
@@ -77,6 +80,7 @@ rmvt <- function(n = 1, mu = rep(0, p), sigma = diag(p), df = Inf) {
 #'
 #' @return numeric array of length \eqn{n}.
 #'
+#' @seealso https://en.wikipedia.org/wiki/Generalized_normal_distribution
 #' @keywords internal
 rgnorm <- function(n = 1, mu = 0, alpha = 1, beta = 1) {
     if (alpha <= 0 | beta <= 0) {
@@ -97,6 +101,7 @@ rgnorm <- function(n = 1, mu = 0, alpha = 1, beta = 1) {
 #'
 #' @return numeric array of length \eqn{n}.
 #'
+#' @seealso https://en.wikipedia.org/wiki/Laplace_distribution
 #' @keywords internal
 rlaplace <- function(n = 1, mu = 0, sd = 1) {
     U <- runif(n, -0.5, 0.5)
@@ -196,9 +201,9 @@ rlaplace <- function(n = 1, mu = 0, sd = 1) {
 #' \eqn{Var(\epsilon) = 0.25}.
 #'
 #' @references
-#'    Fertl, L. and Bura, E. (2021) "Conditional Variance
-#'      Estimation for Sufficient Dimension Reduction"
-#'      <arXiv:2102.08782>
+#'    Fertl, L. and Bura, E. (2021), Conditional Variance
+#'          Estimation for Sufficient Dimension Reduction.
+#'          arXiv:2102.08782
 #'
 #' @import stats
 #' @importFrom stats rnorm rbinom

CVarE/R/summary.R

@@ -6,14 +6,12 @@
 #'          \code{\link{cve}} or \code{\link{cve.call}}.
 #' @param ... ignored.
 #'
-#' @return No return value, prints human readable summary.
-#'
 #' @examples
 #' # create B for simulation
 #' B <- rep(1, 5) / sqrt(5)
 #'
 #' set.seed(21)
-#' # create predictor data x ~ N(0, I_p)
+#' #creat predictor data x ~ N(0, I_p)
 #' x <- matrix(rnorm(500), 100)
 #'
 #' # simulate response variable
@@ -21,7 +19,7 @@
 #' # with f(x1) = x1 and err ~ N(0, 0.25^2)
 #' y <- x %*% B + 0.25 * rnorm(100)
 #' 
-#' # calculate cve for unknown reduction dimension.
+#' # calculate cve for unknown k between min.dim and max.dim.
 #' cve.obj.simple <- cve(y ~ x)
 #' 
 #' summary(cve.obj.simple)

CVarE/man/CVarE-package.Rd

@@ -42,13 +42,13 @@ reduction estimation method that is shown to be consistent under mild
 assumptions.
 }
 \references{
-[1] Fertl, L. and Bura, E. (2021) "Conditional Variance
-         Estimation for Sufficient Dimension Reduction"
-         <arXiv:2102.08782>
+[1] Fertl, L. and Bura, E. (2021), Conditional Variance
+         Estimation for Sufficient Dimension Reduction.
+         arXiv:2102.08782
 
-   [2] Fertl, L. and Bura, E. (2021) "Ensemble Conditional Variance
-         Estimation for Sufficient Dimension Reduction"
-         <arXiv:2102.13435>
+   [2] Fertl, L. and Bura, E. (2021), Ensemble Conditional Variance
+         Estimation for Sufficient Dimension Reduction.
+         arXiv:2102.13435
 }
 \seealso{
 Useful links:

CVarE/man/cve.Rd

@@ -159,13 +159,13 @@ norm(PB - PB.w, type = 'F')
 
 }
 \references{
-[1] Fertl, L. and Bura, E. (2021) "Conditional Variance
-         Estimation for Sufficient Dimension Reduction"
-         <arXiv:2102.08782>
+[1] Fertl, L. and Bura, E. (2021), Conditional Variance
+         Estimation for Sufficient Dimension Reduction.
+         arXiv:2102.08782
 
-   [2] Fertl, L. and Bura, E. (2021) "Ensemble Conditional Variance
-         Estimation for Sufficient Dimension Reduction"
-         <arXiv:2102.13435>
+   [2] Fertl, L. and Bura, E. (2021), Ensemble Conditional Variance
+         Estimation for Sufficient Dimension Reduction.
+         arXiv:2102.13435
 }
 \seealso{
 For a detailed description of \code{formula} see

CVarE/man/cve.call.Rd

@@ -192,11 +192,11 @@ coef(cve.obj.simple1, k = 1)
 coef(cve.obj.simple2, k = 1)
 }
 \references{
-[1] Fertl, L. and Bura, E. (2021) "Conditional Variance
-         Estimation for Sufficient Dimension Reduction"
-         <arXiv:2102.08782>
+[1] Fertl, L. and Bura, E. (2021), Conditional Variance
+         Estimation for Sufficient Dimension Reduction.
+         arXiv:2102.08782
 
-   [2] Fertl, L. and Bura, E. (2021) "Ensemble Conditional Variance
-         Estimation for Sufficient Dimension Reduction"
-         <arXiv:2102.13435>
+   [2] Fertl, L. and Bura, E. (2021), Ensemble Conditional Variance
+         Estimation for Sufficient Dimension Reduction.
+         arXiv:2102.13435
 }

CVarE/man/dataset.Rd

@@ -122,7 +122,7 @@ location 0, shape-parameter 1, and the scale-parameter is chosen such that
 }
 
 \references{
-Fertl, L. and Bura, E. (2021) "Conditional Variance
-     Estimation for Sufficient Dimension Reduction"
-     <arXiv:2102.08782>
+Fertl, L. and Bura, E. (2021), Conditional Variance
+         Estimation for Sufficient Dimension Reduction.
+         arXiv:2102.08782
 }

CVarE/man/rgnorm.Rd

@@ -21,4 +21,7 @@ numeric array of length \eqn{n}.
 \description{
 Random generation for generalized Normal Distribution.
 }
+\seealso{
+https://en.wikipedia.org/wiki/Generalized_normal_distribution
+}
 \keyword{internal}

CVarE/man/rlaplace.Rd

@@ -19,4 +19,7 @@ numeric array of length \eqn{n}.
 \description{
 Random generation for Laplace distribution.
 }
+\seealso{
+https://en.wikipedia.org/wiki/Laplace_distribution
+}
 \keyword{internal}

CVarE/man/rmvnorm.Rd

@@ -21,8 +21,9 @@ Random generation for the multivariate normal distribution.
 \deqn{X \sim N_p(\mu, \Sigma)}{X ~ N_p(\mu, \Sigma)}
 }
 \examples{
-CVarE:::rmvnorm(20, sigma = matrix(c(2, 1, 1, 2), 2))
-CVarE:::rmvnorm(20, mu = c(3, -1, 2))
-
+\dontrun{
+rmvnorm(20, sigma = matrix(c(2, 1, 1, 2), 2))
+rmvnorm(20, mu = c(3, -1, 2))
+}
 }
 \keyword{internal}

CVarE/man/rmvt.Rd

@@ -2,7 +2,7 @@
 % Please edit documentation in R/datasets.R
 \name{rmvt}
 \alias{rmvt}
-\title{Multivariate t Distribution.}
+\title{Multivariate t distribution.}
 \usage{
 rmvt(n = 1, mu = rep(0, p), sigma = diag(p), df = Inf)
 }
@@ -25,9 +25,10 @@ a \eqn{n\times p}{n x p} matrix with samples in its rows.
 Random generation from multivariate t distribution (student distribution).
 }
 \examples{
-CVarE:::rmvt(20, c(0, 1), matrix(c(3, 1, 1, 2), 2), 3)
-CVarE:::rmvt(20, sigma = matrix(c(2, 1, 1, 2), 2), df = 3)
-CVarE:::rmvt(20, mu = c(3, -1, 2), df = 3)
-
+\dontrun{
+rmvt(20, c(0, 1), matrix(c(3, 1, 1, 2), 2), 3)
+rmvt(20, sigma = matrix(c(2, 1, 1, 2), 2), 3)
+rmvt(20, mu = c(3, -1, 2), 3)
+}
 }
 \keyword{internal}

CVarE/man/summary.cve.Rd

@@ -12,9 +12,6 @@
 
 \item{...}{ignored.}
 }
-\value{
-No return value, prints human readable summary.
-}
 \description{
 Prints a summary statistics of the \code{L} component of a \code{cve} object #' for \code{k = min.dim, ..., max.dim}.
 }
@@ -23,7 +20,7 @@ Prints a summary statistics of the \code{L} component of a \code{cve} object #'
 B <- rep(1, 5) / sqrt(5)
 
 set.seed(21)
-# create predictor data x ~ N(0, I_p)
+#creat predictor data x ~ N(0, I_p)
 x <- matrix(rnorm(500), 100)
 
 # simulate response variable
@@ -31,7 +28,7 @@ x <- matrix(rnorm(500), 100)
 # with f(x1) = x1 and err ~ N(0, 0.25^2)
 y <- x \%*\% B + 0.25 * rnorm(100)
 
-# calculate cve for unknown reduction dimension.
+# calculate cve for unknown k between min.dim and max.dim.
 cve.obj.simple <- cve(y ~ x)
 
 summary(cve.obj.simple)