R/RcppExports.R

In avar: Allan Variance

# Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393

#' @title Compute Tau-Overlap Allan Variance
#' @description Computation of Tau-Overlap Allan Variance
#' @param x A \code{vector} with dimensions N x 1.
#' @return av A \code{matrix} that contains:
#' \itemize{
#'  \item{Col 1}{The size of the cluster}
#'  \item{Col 2}{The Allan variance}
#'  \item{Col 3}{The error associated with the variance estimation.}
#' }
#' @details
#' Given \eqn{N} equally spaced samples with averaging time \eqn{\tau = n\tau _0}{tau = n*tau_0},
#' where \eqn{n} is an integer such that \eqn{ 1 \le n \le \frac{N}{2}}{1<= n <= N/2}.
#' Therefore, \eqn{n} is able to be selected from \eqn{\left\{ {n|n < \left\lfloor {{{\log }_2}\left( N \right)} \right\rfloor } \right\}}{{n|n< floor(log2(N))}}
#' Then, a sampling of \eqn{m = \left\lfloor {\frac{{N - 1}}{n}} \right\rfloor  - 1} samples exist.
#' The tau-overlap estimator is given by:
#'
#' where \eqn{ {{\bar y}_t}\left( \tau  \right) = \frac{1}{\tau }\sum\limits_{i = 0}^{\tau  - 1} {{{\bar y}_{t - i}}} }.
#'
#' @author JJB
#' @references Long-Memory Processes, the Allan Variance and Wavelets, D. B. Percival and P. Guttorp
#' @examples
#' \donttest{
#' set.seed(999)
#' N = 100000
#' white.noise = rnorm(N, 0, 2)
#' random.walk = cumsum(0.1*rnorm(N, 0, 2))
#' combined.ts = white.noise+random.walk
#' av_mat = avar_to_cpp(combined.ts)
#' }
#' @keywords internal
avar_to_cpp <- function(x) {
    .Call('_avar_avar_to_cpp', PACKAGE = 'avar', x)
}

#' @title Compute Maximal-Overlap Allan Variance using Means
#' @description Computation of Maximal-Overlap Allan Variance
#' @param x A \code{vector} with dimensions N x 1.
#' @return av A \code{list} that contains:
#' \itemize{
#'  \item{"clusters"}{The size of the cluster}
#'  \item{"allan"}{The Allan variance}
#'  \item{"errors"}{The error associated with the variance estimation.}
#' }
#' @details
#' Given \eqn{N} equally spaced samples with averaging time \eqn{\tau = n\tau _0}{tau = n*tau_0},
#' where \eqn{n} is an integer such that \eqn{ 1 \le n \le \frac{N}{2}}{1<= n <= N/2}.
#' Therefore, \eqn{n} is able to be selected from \eqn{\left\{ {n|n < \left\lfloor {{{\log }_2}\left( N \right)} \right\rfloor } \right\}}{{n|n< floor(log2(N))}}
#' Then, \eqn{M = N - 2n} samples exist.
#' The Maximal-overlap estimator is given by:
#' \eqn{\frac{1}{{2\left( {N - 2k + 1} \right)}}\sum\limits_{t = 2k}^N {{{\left[ {{{\bar Y}_t}\left( k \right) - {{\bar Y}_{t - k}}\left( k \right)} \right]}^2}} }
#'
#' where \eqn{ {{\bar y}_t}\left( \tau  \right) = \frac{1}{\tau }\sum\limits_{i = 0}^{\tau  - 1} {{{\bar y}_{t - i}}} }.
#' @author JJB
#' @references Long-Memory Processes, the Allan Variance and Wavelets, D. B. Percival and P. Guttorp
#' @examples
#' \donttest{
#' set.seed(999)
#' N = 100000
#' white.noise = rnorm(N, 0, 2)
#' random.walk = cumsum(0.1*rnorm(N, 0, 2))
#' combined.ts = white.noise+random.walk
#' av_mat = avar_mo_cpp(combined.ts)
#' }
#' @keywords internal
avar_mo_cpp <- function(x) {
    .Call('_avar_avar_mo_cpp', PACKAGE = 'avar', x)
}