R/RcppExports.R

Defines functions FC_LocalSimple_mean3_stderr SP_Summaries_welch_rect_centroid SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1 SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1 SB_MotifThree_quantile_hh SB_BinaryStats_diff_longstretch0 SP_Summaries_welch_rect_area_5_1 DN_OutlierInclude_n_001_mdrmd DN_OutlierInclude_p_001_mdrmd FC_LocalSimple_mean1_tauresrat IN_AutoMutualInfoStats_40_gaussian_fmmi CO_Embed2_Dist_tau_d_expfit_meandiff PD_PeriodicityWang_th0_01 SB_TransitionMatrix_3ac_sumdiagcov SB_BinaryStats_mean_longstretch1 MD_hrv_classic_pnn40 CO_trev_1_num CO_HistogramAMI_even_2_5 CO_FirstMin_ac CO_f1ecac DN_HistogramMode_10 DN_HistogramMode_5

Documented in CO_Embed2_Dist_tau_d_expfit_meandiff CO_f1ecac CO_FirstMin_ac CO_HistogramAMI_even_2_5 CO_trev_1_num DN_HistogramMode_10 DN_HistogramMode_5 DN_OutlierInclude_n_001_mdrmd DN_OutlierInclude_p_001_mdrmd FC_LocalSimple_mean1_tauresrat FC_LocalSimple_mean3_stderr IN_AutoMutualInfoStats_40_gaussian_fmmi MD_hrv_classic_pnn40 PD_PeriodicityWang_th0_01 SB_BinaryStats_diff_longstretch0 SB_BinaryStats_mean_longstretch1 SB_MotifThree_quantile_hh SB_TransitionMatrix_3ac_sumdiagcov SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1 SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1 SP_Summaries_welch_rect_area_5_1 SP_Summaries_welch_rect_centroid

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

#' Function to calculate a statistical feature
#'
#' @param x a numerical time-series input vector
#' @return scalar value that denotes the calculated time-series statistic
#' @author Carl H. Lubba
#' @export
#' @examples
#' x <- 1 + 0.5 * 1:1000 + arima.sim(list(ma = 0.5), n = 1000)
#' outs <- DN_HistogramMode_5(x)
#'
DN_HistogramMode_5 <- function(x) {
    .Call('_Rcatch22_DN_HistogramMode_5', PACKAGE = 'Rcatch22', x)
}

#' Function to calculate a statistical feature
#'
#' @param x a numerical time-series input vector
#' @return scalar value that denotes the calculated time-series statistic
#' @author Carl H. Lubba
#' @export
#' @examples
#' x <- 1 + 0.5 * 1:1000 + arima.sim(list(ma = 0.5), n = 1000)
#' outs <- DN_HistogramMode_10(x)
#'
DN_HistogramMode_10 <- function(x) {
    .Call('_Rcatch22_DN_HistogramMode_10', PACKAGE = 'Rcatch22', x)
}

#' Function to calculate a statistical feature
#'
#' @param x a numerical time-series input vector
#' @return scalar value that denotes the calculated time-series statistic
#' @author Carl H. Lubba
#' @export
#' @examples
#' x <- 1 + 0.5 * 1:1000 + arima.sim(list(ma = 0.5), n = 1000)
#' outs <- CO_f1ecac(x)
#'
CO_f1ecac <- function(x) {
    .Call('_Rcatch22_CO_f1ecac', PACKAGE = 'Rcatch22', x)
}

#' Function to calculate a statistical feature
#'
#' @param x a numerical time-series input vector
#' @return scalar value that denotes the calculated time-series statistic
#' @author Carl H. Lubba
#' @export
#' @examples
#' x <- 1 + 0.5 * 1:1000 + arima.sim(list(ma = 0.5), n = 1000)
#' outs <- CO_FirstMin_ac(x)
#'
CO_FirstMin_ac <- function(x) {
    .Call('_Rcatch22_CO_FirstMin_ac', PACKAGE = 'Rcatch22', x)
}

#' Function to calculate a statistical feature
#'
#' @param x a numerical time-series input vector
#' @return scalar value that denotes the calculated time-series statistic
#' @author Carl H. Lubba
#' @export
#' @examples
#' x <- 1 + 0.5 * 1:1000 + arima.sim(list(ma = 0.5), n = 1000)
#' outs <- CO_HistogramAMI_even_2_5(x)
#'
CO_HistogramAMI_even_2_5 <- function(x) {
    .Call('_Rcatch22_CO_HistogramAMI_even_2_5', PACKAGE = 'Rcatch22', x)
}

#' Function to calculate a statistical feature
#'
#' @param x a numerical time-series input vector
#' @return scalar value that denotes the calculated time-series statistic
#' @author Carl H. Lubba
#' @export
#' @examples
#' x <- 1 + 0.5 * 1:1000 + arima.sim(list(ma = 0.5), n = 1000)
#' outs <- CO_trev_1_num(x)
#'
CO_trev_1_num <- function(x) {
    .Call('_Rcatch22_CO_trev_1_num', PACKAGE = 'Rcatch22', x)
}

#' Function to calculate a statistical feature
#'
#' @param x a numerical time-series input vector
#' @return scalar value that denotes the calculated time-series statistic
#' @author Carl H. Lubba
#' @export
#' @examples
#' x <- 1 + 0.5 * 1:1000 + arima.sim(list(ma = 0.5), n = 1000)
#' outs <- MD_hrv_classic_pnn40(x)
#'
MD_hrv_classic_pnn40 <- function(x) {
    .Call('_Rcatch22_MD_hrv_classic_pnn40', PACKAGE = 'Rcatch22', x)
}

#' Function to calculate a statistical feature
#'
#' @param x a numerical time-series input vector
#' @return scalar value that denotes the calculated time-series statistic
#' @author Carl H. Lubba
#' @export
#' @examples
#' x <- 1 + 0.5 * 1:1000 + arima.sim(list(ma = 0.5), n = 1000)
#' outs <- SB_BinaryStats_mean_longstretch1(x)
#'
SB_BinaryStats_mean_longstretch1 <- function(x) {
    .Call('_Rcatch22_SB_BinaryStats_mean_longstretch1', PACKAGE = 'Rcatch22', x)
}

#' Function to calculate a statistical feature
#'
#' @param x a numerical time-series input vector
#' @return scalar value that denotes the calculated time-series statistic
#' @author Carl H. Lubba
#' @export
#' @examples
#' x <- 1 + 0.5 * 1:1000 + arima.sim(list(ma = 0.5), n = 1000)
#' outs <- SB_TransitionMatrix_3ac_sumdiagcov(x)
#'
SB_TransitionMatrix_3ac_sumdiagcov <- function(x) {
    .Call('_Rcatch22_SB_TransitionMatrix_3ac_sumdiagcov', PACKAGE = 'Rcatch22', x)
}

#' Function to calculate a statistical feature
#'
#' @param x a numerical time-series input vector
#' @return scalar value that denotes the calculated time-series statistic
#' @author Carl H. Lubba
#' @export
#' @examples
#' x <- 1 + 0.5 * 1:1000 + arima.sim(list(ma = 0.5), n = 1000)
#' outs <- PD_PeriodicityWang_th0_01(x)
#'
PD_PeriodicityWang_th0_01 <- function(x) {
    .Call('_Rcatch22_PD_PeriodicityWang_th0_01', PACKAGE = 'Rcatch22', x)
}

#' Function to calculate a statistical feature
#'
#' @param x a numerical time-series input vector
#' @return scalar value that denotes the calculated time-series statistic
#' @author Carl H. Lubba
#' @export
#' @examples
#' x <- 1 + 0.5 * 1:1000 + arima.sim(list(ma = 0.5), n = 1000)
#' outs <- CO_Embed2_Dist_tau_d_expfit_meandiff(x)
#'
CO_Embed2_Dist_tau_d_expfit_meandiff <- function(x) {
    .Call('_Rcatch22_CO_Embed2_Dist_tau_d_expfit_meandiff', PACKAGE = 'Rcatch22', x)
}

#' Function to calculate a statistical feature
#'
#' @param x a numerical time-series input vector
#' @return scalar value that denotes the calculated time-series statistic
#' @author Carl H. Lubba
#' @export
#' @examples
#' x <- 1 + 0.5 * 1:1000 + arima.sim(list(ma = 0.5), n = 1000)
#' outs <- IN_AutoMutualInfoStats_40_gaussian_fmmi(x)
#'
IN_AutoMutualInfoStats_40_gaussian_fmmi <- function(x) {
    .Call('_Rcatch22_IN_AutoMutualInfoStats_40_gaussian_fmmi', PACKAGE = 'Rcatch22', x)
}

#' Function to calculate a statistical feature
#'
#' @param x a numerical time-series input vector
#' @return scalar value that denotes the calculated time-series statistic
#' @author Carl H. Lubba
#' @export
#' @examples
#' x <- 1 + 0.5 * 1:1000 + arima.sim(list(ma = 0.5), n = 1000)
#' outs <- FC_LocalSimple_mean1_tauresrat(x)
#'
FC_LocalSimple_mean1_tauresrat <- function(x) {
    .Call('_Rcatch22_FC_LocalSimple_mean1_tauresrat', PACKAGE = 'Rcatch22', x)
}

#' Function to calculate a statistical feature
#'
#' @param x a numerical time-series input vector
#' @return scalar value that denotes the calculated time-series statistic
#' @author Carl H. Lubba
#' @export
#' @examples
#' x <- 1 + 0.5 * 1:1000 + arima.sim(list(ma = 0.5), n = 1000)
#' outs <- DN_OutlierInclude_p_001_mdrmd(x)
#'
DN_OutlierInclude_p_001_mdrmd <- function(x) {
    .Call('_Rcatch22_DN_OutlierInclude_p_001_mdrmd', PACKAGE = 'Rcatch22', x)
}

#' Function to calculate a statistical feature
#'
#' @param x a numerical time-series input vector
#' @return scalar value that denotes the calculated time-series statistic
#' @author Carl H. Lubba
#' @export
#' @examples
#' x <- 1 + 0.5 * 1:1000 + arima.sim(list(ma = 0.5), n = 1000)
#' outs <- DN_OutlierInclude_n_001_mdrmd(x)
#'
DN_OutlierInclude_n_001_mdrmd <- function(x) {
    .Call('_Rcatch22_DN_OutlierInclude_n_001_mdrmd', PACKAGE = 'Rcatch22', x)
}

#' Function to calculate a statistical feature
#'
#' @param x a numerical time-series input vector
#' @return scalar value that denotes the calculated time-series statistic
#' @author Carl H. Lubba
#' @export
#' @examples
#' x <- 1 + 0.5 * 1:1000 + arima.sim(list(ma = 0.5), n = 1000)
#' outs <- SP_Summaries_welch_rect_area_5_1(x)
#'
SP_Summaries_welch_rect_area_5_1 <- function(x) {
    .Call('_Rcatch22_SP_Summaries_welch_rect_area_5_1', PACKAGE = 'Rcatch22', x)
}

#' Function to calculate a statistical feature
#'
#' @param x a numerical time-series input vector
#' @return scalar value that denotes the calculated time-series statistic
#' @author Carl H. Lubba
#' @export
#' @examples
#' x <- 1 + 0.5 * 1:1000 + arima.sim(list(ma = 0.5), n = 1000)
#' outs <- SB_BinaryStats_diff_longstretch0(x)
#'
SB_BinaryStats_diff_longstretch0 <- function(x) {
    .Call('_Rcatch22_SB_BinaryStats_diff_longstretch0', PACKAGE = 'Rcatch22', x)
}

#' Function to calculate a statistical feature
#'
#' @param x a numerical time-series input vector
#' @return scalar value that denotes the calculated time-series statistic
#' @author Carl H. Lubba
#' @export
#' @examples
#' x <- 1 + 0.5 * 1:1000 + arima.sim(list(ma = 0.5), n = 1000)
#' outs <- SB_MotifThree_quantile_hh(x)
#'
SB_MotifThree_quantile_hh <- function(x) {
    .Call('_Rcatch22_SB_MotifThree_quantile_hh', PACKAGE = 'Rcatch22', x)
}

#' Function to calculate a statistical feature
#'
#' @param x a numerical time-series input vector
#' @return scalar value that denotes the calculated time-series statistic
#' @author Carl H. Lubba
#' @export
#' @examples
#' x <- 1 + 0.5 * 1:1000 + arima.sim(list(ma = 0.5), n = 1000)
#' outs <- SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1(x)
#'
SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1 <- function(x) {
    .Call('_Rcatch22_SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1', PACKAGE = 'Rcatch22', x)
}

#' Function to calculate a statistical feature
#'
#' @param x a numerical time-series input vector
#' @return scalar value that denotes the calculated time-series statistic
#' @author Carl H. Lubba
#' @export
#' @examples
#' x <- 1 + 0.5 * 1:1000 + arima.sim(list(ma = 0.5), n = 1000)
#' outs <- SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1(x)
#'
SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1 <- function(x) {
    .Call('_Rcatch22_SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1', PACKAGE = 'Rcatch22', x)
}

#' Function to calculate a statistical feature
#'
#' @param x a numerical time-series input vector
#' @return scalar value that denotes the calculated time-series statistic
#' @author Carl H. Lubba
#' @export
#' @examples
#' x <- 1 + 0.5 * 1:1000 + arima.sim(list(ma = 0.5), n = 1000)
#' outs <- SP_Summaries_welch_rect_centroid(x)
#'
SP_Summaries_welch_rect_centroid <- function(x) {
    .Call('_Rcatch22_SP_Summaries_welch_rect_centroid', PACKAGE = 'Rcatch22', x)
}

#' Function to calculate a statistical feature
#'
#' @param x a numerical time-series input vector
#' @return scalar value that denotes the calculated time-series statistic
#' @author Carl H. Lubba
#' @export
#' @examples
#' x <- 1 + 0.5 * 1:1000 + arima.sim(list(ma = 0.5), n = 1000)
#' outs <- FC_LocalSimple_mean3_stderr(x)
#'
FC_LocalSimple_mean3_stderr <- function(x) {
    .Call('_Rcatch22_FC_LocalSimple_mean3_stderr', PACKAGE = 'Rcatch22', x)
}

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Rcatch22 documentation built on May 31, 2021, 9:09 a.m.