R/RcppExports.R

In kalmanfilter: Kalman Filter

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

#' R's implementation of the Moore-Penrose pseudo matrix inverse
#'
#' @param m matrix
#' @return matrix inverse of m
Rginv <- function(m) {
    .Call(`_kalmanfilter_Rginv`, m)
}

#' Generalized matrix inverse
#'
#' @param m matrix
#' @return matrix inverse of m
gen_inv <- function(m) {
    .Call(`_kalmanfilter_gen_inv`, m)
}

#' Check if list contains a name
#' 
#' @param s a string name
#' @param L a list object
#' @return boolean
contains <- function(s, L) {
    .Call(`_kalmanfilter_contains`, s, L)
}

#' Kalman Filter
#'
#' @param ssm list describing the state space model, must include names
#' B0 - N_b x 1 matrix, initial guess for the unobserved components 
#' P0 - N_b x N_b matrix, initial guess for the covariance matrix of the unobserved components
#' Dm - N_b x 1 matrix, constant matrix for the state equation
#' Am - N_y x 1 matrix, constant matrix for the observation equation
#' Fm - N_b X p matrix, state transition matrix
#' Hm - N_y x N_b matrix, observation matrix
#' Qm - N_b x N_b matrix, state error covariance matrix
#' Rm - N_y x N_y matrix, state error covariance matrix
#' betaO - N_y x N_o matrix, coefficient matrix for the observation exogenous data
#' betaS - N_b x N_s matrix, coefficient matrix for the state exogenous data
#' @param yt N x T matrix of data
#' @param Xo N_o x T matrix of exogenous observation data
#' @param Xs N_s x T matrix of exogenous state 
#' @param weight column matrix of weights, T x 1
#' @param smooth boolean indication whether to run the backwards smoother
#' @return list of matrices and cubes output by the Kalman filter
#' @examples
#' #Nelson-Siegel dynamic factor yield curve
#' library(kalmanfilter)
#' library(data.table)
#' data(treasuries)
#' tau = unique(treasuries$maturity)
#'
#' #Set up the state space model
#' ssm = list()
#' ssm[["Fm"]] = rbind(c(0.9720, -0.0209, -0.0061), 
#'                     c(0.1009 , 0.8189, -0.1446), 
#'                     c(-0.1226, 0.0192, 0.8808))
#' ssm[["Dm"]] = matrix(c(0.1234, -0.2285, 0.2020), nrow = nrow(ssm[["Fm"]]), ncol = 1)
#' ssm[["Qm"]] = rbind(c(0.1017, 0.0937, 0.0303), 
#'                     c(0.0937, 0.2267, 0.0351), 
#'                     c(0.0303, 0.0351, 0.7964))
#' ssm[["Hm"]] = cbind(rep(1, 11),
#'                     -(1 - exp(-tau*0.0423))/(tau*0.0423), 
#'                     (1 - exp(-tau*0.0423))/(tau*0.0423) - exp(-tau*0.0423))
#' ssm[["Am"]] = matrix(0, nrow = length(tau), ncol = 1)
#' ssm[["Rm"]] = diag(c(0.0087, 0, 0.0145, 0.0233, 0.0176, 0.0073, 
#'                      0, 0.0016, 0.0035, 0.0207, 0.0210))
#' ssm[["B0"]] = matrix(c(5.9030, -0.7090, 0.8690), nrow = nrow(ssm[["Fm"]]), ncol = 1)
#' ssm[["P0"]] = diag(rep(0.0001, nrow(ssm[["Fm"]])))
#'     
#' #Convert to an NxT matrix
#' yt = dcast(treasuries, "date ~ maturity", value.var = "value")
#' yt = t(yt[, 2:ncol(yt)])
#' kf = kalman_filter(ssm, yt, smooth = TRUE)   
kalman_filter_cpp <- function(ssm, yt, Xo = NULL, Xs = NULL, weight = NULL, smooth = FALSE) {
    .Call(`_kalmanfilter_kalman_filter_cpp`, ssm, yt, Xo, Xs, weight, smooth)
}

kalman_filter_tvp_cpp <- function(ssm, yt, Xo = NULL, Xs = NULL, weight = NULL, smooth = FALSE) {
    .Call(`_kalmanfilter_kalman_filter_tvp_cpp`, ssm, yt, Xo, Xs, weight, smooth)
}