R/RcppExports.R

In fntl: Numerical Tools for 'Rcpp' and Lambda Functions

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#' Numerical Derivatives via Finite Differences
#'
#' @param f Function to differentiate.
#' @param x Scalar at which to evaluate the derivative.
#' @param i First coordinate to differentiate.
#' @param j Second coordinate to differentiate.
#' @param h Step size in the first coordinate.
#' @param h_i Step size in the first coordinate.
#' @param h_j Step size in the second coordinate.
#' @param fd_type Type of derivative: `0` for symmetric difference, `1` for
#' forward difference, and `2` for backward difference.
#' @param args List of additional arguments from the function \code{richardson_args}.
#'
#' @examples
#' args = richardson_args()
#'
#' f = sin   # Try 2nd derivatives of a univariate function
#' x0 = 0.5
#' print(-sin(x0))  ## Exact answer for f''(x0)
#'
#' fd_deriv2(f, x0, i = 0, j = 0, h_i = 0.001, h_j = 0.001, fd_type = 0)
#' fd_deriv2(f, x0, i = 0, j = 0, h_i = 0.001, h_j = 0.001, fd_type = 1)
#' fd_deriv2(f, x0, i = 0, j = 0, h_i = 0.001, h_j = 0.001, fd_type = 2)
#'
#' deriv2(f, x0, i = 0, j = 0, args, fd_type = 0)
#'
#' # Try 2nd derivatives of a bivariate function
#' f = function(x) { sin(x[1]) + cos(x[2]) }
#' x0 = c(0.5, 0.25)
#'
#' print(-sin(x0[1]))  ## Exact answer for f_xx(x0)
#' print(-cos(x0[2]))  ## Exact answer for f_yy(x0)
#' print(0)         ## Exact answer for f_xy(x0,y0)
#'
#' numDeriv::hessian(f, x0)
#'
#' fd_deriv2(f, x0, i = 0, j = 0, h_i = 0.001, h_j = 0.001, fd_type = 0)
#' fd_deriv2(f, x0, i = 0, j = 0, h_i = 0.001, h_j = 0.001, fd_type = 1)
#' fd_deriv2(f, x0, i = 0, j = 0, h_i = 0.001, h_j = 0.001, fd_type = 2)
#'
#' fd_deriv2(f, x0, i = 0, j = 1, h_i = 0.001, h_j = 0.001, fd_type = 0)
#' fd_deriv2(f, x0, i = 0, j = 1, h_i = 0.001, h_j = 0.001, fd_type = 1)
#' fd_deriv2(f, x0, i = 0, j = 1, h_i = 0.001, h_j = 0.001, fd_type = 2)
#'
#' fd_deriv2(f, x0, i = 1, j = 1, h_i = 0.001, h_j = 0.001, fd_type = 0)
#' fd_deriv2(f, x0, i = 1, j = 1, h_i = 0.001, h_j = 0.001, fd_type = 1)
#' fd_deriv2(f, x0, i = 1, j = 1, h_i = 0.001, h_j = 0.001, fd_type = 2)
#'
#' deriv2(f, x0, i = 1, j = 1, args, fd_type = 0)
#' deriv2(f, x0, i = 1, j = 1, args, fd_type = 1)
#' deriv2(f, x0, i = 1, j = 1, args, fd_type = 2)
#'
#' @return
#' `fd_deriv1` and `fd_deriv2` return a single numeric value corresponding to
#' the first and second derivative via finite differences. `deriv1` and
#' `deriv2` return a list with the form of a `richardson_result` described in
#' section "Richardson Extrapolated Finite Differences" of the package
#' vignette.
#'
#' @name deriv
#'
#' @export
fd_deriv1 <- function(f, x, i, h, fd_type) {
    .Call(`_fntl_fd_deriv_rcpp`, f, x, i, h, fd_type)
}

#' @name deriv
#' @export
fd_deriv2 <- function(f, x, i, j, h_i, h_j, fd_type) {
    .Call(`_fntl_fd_deriv2_rcpp`, f, x, i, j, h_i, h_j, fd_type)
}

#' @name deriv
#' @export
deriv1 <- function(f, x, i, args, fd_type) {
    .Call(`_fntl_deriv_rcpp`, f, x, i, args, fd_type)
}

#' @name deriv
#' @export
deriv2 <- function(f, x, i, j, args, fd_type) {
    .Call(`_fntl_deriv2_rcpp`, f, x, i, j, args, fd_type)
}

#' Numerical Gradient Vector
#'
#' @param f Function to differentiate.
#' @param x Vector at which to evaluate the gradient.
#' @param args List of additional arguments from the function \code{richardson_args}.
#'
#' @examples
#' f = function(x) { sum(sin(x)) }
#' args = richardson_args()
#' x0 = seq(0, 1, length.out = 5)
#' cos(x0)  ## Exact answer
#' gradient0(f, x0, args)
#' numDeriv::grad(f, x0)
#'
#' @return
#' A list with the form of a `gradient_result` described in section "Gradient"
#' of the package vignette.
#'
#' @export
gradient0 <- function(f, x, args) {
    .Call(`_fntl_gradient_rcpp`, f, x, args)
}

#' Numerical Jacobian Matrix
#'
#' @param f Function to differentiate.
#' @param x Vector at which to evaluate the Jacobian.
#' @param args List of additional arguments from the function \code{richardson_args}.
#'
#' @examples
#' f = function(x) { cumsum(sin(x)) }
#' x0 = seq(1, 10, length.out = 5)
#' args = richardson_args()
#' out = jacobian0(f, x0, args)
#' print(out$value)
#' numDeriv::jacobian(f, x0)
#'
#' @return
#' A list with the form of a `jacobian_result` described in section "Jacobian"
#' of the package vignette.
#'
#' @export
jacobian0 <- function(f, x, args) {
    .Call(`_fntl_jacobian_rcpp`, f, x, args)
}

#' Numerical Hessian
#'
#' @param f Function to differentiate.
#' @param x Vector at which to evaluate the Hessian.
#' @param args List of additional arguments from the function \code{richardson_args}.
#'
#' @examples
#' f = function(x) { sum(x^2) }
#' x0 = seq(1, 10, length.out = 5)
#' args = richardson_args()
#' hessian0(f, x0, args)
#' numDeriv::hessian(f, x0)
#'
#' @return
#' A list with the form of a `hessian_result` described in section "Hessian"
#' of the package vignette.
#'
#' @export
hessian0 <- function(f, x, args) {
    .Call(`_fntl_hessian_rcpp`, f, x, args)
}

#' Find Root
#'
#' @param f Function for which a root is desired.
#' @param lower Lower limit of search interval. Must be finite.
#' @param upper Upper limit of search interval. Must be finite.
#' @param args List of additional arguments from the function \code{findroot_args}.
#'
#' @examples
#' f = function(x) { x^2 - 1 }
#' args = findroot_args()
#' findroot_bisect(f, 0, 10, args)
#' findroot_brent(f, 0, 10, args)
#'
#' @return
#' A list with the form of a `findroot_result` described in section
#' "Root-Finding" of the package vignette.
#'
#' @name findroot
#'
#' @export
findroot_bisect <- function(f, lower, upper, args) {
    .Call(`_fntl_findroot_bisect_rcpp`, f, lower, upper, args)
}

#' @name findroot
#' @export
findroot_brent <- function(f, lower, upper, args) {
    .Call(`_fntl_findroot_brent_rcpp`, f, lower, upper, args)
}

#' Univariate Optimization
#'
#' @param f Function to optimize.
#' @param lower Lower limit of search interval. Must be finite.
#' @param upper Upper limit of search interval. Must be finite.
#' @param args List of additional arguments from the function \code{optimize_args}.
#'
#' @examples
#' f = function(x) { x^2 - 1 }
#' args = optimize_args()
#' goldensection(f, 0, 10, args)
#' optimize_brent(f, 0, 10, args)
#'
#' @return
#' A list with the form of a `optimize_result` described in section
#' "Univariate Optimization" of the package vignette.
#'
#' @name univariate-optimization
#'
#' @export
goldensection <- function(f, lower, upper, args) {
    .Call(`_fntl_goldensection_rcpp`, f, lower, upper, args)
}

#' @name univariate-optimization
#' @export
optimize_brent <- function(f, lower, upper, args) {
    .Call(`_fntl_optimize_brent_rcpp`, f, lower, upper, args)
}

#' Integration
#'
#' Compute the integral \eqn{\int_a^b f(x) dx}.
#'
#' @param f Function to integrate.
#' @param lower Lower limit of integral.
#' @param upper Upper limit of integral.
#' @param args List of additional arguments from the function \code{integrate_args}.
#'
#' @examples
#' f = function(x) { exp(-x^2 / 2) }
#' args = integrate_args()
#' integrate0(f, 0, 10, args)
#'
#' @return
#' A list with the form of a `integrate_result` described in section
#' "Integration" of the package vignette.
#'
#' @export
integrate0 <- function(f, lower, upper, args) {
    .Call(`_fntl_integrate_rcpp`, f, lower, upper, args)
}

#'  Multivariate Optimization
#'
#' @param init Initial value
#' @param f Function \eqn{f} to optimize
#' @param g Gradient function of \eqn{f}.
#' @param h Hessian function of \eqn{f}.
#' @param args List of additional arguments for optimization.
#'
#' @details
#' The argument `args` should be a list constructed from one of the following
#' functions:
#'
#' - `bfgs_args` for BFGS;
#' - `lbfgsb_args` for L-BFGS-B;
#' - `cg_args` for CG;
#' - `neldermead_args` for Nelder-Mead;
#' - `nlm_args` for the Newton-type algorithm used in `nlm`.
#'
#' When `g` or `h` are omitted, the gradient or Hessian will be respectively
#' be computed via finite differences.
#'
#' @examples
#' f = function(x) { sum(x^2) }
#' g = function(x) { 2*x }
#' h = function(x) { 2*diag(length(x)) }
#' x0 = c(1,1)
#'
#' args = cg_args()
#' cg1(x0, f, g, args)
#' cg2(x0, f, args)
#'
#' args = bfgs_args()
#' bfgs1(x0, f, g, args)
#' bfgs2(x0, f, args)
#'
#' args = lbfgsb_args()
#' lbfgsb1(x0, f, g, args)
#' lbfgsb2(x0, f, args)
#'
#' args = neldermead_args()
#' neldermead(x0, f, args)
#'
#' args = nlm_args()
#' nlm1(x0, f, g, h, args)
#' nlm2(x0, f, g, args)
#' nlm3(x0, f, args)
#'
#' @return
#' A list with results corresponding to the specified function. See the
#' package vignette for further details.
#'
#' - `cg1` and `cg2` return a `cg_result` which is documented in the section
#'   "Conjugate Gradient".
#' - `bfgs1` and `bfgs2` return a `bfgs_result` which is documented in the
#'   section "BFGS".
#' - `lbfgsb1` and `lbfgsb2` return a `lbfgsb_result` which is documented in
#'   the section "L-BFGS-B".
#' - `neldermead` returns a `neldermead_result` which is documented in
#'   the section "Nelder-Mead".
#' - `nlm1`, `nlm2`, and `nlm3` return a `nlm_result` which is documented in
#'   the section "Newton-Type Algorithm for Nonlinear Optimization".
#'
#' @name multivariate-optimization
#'
#' @export
cg1 <- function(init, f, g, args) {
    .Call(`_fntl_cg1_rcpp`, init, f, g, args)
}

#' @name multivariate-optimization
#' @export
cg2 <- function(init, f, args) {
    .Call(`_fntl_cg2_rcpp`, init, f, args)
}

#' @name multivariate-optimization
#' @export
bfgs1 <- function(init, f, g, args) {
    .Call(`_fntl_bfgs1_rcpp`, init, f, g, args)
}

#' @name multivariate-optimization
#' @export
bfgs2 <- function(init, f, args) {
    .Call(`_fntl_bfgs2_rcpp`, init, f, args)
}

#' @name multivariate-optimization
#' @export
lbfgsb1 <- function(init, f, g, args) {
    .Call(`_fntl_lbfgsb1_rcpp`, init, f, g, args)
}

#' @name multivariate-optimization
#' @export
lbfgsb2 <- function(init, f, args) {
    .Call(`_fntl_lbfgsb2_rcpp`, init, f, args)
}

#' @name multivariate-optimization
#' @export
neldermead <- function(init, f, args) {
    .Call(`_fntl_neldermead_rcpp`, init, f, args)
}

#' @name multivariate-optimization
#' @export
nlm1 <- function(init, f, g, h, args) {
    .Call(`_fntl_nlm1_rcpp`, init, f, g, h, args)
}

#' @name multivariate-optimization
#' @export
nlm2 <- function(init, f, g, args) {
    .Call(`_fntl_nlm2_rcpp`, init, f, g, args)
}

#' @name multivariate-optimization
#' @export
nlm3 <- function(init, f, args) {
    .Call(`_fntl_nlm3_rcpp`, init, f, args)
}

#' Matrix Apply Functions
#'
#' @param X A matrix
#' @param f The function to apply.
#'
#' @details
#' The `mat_apply`, `row_apply`, and `col_apply` C++ functions are intended to
#' operate like the following calls in R, respectively.
#' ```
#' apply(x, c(1,2), f)
#' apply(x, 1, f)
#' apply(x, 2, f)
#' ```
#'
#' The R functions exposed here are specific to numeric-valued matrices, but
#' the underlying C++ functions are intended to work with any type of Rcpp
#' Matrix.
#'
#' @examples
#' X = matrix(1:12, nrow = 4, ncol = 3)
#' mat_apply(X, f = function(x) { x^(1/3) })
#' row_apply(X, f = function(x) { sum(x^2) })
#' col_apply(X, f = function(x) { sum(x^2) })
#'
#' @return
#' `mat_apply` returns a matrix. `row_apply` and `col_apply` return a vector.
#' See section "Apply" of the package vignette for details.
#'
#' @name matrix_apply
#'
#' @export
mat_apply <- function(X, f) {
    .Call(`_fntl_mat_apply_rcpp`, X, f)
}

#' @name matrix_apply
#' @export
row_apply <- function(X, f) {
    .Call(`_fntl_row_apply_rcpp`, X, f)
}

#' @name matrix_apply
#' @export
col_apply <- function(X, f) {
    .Call(`_fntl_col_apply_rcpp`, X, f)
}

#' Matrix Which Function
#'
#' @param X A matrix
#' @param f A predicate to apply to each element of \eqn{X}.
#'
#' @details
#' The `which` C++ functions are intended to operate like the following call
#' in R.
#' ```
#' which(f(X), arr.ind = TRUE) - 1
#' ```
#'
#' The R functions exposed here are specific to numeric-valued matrices, but
#' the underlying C++ functions are intended to work with any type of Rcpp
#' Matrix.
#'
#' @examples
#' X = matrix(1:12 / 6, nrow = 4, ncol = 3)
#' f = function(x) { x < 1 }
#' which0(X, f)
#'
#' @return
#' A matrix with two columns. Each row contains a row and column index
#' corresponding to an element of \eqn{X} that matches the criteria of \eqn{f}.
#' See section "Which" of the package vignette for details.
#'
#' @export
which0 <- function(X, f) {
    .Call(`_fntl_which_rcpp`, X, f)
}

#' Outer Matrix
#'
#' Compute "outer" matrices and matrix-vector products based on a function
#' that operators on pairs of rows. See details.
#'
#' @param X A numerical matrix.
#' @param Y A numerical matrix.
#' @param f Function \eqn{f(x, y)} that operates on a pair of rows. Depending
#' on the context, rows \eqn{x} and \eqn{y} are both rows of \eqn{X}, or
#' \eqn{x} is a row from \eqn{X} and \eqn{y} is a row from from \eqn{Y}.
#' @param a A scalar vector.
#'
#' @details
#' The `outer1` function computes the \eqn{n \times n} symmetric matrix
#'
#' \deqn{
#' \text{\texttt outer1}(X, f) =
#' \begin{bmatrix}
#' f(x_1, x_1) & \cdots & f(x_1, x_n) \cr
#' \vdots & \ddots & \vdots \cr
#' f(x_n, x_1) & \cdots & f(x_n, x_n) \cr
#' \end{bmatrix}
#' }
#'
#' and the `outer1_matvec` operation computes the \eqn{n}-dimensional vector
#'
#' \deqn{
#' \text{\texttt outer1\_matvec}(X, f, a) =
#' \begin{bmatrix}
#' f(x_1, x_1) & \cdots & f(x_1, x_n) \cr
#' \vdots & \ddots & \vdots \cr
#' f(x_n, x_1) & \cdots & f(x_n, x_n) \cr
#' \end{bmatrix}
#' \begin{bmatrix}
#' a_1 \cr
#' \vdots \cr
#' a_n \cr
#' \end{bmatrix}.
#' }
#'
#' The `outer2` operation computes the \eqn{m \times n} matrix
#'
#' \deqn{
#' \text{\texttt outer2}(X, Y, f) =
#' \begin{bmatrix}
#' f(x_1, y_1) & \cdots & f(x_1, y_n) \cr
#' \vdots & \ddots & \vdots \cr
#' f(x_m, y_1) & \cdots & f(x_m, y_n) \cr
#' \end{bmatrix}
#' }
#'
#' and the `outer2_matvec` operation computes the \eqn{m}-dimensional vector
#'
#' \deqn{
#' \text{\texttt outer2\_matvec}(X, Y, f, a) =
#' \begin{bmatrix}
#' f(x_1, y_1) & \cdots & f(x_1, y_n) \cr
#' \vdots & \ddots & \vdots \cr
#' f(x_m, y_1) & \cdots & f(x_m, y_n) \cr
#' \end{bmatrix}
#' \begin{bmatrix}
#' a_1 \cr
#' \vdots \cr
#' a_n \cr
#' \end{bmatrix}.
#' }
#'
#' @examples
#' set.seed(1234)
#' f = function(x,y) { sum( (x - y)^2 ) }
#' X = matrix(rnorm(12), 6, 2)
#' Y = matrix(rnorm(10), 5, 2)
#' outer1(X, f)
#' outer2(X, Y, f)
#'
#' a = rep(1, 6)
#' b = rep(1, 5)
#' outer1_matvec(X, f, a)
#' outer2_matvec(X, Y, f, b)
#'
#' @return
#' `outer1` and `outer2` return a matrix. `outer1_matvec` and `outer2_matvec`
#' return a vector. See section "Outer" of the package vignette for details.
#'
#' @name outer
#'
#' @export
outer1 <- function(X, f) {
    .Call(`_fntl_outer1_rcpp`, X, f)
}

#' @name outer
#' @export
outer2 <- function(X, Y, f) {
    .Call(`_fntl_outer2_rcpp`, X, Y, f)
}

#' @name outer
#' @export
outer1_matvec <- function(X, f, a) {
    .Call(`_fntl_outer1_matvec_rcpp`, X, f, a)
}

#' @name outer
#' @export
outer2_matvec <- function(X, Y, f, a) {
    .Call(`_fntl_outer2_matvec_rcpp`, X, Y, f, a)
}

#' Iteratively Solve a Linear System with Conjugate Gradient
#'
#' Solve the system \eqn{l(x) = b} where \eqn{l(x)} is a matrix-free
#' representation of the linear operation \eqn{Ax}.
#'
#' @param l A linear transformation of \eqn{x}.
#' @param b A vector.
#' @param init Initial value of solution.
#' @param args List of additional arguments from `cg_args`.
#'
#' @examples
#' set.seed(1234)
#'
#' n = 8
#' idx_diag = cbind(1:n, 1:n)
#' idx_ldiag = cbind(2:n, 1:(n-1))
#' idx_udiag = cbind(1:(n-1), 2:n)
#' b = rep(1, n)
#'
#' ## Solution by explicit computation of solve(A, b)
#' A = matrix(0, n, n)
#' A[idx_diag] = 2
#' A[idx_ldiag] = 1
#' A[idx_udiag] = 1
#' solve(A, b)
#'
#' ## Solve iteratively with solve_cg
#' f = function(x) { A %*% x }
#' args = cg_args()
#' init = rep(0, n)
#' solve_cg(f, b, init, args)
#'
#' @return
#' A list with the form of a `solve_cg_result` described in section "Conjugate
#' Gradient" of the package vignette.
#'
#' @export
solve_cg <- function(l, b, init, args) {
    .Call(`_fntl_solve_cg_rcpp`, l, b, init, args)
}

#' Functions for Truncated Distributions
#'
#' Density, CDF, quantile, and drawing functions for a univariate distribution
#' with density \eqn{f}, cdf, \eqn{F}, and quantile function \eqn{F^{-}}
#' truncated to the interval \eqn{[a ,b]}.
#'
#' @param n Number of draws.
#' @param x Vector of quantiles.
#' @param p Vector of probabilities.
#' @param lo Vector of lower limits.
#' @param hi Vector of upper limits.
#' @param f Density function with form `f(x, log)`.
#' @param F CDF function with signature `F(x, lower, log)`, where `x` is
#' numeric and `lower` and `log` are logical.
#' @param Finv Quantile function with signature `Finv(x, lower, log)`, where
#' `x` is numeric and `lower` and `log` are logical.
#' @param lower logical; if `TRUE`, probabilities are \eqn{P(X \leq x)};
#' otherwise, \eqn{P(X > x)}.
#' @param log logical; if `TRUE`, probabilities are given on log-scale.
#'
#' @return Vector with results.
#' `d_trunc` computes the density, `r_trunc` generates random deviates,
#' `p_trunc` computes the CDF, and `q_trunc` computes quantiles.
#'
#' @examples
#' library(tidyverse)
#'
#' m = 100  ## Length of sequence for density, CDF, etc
#' shape1 = 5
#' shape2 = 2
#' lo = 0.5
#' hi = 0.7
#'
#' # Density, CDF, and quantile function for untruncated distribution
#' ff = function(x, log) { dbeta(x, shape1, shape2, log = log) }
#' FF = function(x, lower, log) { pbeta(x, shape1, shape2, lower.tail = lower, log = log) }
#' FFinv = function(x, lower, log) { qbeta(x, shape1, shape2, lower.tail = lower, log = log) }
#'
#' # Compare truncated and untruncated densities
#' xseq = seq(0, 1, length.out = m)
#' lo_vec = rep(lo, m)
#' hi_vec = rep(hi, m)
#' f0seq = ff(xseq, log = FALSE)
#' fseq = d_trunc(xseq, lo_vec, hi_vec, ff, FF)
#' data.frame(x = xseq, f = fseq, f0 = f0seq) %>%
#'     ggplot() +
#'	    geom_line(aes(xseq, fseq)) +
#'	    geom_line(aes(xseq, f0seq), lty = 2) +
#'	    xlab("x") +
#'	    ylab("Density") +
#'     theme_minimal()
#'
#' # Compare truncated densities and empirical density of generated draws
#' n = 100000
#' lo_vec = rep(lo, n)
#' hi_vec = rep(hi, n)
#' x = r_trunc(n = n, lo_vec, hi_vec, FF, FFinv)
#' hist(x, probability = TRUE, breaks = 15)
#' points(xseq, fseq)
#'
#' # Compare empirical CDF of draws with CDF function
#' Femp = ecdf(x)
#' lo_vec = rep(lo, m)
#' hi_vec = rep(hi, m)
#' Fseq = p_trunc(xseq, lo_vec, hi_vec, FF)
#' data.frame(x = xseq, FF = Fseq) %>%
#'     mutate(F0 = Femp(x)) %>%
#'     ggplot() +
#'     geom_line(aes(xseq, FF), lwd = 1.2) +
#'     geom_line(aes(xseq, F0), col = "orange") +
#'     xlab("x") +
#'     ylab("Probability") +
#'     theme_minimal()
#'
#' # Compare empirical quantiles of draws with quantile function
#' pseq = seq(0, 1, length.out = m)
#' lo_vec = rep(lo, m)
#' hi_vec = rep(hi, m)
#' Finvseq = q_trunc(pseq, lo_vec, hi_vec, FF, FFinv)
#' Finvemp = quantile(x, prob = pseq)
#' data.frame(p = pseq, Finv = Finvseq, Finvemp = Finvemp) %>%
#'	    ggplot() +
#'	    geom_line(aes(pseq, Finv), lwd = 1.2) +
#'	    geom_line(aes(pseq, Finvemp), col = "orange") +
#'	    xlab("p") +
#'	    ylab("Quantile") +
#'	    theme_minimal()
#'
#' @name trunc
#' @export
d_trunc <- function(x, lo, hi, f, F, log = FALSE) {
    .Call(`_fntl_d_trunc_rcpp`, x, lo, hi, f, F, log)
}

#' @name trunc
#' @export
p_trunc <- function(x, lo, hi, F, lower = TRUE, log = FALSE) {
    .Call(`_fntl_p_trunc_rcpp`, x, lo, hi, F, lower, log)
}

#' @name trunc
#' @export
q_trunc <- function(p, lo, hi, F, Finv, lower = TRUE, log = FALSE) {
    .Call(`_fntl_q_trunc_rcpp`, p, lo, hi, F, Finv, lower, log)
}

#' @name trunc
#' @export
r_trunc <- function(n, lo, hi, F, Finv) {
    .Call(`_fntl_r_trunc_rcpp`, n, lo, hi, F, Finv)
}

#' Arguments
#'
#' Get an arguments list for internal methods with the default settings. This
#' object can be adjusted and passed to the respective function.
#'
#' @return
#' An argument list corresponding to the specified function. The elements of
#' the list are named and supplied with default values. See the package
#' vignette for further details.
#'
#' - `findroot_args` is documented in the section "Root-Finding".
#' - `optimize_args` is documented in the section "Univariate Optimization".
#' - `integrate_args` is documented in the section "Integration".
#' - `cg_args` is documented in the section "Conjugate Gradient".
#' - `bfgs_args` is documented in the section "BFGS".
#' - `lbfgsb_args` is documented in the section "L-BFGS-B".
#' - `neldermead_args`is documented in the section "Nelder-Mead".
#' - `nlm_args` is documented in the section "Newton-Type Algorithm for
#'   Nonlinear Optimization".
#' - `richardson_args` is documented in the section "Richardson
#'   Extrapolated Finite Differences".
#'
#' @name args
#' @export
findroot_args <- function() {
    .Call(`_fntl_findroot_args_rcpp`)
}

#' @name args
#' @export
optimize_args <- function() {
    .Call(`_fntl_optimize_args_rcpp`)
}

#' @name args
#' @export
integrate_args <- function() {
    .Call(`_fntl_integrate_args_rcpp`)
}

#' @name args
#' @export
cg_args <- function() {
    .Call(`_fntl_cg_args_rcpp`)
}

#' @name args
#' @export
bfgs_args <- function() {
    .Call(`_fntl_bfgs_args_rcpp`)
}

#' @name args
#' @export
lbfgsb_args <- function() {
    .Call(`_fntl_lbfgsb_args_rcpp`)
}

#' @name args
#' @export
neldermead_args <- function() {
    .Call(`_fntl_neldermead_args_rcpp`)
}

#' @name args
#' @export
nlm_args <- function() {
    .Call(`_fntl_nlm_args_rcpp`)
}

#' @name args
#' @export
richardson_args <- function() {
    .Call(`_fntl_richardson_args_rcpp`)
}