use Roxygen more

3 files changed, 84 insertions, 19 deletions

diff --git a/DESCRIPTION b/DESCRIPTION
index c19cdcd..f4eb62a 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -6,6 +6,8 @@ Date: 2014-10-03
 Author: Guillaume Horel
 Maintainer: Guillaume Horel <guillaume.horel@serenitascapital.com>
 Description: Utility functions for generating the loss and recovery
-	distributions of a portfolio of obligations
+             distributions of a portfolio of obligations.
 License: GPL-3
 Suggests: testthat
+RoxygenNote: 6.0.1
+Imports: distr
diff --git a/NAMESPACE b/NAMESPACE
index 65b8827..8f0bb73 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,2 +1,34 @@
-exportPattern("^[[:alpha:]]+")
-useDynLib("lossdistrib", .registration=TRUE, .fixes = "C_")
+# Generated by roxygen2: do not edit by hand
+
+export(BCER)
+export(BClossdist)
+export(BClossdistC)
+export(GHquad)
+export(dist.transform)
+export(dqnorm)
+export(dshockprob)
+export(exp_trunc)
+export(fit.prob)
+export(fit.probC)
+export(lossdist.joint)
+export(lossdist.prepay.joint)
+export(lossdistC)
+export(lossdistC.prepay.joint)
+export(lossdistC.prepay.jointZ)
+export(lossdistC.truncated)
+export(lossdistCZ)
+export(lossdistrib)
+export(lossdistrib.fft)
+export(lossdistrib2)
+export(lossdistrib2.truncated)
+export(lossrecovdist)
+export(lossrecovdist.joint.term)
+export(lossrecovdist.term)
+export(rec.trunc)
+export(recovdist)
+export(recovdistC)
+export(shockprob)
+export(shockseverity)
+export(stochasticrecov)
+export(stochasticrecovC)
+useDynLib(lossdistrib, .registration=TRUE, .fixes = "C_")
diff --git a/R/distrib.R b/R/distrib.R
index 59fba37..ae5e51f 100644
--- a/R/distrib.R
+++ b/R/distrib.R
@@ -9,7 +9,8 @@
 ## recovery with 0.1-1 support, so it's not clear that there is a big gain.
 ## - do the correlation adjustments when computing the deltas since it seems to be
 ## the market standard
-
+#' @useDynLib lossdistrib, .registration=TRUE, .fixes = "C_"
+NULL
 #' Gauss-Hermite quadrature weights
 #'
 #' \code{GHquad} computes the quadrature weights for integrating against a
@@ -21,8 +22,8 @@
 #' @return A list with two components:
 #'  \item{Z}{the list of nodes}
 #'  \item{w}{the corresponding weights}
-#'
-GHquad <- function(n){
+#' @export
+GHquad <- function(n) {
     n <- as.integer(n)
     Z <- double(n)
     w <- double(n)
@@ -42,6 +43,7 @@ GHquad <- function(n){
 #' algorithm of Andersen, Sidenius and Basu
 #' @param p Numeric vector, the vector of success probabilities
 #' @return A vector q such that \eqn{q_k=\Pr(S=k)}
+#' @export
 lossdistrib <- function(p){
     ## basic recursive algorithm of Andersen, Sidenius and Basu
     n <- length(p)
@@ -81,6 +83,7 @@ convolve <- function(dist1, dist2) {
 #' compared to \eqn{O(n^2)} for the recursive algorithm.
 #' @param p Numeric vector, the vector of success probabilities
 #' @return A vector such that \eqn{q_k=\Pr(S=k)}
+#' @export
 lossdistrib.fft <- function(p) {
     ## haven't tested when p is not a power of 2.
     if(length(p) == 1){
@@ -109,7 +112,8 @@ lossdistrib.fft <- function(p) {
 #' (instead of the loss distribution).
 #' @return a Numeric vector of size \code{N} computing the loss (resp.
 #' default) distribution if \code{defaultflag} is FALSE (resp. TRUE).
-lossdistrib2 <- function(p, w, S, N, defaultflag=FALSE){
+#' @export
+lossdistrib2 <- function(p, w, S, N, defaultflag = FALSE){
     n <- length(p)
     lu <- 1/(N-1)
     q <- rep(0, N)
@@ -147,7 +151,8 @@ lossdistrib2 <- function(p, w, S, N, defaultflag=FALSE){
 #' @param N Integer, number of ticks in the grid
 #' @param cutoff Integer, where to stop computing the exact probabilities
 #' @return a Numeric vector of size \code{N} computing the loss distribution
-lossdistrib2.truncated <- function(p, w, S, N, cutoff=N){
+#' @export
+lossdistrib2.truncated <- function(p, w, S, N, cutoff = N){
     n <- length(p)
     lu <- 1/(N-1)
     q <- rep(0, cutoff)
@@ -188,6 +193,7 @@ lossdistrib2.truncated <- function(p, w, S, N, cutoff=N){
 #' @param S Numeric, vector of severities
 #' @param N Integer, number of ticks in the grid
 #' @return a Numeric vector of size \code{N} computing the recovery distribution
+#' @export
 recovdist <- function(dp, pp, w, S, N){
     n <- length(dp)
     q <- rep(0, N)
@@ -226,7 +232,8 @@ recovdist <- function(dp, pp, w, S, N){
 #' @return q Matrix of joint loss, recovery probability distribution
 #' colSums(q) is the recovery distribution marginal
 #' rowSums(q) is the loss distribution marginal
-lossdist.joint <- function(p, w, S, N, defaultflag=FALSE){
+#' @export
+lossdist.joint <- function(p, w, S, N, defaultflag = FALSE){
     n <- length(p)
     lu <- 1/(N-1)
     q <- matrix(0, N, N)
@@ -249,11 +256,12 @@ lossdist.joint <- function(p, w, S, N, defaultflag=FALSE){
         qtemp[(i+2):N, (j+1):N] <- qtemp[(i+2):N, (j+1):N] + psplit[4] * q[1:(N-i-1), 1:(N-j)]
         q <- qtemp + (1-p[k])*q
     }
-    q[length(q)] <- q[length(q)]+1-sum(q)
+    q[length(q)] <- q[length(q)] + 1 - sum(q)
     return(q)
 }
 
-lossdist.prepay.joint <- function(dp, pp, w, S, N, defaultflag=FALSE){
+#' @export
+lossdist.prepay.joint <- function(dp, pp, w, S, N, defaultflag = FALSE){
     ## recursive algorithm with first order correction
     ## to compute the joint probability distribition of the loss and recovery
     ## inputs:
@@ -314,6 +322,7 @@ lossdist.prepay.joint <- function(dp, pp, w, S, N, defaultflag=FALSE){
     return(q)
 }
 
+#' @export
 lossdistC <- function(p, w, S, N, defaultflag=FALSE){
     ## C version of lossdistrib2, roughly 50 times faster
     .C(C_lossdistrib, as.double(p), as.integer(length(p)),
@@ -321,6 +330,7 @@ lossdistC <- function(p, w, S, N, defaultflag=FALSE){
        as.logical(defaultflag), q = double(N))$q
 }
 
+#' @export
 lossdistCZ <- function(p, w, S, N, defaultflag=FALSE, rho, Z){
     ##S is of size (length(p), length(Z))
     stopifnot(length(rho) == length(p),
@@ -332,6 +342,7 @@ lossdistCZ <- function(p, w, S, N, defaultflag=FALSE, rho, Z){
        as.double(Z), as.integer(length(Z)), q = matrix(0, N, length(Z)))$q
 }
 
+#' @export
 lossdistC.truncated <- function(p, w, S, N, T=N, defaultflag=FALSE){
     ## truncated version of lossdistrib
     ## q[i] is 0 for i>=T
@@ -340,13 +351,15 @@ lossdistC.truncated <- function(p, w, S, N, T=N, defaultflag=FALSE){
        as.logical(defaultflag), q = double(N))$q
 }
 
-exp.trunc <- function(p, w, S, N, K){
+#' @export
+exp_trunc <- function(p, w, S, N, K){
     ## computes E[(K-L)^+]
     r <- 0
     .C(C_exp_trunc, as.double(p), as.integer(length(p)),
        as.double(w), as.double(S), as.integer(N), as.double(K), res = r)$res
 }
 
+#' @export
 rec.trunc <- function(p, w, S, N, K){
     ## computes E[(K-(1-R))^+] = E[(\tilde K- \bar R)]
     ## where \tilde K = K-sum_i w_i S_i and \bar R=\sum_i w_i R_i (1-X_i)
@@ -358,12 +371,14 @@ rec.trunc <- function(p, w, S, N, K){
     }
 }
 
+#' @export
 recovdistC <- function(dp, pp, w, S, N){
     ## C version of recovdist
     .C(C_recovdist, as.double(dp), as.double(pp), as.integer(length(dp)),
        as.double(w), as.double(S), as.integer(N), q = double(N))$q
 }
 
+#' @export
 lossdistC.prepay.joint <- function(dp, pp, w, S, N, defaultflag=FALSE){
     ## C version of lossdist.prepay.joint
     r <- .C(C_lossdistrib_joint, as.double(dp), as.double(pp), as.integer(length(dp)),
@@ -371,6 +386,7 @@ lossdistC.prepay.joint <- function(dp, pp, w, S, N, defaultflag=FALSE){
     return(r)
 }
 
+#' @export
 lossdistC.prepay.jointZ <- function(dp, pp, w, S, N, defaultflag = FALSE, rho, Z, wZ){
     ## N is the size of the grid
     ## dp is of size n.credits
@@ -387,6 +403,7 @@ lossdistC.prepay.jointZ <- function(dp, pp, w, S, N, defaultflag = FALSE, rho, Z
     return(r$output)
 }
 
+#' @export
 lossrecovdist <- function(defaultprob, prepayprob, w, S, N, defaultflag=FALSE, useC=TRUE){
     lossdistrib2 <- if(useC) lossdistC
     recovdist <- if(useC) recovdistC
@@ -400,6 +417,7 @@ lossrecovdist <- function(defaultprob, prepayprob, w, S, N, defaultflag=FALSE, u
     return(list(L=L, R=R))
 }
 
+#' @export
 lossrecovdist.term <- function(defaultprob, prepayprob, w, S, N, defaultflag=FALSE, useC=TRUE){
     ## computes the loss and recovery distribution over time
     L <- array(0, dim=c(N, ncol(defaultprob)))
@@ -420,6 +438,7 @@ lossrecovdist.term <- function(defaultprob, prepayprob, w, S, N, defaultflag=FAL
     return(list(L=L, R=R))
 }
 
+#' @export
 lossrecovdist.joint.term <- function(defaultprob, prepayprob, w, S, N, defaultflag=FALSE, useC=TRUE){
     ## computes the joint loss and recovery distribution over time
     Q <- array(0, dim=c(ncol(defaultprob), N, N))
@@ -437,7 +456,8 @@ lossrecovdist.joint.term <- function(defaultprob, prepayprob, w, S, N, defaultfl
     return(Q)
 }
 
-dist.transform <- function(dist.joint){
+#' @export
+dist.transform <- function(dist.joint) {
     ## compute the joint (D, R) distribution
     ## from the (L, R) distribution using D = L+R
     distDR <- array(0, dim=dim(dist.joint))
@@ -459,7 +479,8 @@ dist.transform <- function(dist.joint){
     return( distDR )
 }
 
-shockprob <- function(p, rho, Z, log.p=F){
+#' @export
+shockprob <- function(p, rho, Z, log.p=F) {
   ## computes the shocked default probability as a function of the copula factor
   ## function is vectorized provided the below caveats:
   ## p and rho are vectors of same length n, Z is a scalar, returns vector of length n
@@ -478,7 +499,8 @@ shockprob <- function(p, rho, Z, log.p=F){
   }
 }
 
-shockseverity <- function(S, Stilde=1, Z, rho, p){
+#' @export
+shockseverity <- function(S, Stilde=1, Z, rho, p) {
   ## computes the severity as a function of the copula factor Z
   result <- double(length(S))
   result[p==0] <- 0
@@ -487,15 +509,18 @@ shockseverity <- function(S, Stilde=1, Z, rho, p){
   return(result)
 }
 
-dshockprob <- function(p,rho,Z){
+#' @export
+dshockprob <- function(p,rho,Z) {
     dnorm((qnorm(p)-sqrt(rho)*Z)/sqrt(1-rho))*dqnorm(p)/sqrt(1-rho)
 }
 
+#' @export
 dqnorm <- function(x){
     1/dnorm(qnorm(x))
 }
 
-fit.prob <- function(Z, w, rho, p0){
+#' @export
+fit.prob <- function(Z, w, rho, p0) {
     ## if the weights are not perfectly gaussian, find the probability p such
     ## E_w(shockprob(p, rho, Z)) = p0
     if(p0==0){
@@ -519,14 +544,16 @@ fit.prob <- function(Z, w, rho, p0){
     return(p)
 }
 
-fit.probC <- function(Z, w, rho, p0){
+#' @export
+fit.probC <- function(Z, w, rho, p0) {
     stopifnot(length(Z)==length(w))
     r <- .C(C_fitprob, as.double(Z), as.double(w), as.integer(length(Z)),
             as.double(rho), as.double(p0), q = double(1))
     return(r$q)
 }
 
-stochasticrecov <- function(R, Rtilde, Z, w, rho, porig, pmod){
+#' @export
+stochasticrecov <- function(R, Rtilde, Z, w, rho, porig, pmod) {
     ## if porig == 0 (probably matured asset) then return orginal recovery
     ## it shouldn't matter anyway since we never default that asset
     if(porig == 0){
@@ -537,6 +564,7 @@ stochasticrecov <- function(R, Rtilde, Z, w, rho, porig, pmod){
     }
 }
 
+#' @export
 stochasticrecovC <- function(R, Rtilde, Z, w, rho, porig, pmod){
     r <- .C(C_stochasticrecov, as.double(R), as.double(Rtilde), as.double(Z),
             as.double(w), as.integer(length(Z)), as.double(rho), as.double(porig),
@@ -544,6 +572,7 @@ stochasticrecovC <- function(R, Rtilde, Z, w, rho, porig, pmod){
     return(r$q)
 }
 
+#' @export
 BClossdist <- function(defaultprob, issuerweights, recov, rho, Z, w,
                        N=length(recov)+1, defaultflag=FALSE, n.int=500){
 
@@ -575,6 +604,7 @@ BClossdist <- function(defaultprob, issuerweights, recov, rho, Z, w,
     list(L=L, R=R)
 }
 
+#' @export
 BClossdistC <- function(defaultprob, issuerweights, recov, rho, Z, w,
                         N=length(issuerweights)+1, defaultflag=FALSE){
     if(is.null(dim(defaultprob))){
@@ -593,6 +623,7 @@ BClossdistC <- function(defaultprob, issuerweights, recov, rho, Z, w,
     return(list(L=r$L,R=r$R))
 }
 
+#' @export
 BCER <- function(defaultprob, issuerweights, recov, K, rho, Z, w,
                  N=length(issuerweights)+1, defaultflag=FALSE){
     stopifnot(length(Z)==length(w),