Added the Semi-Parametric Distribution

* Some additional fixes for CRAN Additional Issues

.Rbuildignore

@@ -3,3 +3,4 @@
 ^\.github$
 README.Rmd
 README.md
+^cran-comments\.md$

DESCRIPTION

@@ -1,18 +1,19 @@
 Package: tsdistributions
 Type: Package
 Title: Location Scale Standardized Distributions
-Version: 1.0.0
+Version: 1.0.1
 Authors@R: c(person("Alexios", "Galanos", role = c("aut", "cre","cph"), email = "[email protected]"))
 Maintainer: Alexios Galanos <[email protected]>
 Depends: R (>= 3.5.0), methods, tsmethods
 LinkingTo: TMB, RcppEigen
-Imports: TMB (>= 1.7.20), GeneralizedHyperbolic, SkewHyperbolic, stats, utils, data.table, zoo, Rsolnp, sandwich, future.apply, future, progressr
-Description: Location-Scale based distributions parameterized in terms of mean, standard deviation, skew and shape parameters and estimation using automatic differentiation. Distributions include the Normal, Student and GED as well as their skewed variants ('Fernandez and Steel'), the 'Johnson SU', and the Generalized Hyperbolic.
+Imports: TMB (>= 1.7.20), Rdpack, GeneralizedHyperbolic, KernSmooth, SkewHyperbolic, mev, stats, utils, data.table, zoo, Rsolnp, sandwich, future.apply, future, progressr
+Description: Location-Scale based distributions parameterized in terms of mean, standard deviation, skew and shape parameters and estimation using automatic differentiation. Distributions include the Normal, Student and GED as well as their skewed variants ('Fernandez and Steel'), the 'Johnson SU', and the Generalized Hyperbolic. Also included is the semi-parametric piecewise distribution ('spd') with pareto tails and kernel interior.
 License: GPL-2
 Encoding: UTF-8
 LazyData: true
 RoxygenNote: 7.3.1
 VignetteBuilder: knitr
+RdMacros: Rdpack
 URL: https://www.nopredict.com/packages/tsdistributions, https://github.com/tsmodels/tsdistributions
 Suggests: 
     knitr, rmarkdown, testthat (>= 3.0.0)

NAMESPACE

@@ -1,19 +1,28 @@
 # Generated by roxygen2: do not edit by hand
 
 S3method(AIC,tsdistribution.estimate)
+S3method(AIC,tsdistribution.spdestimate)
 S3method(BIC,tsdistribution.estimate)
+S3method(BIC,tsdistribution.spdestimate)
 S3method(bread,tsdistribution.estimate)
+S3method(bread,tsdistribution.spdestimate)
 S3method(coef,tsdistribution.estimate)
+S3method(coef,tsdistribution.spdestimate)
 S3method(estfun,tsdistribution.estimate)
+S3method(estimate,tsdistribution.spdspec)
 S3method(estimate,tsdistribution.spec)
 S3method(logLik,tsdistribution.estimate)
+S3method(logLik,tsdistribution.spdestimate)
+S3method(print,summary.spd)
 S3method(print,summary.tsdistribution)
 S3method(print,summary.tsdistribution.profile)
 S3method(summary,tsdistribution.estimate)
 S3method(summary,tsdistribution.profile)
+S3method(summary,tsdistribution.spdestimate)
 S3method(tsmoments,tsdistribution.estimate)
 S3method(tsprofile,tsdistribution.spec)
 S3method(vcov,tsdistribution.estimate)
+S3method(vcov,tsdistribution.spdestimate)
 export(authorized_domain)
 export(ddist)
 export(dged)
@@ -27,6 +36,7 @@ export(dnig)
 export(dsged)
 export(dskewness)
 export(dsnorm)
+export(dspd)
 export(dsstd)
 export(dstd)
 export(ghyptransform)
@@ -39,6 +49,7 @@ export(pjsu)
 export(pnig)
 export(psged)
 export(psnorm)
+export(pspd)
 export(psstd)
 export(pstd)
 export(qdist)
@@ -49,6 +60,7 @@ export(qjsu)
 export(qnig)
 export(qsged)
 export(qsnorm)
+export(qspd)
 export(qsstd)
 export(qstd)
 export(rdist)
@@ -59,19 +71,28 @@ export(rjsu)
 export(rnig)
 export(rsged)
 export(rsnorm)
+export(rspd)
 export(rsstd)
 export(rstd)
+export(spd_modelspec)
 import(data.table)
 import(methods)
 import(tsmethods)
 importFrom(GeneralizedHyperbolic,ghypMom)
 importFrom(GeneralizedHyperbolic,rghyp)
+importFrom(KernSmooth,bkde)
+importFrom(Rdpack,reprompt)
 importFrom(Rsolnp,solnp)
 importFrom(SkewHyperbolic,pskewhyp)
 importFrom(SkewHyperbolic,qskewhyp)
 importFrom(TMB,MakeADFun)
 importFrom(future,"%<-%")
 importFrom(future.apply,future_lapply)
+importFrom(mev,dgp)
+importFrom(mev,fit.gpd)
+importFrom(mev,pgp)
+importFrom(mev,qgp)
+importFrom(mev,rgp)
 importFrom(progressr,handlers)
 importFrom(progressr,progressor)
 importFrom(sandwich,bread)
@@ -81,6 +102,7 @@ importFrom(sandwich,vcovHAC)
 importFrom(sandwich,vcovOPG)
 importFrom(stats,AIC)
 importFrom(stats,BIC)
+importFrom(stats,approx)
 importFrom(stats,ar)
 importFrom(stats,coef)
 importFrom(stats,dnorm)
@@ -91,13 +113,16 @@ importFrom(stats,na.omit)
 importFrom(stats,nlminb)
 importFrom(stats,optim)
 importFrom(stats,pnorm)
+importFrom(stats,ppoints)
 importFrom(stats,printCoefmat)
 importFrom(stats,qnorm)
+importFrom(stats,rexp)
 importFrom(stats,rnorm)
 importFrom(stats,runif)
 importFrom(stats,sd)
 importFrom(stats,spline)
 importFrom(stats,uniroot)
+importFrom(stats,var)
 importFrom(stats,vcov)
 importFrom(utils,tail)
 importFrom(zoo,coredata)

NEWS.md

@@ -1,3 +1,14 @@
+# tsdistributions 1.0.1
+
+* Added the semi-parametric piece-wise distribution (`spd`). As this is a special
+type of distribution, it gets its own class for specification and estimation. 
+Added p, d, q, r functions and other methods (summary, vcov, coef etc).
+* Fixed the noRemap Additional Issue on CRAN checks (renames error to Rf_error
+in C++ code).
+* Added a unit test for the spd in the test-quantile.R file.
+* Added an html vignette for the spd.
+
+
 # tsdistributions 1.0.0
 
 * Initial CRAN submission.
@@ -40,4 +51,4 @@ distributions used signum function but this distribution seems to have missed th
 0 case for some reason.
 * Added vcov methods and other sandwich estimators (QMLE, OP, NW).
 * Added BIC and AIC and created a print method for the summary.
-* Fixed some code fomatting issues
+* Fixed some code formatting issues

R/moments.R

@@ -8,7 +8,7 @@
 #' @param shape  shape parameter.
 #' @param lambda additional shape parameter for the Generalized Hyperbolic
 #' distribution.
-#' @returns A numeric value.
+#' @returns A numeric value for the skewness and excess kurtosis.
 #' @rdname dskewness
 #' @export
 #'

R/pdqr.R

@@ -32,7 +32,7 @@
 #' @param lower_tail if TRUE (default), probabilities are \eqn{P[X \le x]} otherwise, \eqn{P[X > x]}.
 #' @returns d gives the density, p gives the distribution function, q gives the quantile function
 #' and r generates random deviates. Output depends on x or q length, or n for the random number
-#' generator
+#' generator.
 #' @rdname ghst
 #' @export
 #'
@@ -171,7 +171,7 @@ qghst <- function(p, mu = 0, sigma = 1, skew = 1, shape = 8, lower_tail = TRUE,
 #' @param lower_tail if TRUE (default), probabilities are \eqn{P[X \le x]} otherwise, \eqn{P[X > x]}.
 #' @returns d gives the density, p gives the distribution function, q gives the quantile function
 #' and r generates random deviates. Output depends on x or q length, or n for the random number
-#' generator
+#' generator.
 #' @rdname snorm
 #' @export
 #'
@@ -279,7 +279,7 @@ rsnorm <- function(n, mu = 0, sigma = 1, skew = 1.5)
 #' @param lower_tail if TRUE (default), probabilities are \eqn{P[X \le x]} otherwise, \eqn{P[X > x]}.
 #' @returns d gives the density, p gives the distribution function, q gives the quantile function
 #' and r generates random deviates. Output depends on x or q length, or n for the random number
-#' generator
+#' generator.
 #' @rdname ged
 #' @export
 #'
@@ -388,7 +388,7 @@ rged <- function(n, mu = 0, sigma = 1, shape = 2)
 #' @param lower_tail if TRUE (default), probabilities are \eqn{P[X \le x]} otherwise, \eqn{P[X > x]}.
 #' @returns d gives the density, p gives the distribution function, q gives the quantile function
 #' and r generates random deviates. Output depends on x or q length, or n for the random number
-#' generator
+#' generator.
 #' @rdname sged
 #' @export
 #'
@@ -507,7 +507,7 @@ Heaviside <- function(x, a = 0)
 #' @param lower_tail if TRUE (default), probabilities are \eqn{P[X \le x]} otherwise, \eqn{P[X > x]}.
 #' @returns d gives the density, p gives the distribution function, q gives the quantile function
 #' and r generates random deviates. Output depends on x or q length, or n for the random number
-#' generator
+#' generator.
 #' @rdname std
 #' @export
 #'
@@ -616,7 +616,7 @@ rstd <- function(n, mu = 0, sigma = 1, shape = 5)
 #' @param lower_tail if TRUE (default), probabilities are \eqn{P[X \le x]} otherwise, \eqn{P[X > x]}.
 #' @returns d gives the density, p gives the distribution function, q gives the quantile function
 #' and r generates random deviates. Output depends on x or q length, or n for the random number
-#' generator
+#' generator.
 #' @rdname sstd
 #' @export
 #'
@@ -873,7 +873,7 @@ qghyp <- function(p, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = 1, lower_t
 #' @param lower_tail if TRUE (default), probabilities are \eqn{P[X \le x]} otherwise, \eqn{P[X > x]}.
 #' @return d gives the density, p gives the distribution function, q gives the quantile function
 #' and r generates random deviates. Output depends on x or q length, or n for the random number
-#' generator
+#' generator.
 #' @rdname gh
 #' @export
 #'
@@ -967,7 +967,7 @@ rgh <- function(n, mu = 0, sigma = 1, skew = 0, shape = 1, lambda = 1)
 #' @param lower_tail if TRUE (default), probabilities are \eqn{P[X \le x]} otherwise, \eqn{P[X > x]}.
 #' @returns d gives the density, p gives the distribution function, q gives the quantile function
 #' and r generates random deviates. Output depends on x or q length, or n for the random number
-#' generator
+#' generator.
 #' @rdname nig
 #' @export
 #'
@@ -1032,7 +1032,7 @@ rnig <- function(n, mu = 0, sigma = 1, skew = 0, shape = 1)
 #' @param lower_tail if TRUE (default), probabilities are \eqn{P[X \le x]} otherwise, \eqn{P[X > x]}.
 #' @returns d gives the density, p gives the distribution function, q gives the quantile function
 #' and r generates random deviates. Output depends on x or q length, or n for the random number
-#' generator
+#' generator.
 #' @rdname jsu
 #' @export
 #'
@@ -1243,7 +1243,7 @@ ghyptransform <- function(mu = 0, sigma = 1,  skew = 0, shape = 3, lambda = -0.5
 #' @param lower_tail if TRUE (default), probabilities are \eqn{P[X \le x]} otherwise, \eqn{P[X > x]}.
 #' @returns d gives the density, p gives the distribution function, q gives the quantile function
 #' and r generates random deviates. Output depends on x or q length, or n for the random number
-#' generator
+#' generator.
 #' @rdname ddist
 #' @export
 #'

R/sandwich.R

@@ -3,7 +3,7 @@
 #'
 #' @param x an object of class \dQuote{tsdistribution.estimate}.
 #' @param ... not currently used.
-#' @returns The score matrix
+#' @returns The score matrix.
 #' @details The function returns the scores of likelihood at the optimal solution.
 #' @method estfun tsdistribution.estimate
 #' @aliases estfun
@@ -18,6 +18,19 @@ estfun.tsdistribution.estimate <- function(x, ...)
 }
 
 
+#' @method bread tsdistribution.spdestimate
+#' @aliases bread
+#' @rdname bread.tsdistribution.estimate
+#' @author Alexios Galanos
+#' @export
+#'
+bread.tsdistribution.spdestimate <- function(x, ...)
+{
+    H <- bdiag(x$gpd$lower$hessian, x$gpd$upper$hessian)
+    return(H)
+}
+
+
 #' Bread Method
 #'
 #' @param x an object of class \dQuote{tsdistribution.estimate}.