Release 1.0.0 (#45)

* bump version * add weights argument (#29) * remove unused variable (#26) * fix edf computation for deg = 2 * bump version + update docs * remove package cache on appveyor * weighted bandwidth selection * weighted fitting * adjust R interface * add unit tests * a few more sanity checks * update docs * update docs * fix devtools install link * stabilize weighted quantiles * ensure scale != 0 in case of too many ties * remove stripping of debug symbols * prepare release * update docs * allow already jittered input to be treated as continuous * some improvements to interpolation (#32) * use sample quantiles as grid points * fix unweighted quantile computation * improve safety of integral computations. * make sure that weighted version is consistent * improve grid extensions * fix boundary handling for bounded domain * fix interpolation bug when grid distance is small * install gsl * extend grid a bit further * fix bug for weighted quantile with zero weight * decrease lower bound for interpolation, related to vinecopulib/rvinecopulib#157 * More numerical stability (#35) * hybrid quantile/equidist grid * normalize weights * fix weighted influence and use it as safeguard * avoid double computation of influence fot deg = 0 * interpolant positivity and smooth extrapolation (#37) * switch to linear interpolation if result is negative * use positive constraints for spline coefficients * Gaussian tail for extrapolation * optimal bandwidths for all degrees (#38) * missing inline * fix levels in pkde1d * stabilize edf computation * tabs = 2 spaces (#39) * simplified, deterministic jittering (#40) * tabs = 2 spaces * simplified, deterministic jittering * prepare standalone c++ version with interface (#41) * rename lpdens -> kde1d * use namespaces * bw selection in Kde1d class * jittering + nan handling in cpp * statistical functions * constructor from interpolation grid * all discrete functionality in cpp * kde1d dir structure * seperate wrappers from interface * adapt R code * go to office * fix nans in grid * fix boundary transform order * refactor unit tests * Fast integration (#42) * rename lpdens -> kde1d * use namespaces * bw selection in Kde1d class * jittering + nan handling in cpp * statistical functions * constructor from interpolation grid * all discrete functionality in cpp * kde1d dir structure * seperate wrappers from interface * adapt R code * go to office * fix nans in grid * fix boundary transform order * refactor unit tests * unnormalized algorithm * faster integration by sorting input * Fit models with the fast fourier transform (#43) * isolate KdeFFT class * almost done with fft * proper weights in edf calculation * fix grid related issues * eliminate dead code * safer integration * reorder arguments for the feels * prepare release (#44) * Release 0.4.0 (#31) * bump version * add weights argument (#29) * remove unused variable (#26) * fix edf computation for deg = 2 * bump version + update docs * remove package cache on appveyor * weighted bandwidth selection * weighted fitting * adjust R interface * add unit tests * a few more sanity checks * update docs * update docs * fix devtools install link * stabilize weighted quantiles * ensure scale != 0 in case of too many ties * remove stripping of debug symbols * prepare release * update docs * bump version * update NEWS * update docs * update NEWS and DESCRIPTION * update API docs * backwards compatibility with bad code in rvineocpulib *sigh* * initialization order warning * typo in API doc * CRAN comments * API docs one more time
tnagler · Nov 15, 2019 · 5eaf81f · 5eaf81f
1 parent 5502fec
commit 5eaf81f
Show file tree

Hide file tree

Showing 56 changed files with 3,012 additions and 1,994 deletions.
diff --git a/.gitignore b/.gitignore
@@ -4,3 +4,4 @@
 .Ruserdata
 revdep
 /revdep/.cache.rds
+.vscode
diff --git a/.travis.yml b/.travis.yml
@@ -27,6 +27,7 @@ before_install:
   - sudo apt-get install gcc-5 g++-5 gfortran-5
   - sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-5 100
   - sudo update-alternatives --install /usr/bin/gfortran gfortran /usr/bin/gfortran-5 100
+  - sudo apt-get install libgsl-dev
 
 repos:
   CRAN: http://cran.rstudio.com

diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: kde1d
 Type: Package
 Title: Univariate Kernel Density Estimation
-Version: 0.4.0
+Version: 1.0.0
 Authors@R: c(
         person("Thomas", "Nagler",, "mail@tnagler.com", role = c("aut", "cre")),
         person("Thibault", "Vatter",, "thibault.vatter@gmail.com", role = c("aut"))
@@ -20,13 +20,12 @@ LinkingTo:
     Rcpp,
     RcppEigen
 Imports:
-    cctools, 
     graphics,
     Rcpp, 
     qrng,
     stats,
     utils
-RoxygenNote: 6.1.1
+RoxygenNote: 7.0.0
 Suggests: 
     testthat
 URL: https://github.com/tnagler/kde1d

diff --git a/NAMESPACE b/NAMESPACE
@@ -6,13 +6,12 @@ S3method(plot,kde1d)
 S3method(print,kde1d)
 S3method(summary,kde1d)
 export(dkde1d)
+export(equi_jitter)
 export(kde1d)
 export(pkde1d)
 export(qkde1d)
 export(rkde1d)
 importFrom(Rcpp,sourceCpp)
-importFrom(cctools,cont_conv)
-importFrom(cctools,expand_as_numeric)
 importFrom(graphics,lines)
 importFrom(graphics,plot)
 importFrom(qrng,ghalton)

diff --git a/NEWS.md b/NEWS.md
@@ -1,3 +1,28 @@
+# kde1d 1.0.0
+
+NEW FEATURES
+
+  * optimal plug-in bandwidth selection for all polynomial degrees (#38).
+
+  * avoid randomness through simplified, deterministic jittering, see 
+  `equi_jitter()` (#40).
+
+  * removed dependency `cctools`.
+
+  * headers in `inst/include` can be used as standalone C++ library with 
+    convenience wrappers for R (#41).
+
+  * (several times) faster `pkde1d()`, `qkde1d()`, and `rkde1d()` due to
+    a more clever algorithm for numerical integration (#42).
+
+  * faster `kde1d()` thanks to the Fast Fourier Transform (#43).
+
+BUG FIXES
+
+  * improvements to numerical stability, inter- and extrapolation (#32, #35, 
+  #37).
+
+
 # kde1d 0.4.0
 
 NEW FEATURE

diff --git a/R/RcppExports.R b/R/RcppExports.R
@@ -3,7 +3,9 @@
 
 #' fits a kernel density estimate and calculates the effective degrees of
 #' freedom.
-#' @param x vector of observations.
+#' @param x vector of observations; catergorical data must be converted to
+#'   non-negative integers.
+#' @param nlevels the number of factor levels; 0 for continuous data.
 #' @param bw the bandwidth parameter.
 #' @param xmin lower bound for the support of the density, `NaN` means no
 #'   boundary.
@@ -13,49 +15,35 @@
 #' @return `An Rcpp::List` containing the fitted density values on a grid and
 #'   additional information.
 #' @noRd
-fit_kde1d_cpp <- function(x, bw, xmin, xmax, deg, weights) {
-    .Call('_kde1d_fit_kde1d_cpp', PACKAGE = 'kde1d', x, bw, xmin, xmax, deg, weights)
+fit_kde1d_cpp <- function(x, nlevels, bw, mult, xmin, xmax, deg, weights) {
+    .Call('_kde1d_fit_kde1d_cpp', PACKAGE = 'kde1d', x, nlevels, bw, mult, xmin, xmax, deg, weights)
 }
 
 #' computes the pdf of a kernel density estimate by interpolation.
 #' @param x vector of evaluation points.
-#' @param R_object the fitted object passed from R.
+#' @param kde1d_r the fitted object passed from R.
 #' @return a vector of pdf values.
 #' @noRd
-dkde1d_cpp <- function(x, R_object) {
-    .Call('_kde1d_dkde1d_cpp', PACKAGE = 'kde1d', x, R_object)
+dkde1d_cpp <- function(x, kde1d_r) {
+    .Call('_kde1d_dkde1d_cpp', PACKAGE = 'kde1d', x, kde1d_r)
 }
 
 #' computes the cdf of a kernel density estimate by numerical integration.
 #' @param x vector of evaluation points.
-#' @param R_object the fitted object passed from R.
+#' @param kde1d_r the fitted object passed from R.
 #' @return a vector of cdf values.
 #' @noRd
-pkde1d_cpp <- function(x, R_object) {
-    .Call('_kde1d_pkde1d_cpp', PACKAGE = 'kde1d', x, R_object)
+pkde1d_cpp <- function(q, kde1d_r) {
+    .Call('_kde1d_pkde1d_cpp', PACKAGE = 'kde1d', q, kde1d_r)
 }
 
 #' computes the quantile of a kernel density estimate by numerical inversion
 #' (bisection method).
 #' @param x vector of evaluation points.
-#' @param R_object the fitted object passed from R.
+#' @param kde1d_r the fitted object passed from R.
 #' @return a vector of quantiles.
 #' @noRd
-qkde1d_cpp <- function(x, R_object) {
-    .Call('_kde1d_qkde1d_cpp', PACKAGE = 'kde1d', x, R_object)
-}
-
-#' @param x vector of observations
-#' @param grid_size number of equally-spaced points over which binning is
-#' performed to obtain kernel functional approximation
-#' @param weights vector of weights for each observation (can be empty).
-#' @return the selected bandwidth
-#' @noRd
-select_bw_cpp <- function(x, bw, mult, discrete, weights) {
-    .Call('_kde1d_select_bw_cpp', PACKAGE = 'kde1d', x, bw, mult, discrete, weights)
-}
-
-quan <- function(x, a, w) {
-    .Call('_kde1d_quan', PACKAGE = 'kde1d', x, a, w)
+qkde1d_cpp <- function(p, kde1d_r) {
+    .Call('_kde1d_qkde1d_cpp', PACKAGE = 'kde1d', p, kde1d_r)
 }
 
diff --git a/R/jitter.R b/R/jitter.R
@@ -0,0 +1,45 @@
+#' Conditionally equidistant jittering
+#'
+#' Converts ordered variables to numeric and Adds deterministic uniform noise.
+#' See *Details*.
+#'
+#' Jittering makes discrete variables continuous by adding noise. This simple
+#' trick allows to consistently estimate densities with tools designed for the
+#' continuous case (see, Nagler, 2018a/b). The drawback is that estimates are
+#' random and the noise may deteriorate the estimate by chance.
+#'
+#' Here, we add a form of deterministic noise that makes estimators well
+#' behaved. Tied occurences of a factor level are spread out uniformly
+#' (i.e., equidistantly) on the interval \eqn{[-0.5, 0.5]}. This is similar to
+#' adding random noise that is uniformly distributed, conditional on the
+#' observed outcome. Integrating over the outcome, one can check that the
+#' unconditional noise distribution is also uniform on \eqn{[-0.5, 0.5]}.
+#'
+#' Asymptotically, the deterministic jittering variant is equivalent to the
+#' random one.
+#'
+#' @param x observations; the function does nothing if `x` is already numeric.
+#'
+#' @export
+#'
+#' @references
+#' Nagler, T. (2018a). *A generic approach to nonparametric function estimation
+#' with mixed data.* Statistics & Probability Letters, 137:326–330,
+#' [arXiv:1704.07457](https://arxiv.org/abs/1704.07457)
+#'
+#' Nagler, T. (2018b). *Asymptotic analysis of the jittering kernel density
+#' estimator.* Mathematical Methods of Statistics, in press,
+#' [arXiv:1705.05431](https://arxiv.org/abs/1705.05431)
+#'
+#' @examples
+#' x <- as.factor(rbinom(10, 1, 0.5))
+#' equi_jitter(x)
+equi_jitter <- function(x) {
+  if (is.numeric(x))
+    return(x)
+  x <- as.numeric(x)
+  tab <- table(x)
+  noise <- unname(unlist(lapply(tab, function(l) -0.5 + 1:l / (l + 1))))
+  s <- sort(x, index.return = TRUE)
+  (s$x + noise)[rank(x, ties.method = "first", na.last = "keep")]
+}