From 3a569386698fe4c7f019a70594d7f605d9236de2 Mon Sep 17 00:00:00 2001 From: Thomas Nagler Date: Fri, 15 Nov 2019 18:56:21 +0100 Subject: [PATCH] Patch 1.0.1 (#48) * Code clean-up and safety (#46) * code safety * remove dependence on qrng * some cleanup * avoid lower > upper by reversing the grid at the right places * ensure non-NaN bandwidth * unused variable * add randtoolbox to Imports * Prepare patch (#47) * code safety * remove dependence on qrng * some cleanup * avoid lower > upper by reversing the grid at the right places * ensure non-NaN bandwidth * unused variable * add randtoolbox to Imports * update NEWS and DESCRIPTION * update API docs * update CRAN comments --- .Rbuildignore | 1 + DESCRIPTION | 4 +- NAMESPACE | 2 +- NEWS.md | 23 ++++++++-- R/kde1d-methods.R | 6 +-- cran-comments.md | 5 +++ docs/404.html | 4 +- docs/LICENSE-text.html | 4 +- docs/authors.html | 4 +- docs/index.html | 22 +++++----- docs/news/index.html | 37 +++++++++++++---- docs/pkgdown.yml | 4 +- docs/reference/dkde1d.html | 10 ++--- docs/reference/equi_jitter.html | 4 +- docs/reference/index.html | 4 +- docs/reference/kde1d-2.png | Bin 19022 -> 20071 bytes docs/reference/kde1d-package.html | 4 +- docs/reference/kde1d.html | 18 ++++---- docs/reference/plot.kde1d.html | 12 +++--- inst/include/kde1d/dpik.hpp | 3 ++ inst/include/kde1d/interpolation.hpp | 60 +++++++++++++-------------- inst/include/kde1d/kde1d.hpp | 28 +++++++------ inst/include/kde1d/kdefft.hpp | 11 +++-- inst/include/kde1d/stats.hpp | 3 +- inst/include/kde1d/tools.hpp | 43 ++++++++++--------- man/dkde1d.Rd | 2 +- 26 files changed, 183 insertions(+), 135 deletions(-) diff --git a/.Rbuildignore b/.Rbuildignore index ee36597..046136c 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -8,3 +8,4 @@ docs ^cran-comments\.md$ ^revdep$ +^.vscode$ diff --git a/DESCRIPTION b/DESCRIPTION index cdddcda..b2a06a3 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: kde1d Type: Package Title: Univariate Kernel Density Estimation -Version: 1.0.0 +Version: 1.0.1 Authors@R: c( person("Thomas", "Nagler",, "mail@tnagler.com", role = c("aut", "cre")), person("Thibault", "Vatter",, "thibault.vatter@gmail.com", role = c("aut")) @@ -22,7 +22,7 @@ LinkingTo: Imports: graphics, Rcpp, - qrng, + randtoolbox, stats, utils RoxygenNote: 7.0.0 diff --git a/NAMESPACE b/NAMESPACE index 95e7bcb..9c19c65 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -14,7 +14,7 @@ export(rkde1d) importFrom(Rcpp,sourceCpp) importFrom(graphics,lines) importFrom(graphics,plot) -importFrom(qrng,ghalton) +importFrom(randtoolbox,sobol) importFrom(stats,logLik) importFrom(stats,na.omit) importFrom(stats,qnorm) diff --git a/NEWS.md b/NEWS.md index 93c62cb..99a3eb1 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,13 +1,30 @@ +# kde1d 1.0.1 + +DEPENDENCIES + + * Removed dependence on `qrng` (#46). + +BUG FIXES + + * Fixed undefined behavior with potential to cause memory issues (#46). + + * Prevent rare `bw_ == NaN` cases (#46). + + * Fixed compiler warnings due to unused or uninitialized variables (#46). + + # kde1d 1.0.0 +DEPENDENCIES + + * removed dependency on `cctools`. + 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`. + `equi_jitter()` (#40). * headers in `inst/include` can be used as standalone C++ library with convenience wrappers for R (#41). diff --git a/R/kde1d-methods.R b/R/kde1d-methods.R index 17f04f3..65bdffb 100644 --- a/R/kde1d-methods.R +++ b/R/kde1d-methods.R @@ -88,10 +88,10 @@ qkde1d <- function(p, obj) { #' @param n integer; number of observations. #' @param quasi logical; the default (`FALSE`) returns pseudo-random #' numbers, use `TRUE` for quasi-random numbers (generalized Halton, see -#' [`qrng::ghalton()`]). +#' [`randtoolbox::sobol()`]). #' #' @rdname dkde1d -#' @importFrom qrng ghalton +#' @importFrom randtoolbox sobol #' @importFrom stats runif #' @export rkde1d <- function(n, obj, quasi = FALSE) { @@ -99,7 +99,7 @@ rkde1d <- function(n, obj, quasi = FALSE) { if (!quasi) { U <- runif(n) } else { - U <- ghalton(n, d = 1) + U <- randtoolbox::sobol(n, scrambling = 1, seed = sample.int(1e10, 1)) } # simulated data from KDE is the quantile transform of U qkde1d(U, obj) diff --git a/cran-comments.md b/cran-comments.md index a14c5c9..99f62be 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -1,4 +1,9 @@ +Patch to fix undefined behavior/heap overflow failures detected by the +sanitizers: https://cran.r-project.org/web/checks/check_results_kde1d.html + ## Test environments +* ubuntu 18.04 with valgrind (release) +* ubuntu 18.04 with clang ASAN/UBSAN on rocker (devel) * ubuntu 14.04 on travis-ci (release, devel, oldrel) * win-builder (devel, release) diff --git a/docs/404.html b/docs/404.html index 7580fdb..ee5a16c 100644 --- a/docs/404.html +++ b/docs/404.html @@ -67,7 +67,7 @@ kde1d - 1.0.0 + 1.0.1 @@ -123,7 +123,7 @@

Page not found (404)

-

Site built with pkgdown 1.4.1.9000.

+

Site built with pkgdown 1.4.1.

diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html index 8c567fb..fecd3f4 100644 --- a/docs/LICENSE-text.html +++ b/docs/LICENSE-text.html @@ -67,7 +67,7 @@ kde1d - 1.0.0 + 1.0.1 @@ -125,7 +125,7 @@

License

-

Site built with pkgdown 1.4.1.9000.

+

Site built with pkgdown 1.4.1.

diff --git a/docs/authors.html b/docs/authors.html index fe1e2bc..b076654 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -67,7 +67,7 @@ kde1d - 1.0.0 + 1.0.1 @@ -132,7 +132,7 @@

Authors

-

Site built with pkgdown 1.4.1.9000.

+

Site built with pkgdown 1.4.1.

diff --git a/docs/index.html b/docs/index.html index b9fe318..7f4bde4 100644 --- a/docs/index.html +++ b/docs/index.html @@ -36,7 +36,7 @@ kde1d - 1.0.0 + 1.0.1 @@ -99,7 +99,7 @@

  • the latest development version:
    • +devtools::install_github("tnagler/kde1d@dev")

    @@ -108,8 +108,8 @@

    Unbounded data
    x <- rnorm(100)                    # simulate data
-fit <- kde1d(x)                    # estimate density
-dkde1d(0, fit)                     # evaluate density estimate
+fit <- kde1d(x)                    # estimate density
+dkde1d(0, fit)                     # evaluate density estimate
 summary(fit)                       # information about the estimate
 plot(fit)                          # plot the density estimate
 curve(dnorm(x), add = TRUE,        # add true density
@@ -119,8 +119,8 @@ 
    
 
    
 Bounded data, log-linear
    
 
    x <- rgamma(100, shape = 1)        # simulate data
-fit <- kde1d(x, xmin = 0, deg = 1) # estimate density
-dkde1d(seq(0, 5, by = 1), fit)     # evaluate density estimate
+fit <- kde1d(x, xmin = 0, deg = 1) # estimate density
+dkde1d(seq(0, 5, by = 1), fit)     # evaluate density estimate
 summary(fit)                       # information about the estimate
 plot(fit)                          # plot the density estimate
 curve(dgamma(x, shape = 1),        # add true density
@@ -132,8 +132,8 @@ 
    
 Discrete data
    
 
    x <- rbinom(100, size = 5, prob = 0.5)  # simulate data
 x <- ordered(x, levels = 0:5)           # declare as ordered
-fit <- kde1d(x)                         # estimate density
-dkde1d(sort(unique(x)), fit)            # evaluate density estimate
+fit <- kde1d(x)                         # estimate density
+dkde1d(sort(unique(x)), fit)            # evaluate density estimate
 summary(fit)                            # information about the estimate
 plot(fit)                               # plot the density estimate
 points(ordered(0:5, 0:5),               # add true density
@@ -144,9 +144,9 @@ 
    
 Weighted estimate
    
 
    x <- rnorm(100)                         # simulate data
 weights <- rexp(100)                    # weights as in Bayesian bootstrap
-fit <- kde1d(x, weights = weights)      # weighted fit
+fit <- kde1d(x, weights = weights)      # weighted fit
 plot(fit)                               # compare with unweighted fit
-lines(kde1d(x), col = 2)
    
+lines(kde1d(x), col = 2)
    @@ -207,7 +207,7 @@

    Dev status

    -

    Site built with pkgdown 1.4.1.9000.

    +

    Site built with pkgdown 1.4.1.

    diff --git a/docs/news/index.html b/docs/news/index.html index cdf9ea8..f2969b6 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -67,7 +67,7 @@ kde1d - 1.0.0 + 1.0.1
    @@ -110,18 +110,36 @@

    Changelog

    Source: NEWS.md +
    +

    +kde1d 1.0.1 Unreleased +

    +

    DEPENDENCIES

    +
      +
    • Removed dependence on qrng package because it was to heavy (qrng <- copula <- gsl, #46).
      • +
    +

    BUG FIXES

    +
      +

    • Fixed undefined behavior with potential to cause memory issues (#46).

      • +

    • Prevent rare bw_ == NaN cases (#46).

      • +

    • Fixed compiler warnings due to unused or uninitialized variables (#46).

      • +
    +

    -kde1d 1.0.0 Unreleased +kde1d 1.0.0 2019-11-15

    +

    DEPENDENCIES

    +
      +
    • removed dependency on cctools.
      • +

    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.

      • +

    • avoid randomness through simplified, deterministic jittering, see equi_jitter() (#40).

    • 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).

      • +

    • (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

      @@ -134,7 +152,7 @@

      NEW FEATURE

      BUG FIX

        @@ -152,7 +170,7 @@

      BUG FIXES

        -

      • consistent behavior when dkde1d() and pkde1d() are called with non-ordered input although data are discrete (#19).

        • +

      • consistent behavior when dkde1d() and pkde1d() are called with non-ordered input although data are discrete (#19).

      • fixed bug in computation of kernel density estimates (#20).

      • adapt minimum bw allowed for discrete data to truncated Gaussian kernel (#20).

      @@ -185,6 +203,7 @@

      Contents

      -

      Site built with pkgdown 1.4.1.9000.

      +

      Site built with pkgdown 1.4.1.

      diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 2079fee..8999c6f 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -1,5 +1,5 @@ pandoc: 2.3.1 -pkgdown: 1.4.1.9000 -pkgdown_sha: 61c3999702966dd75267b98ce905df22521ebd11 +pkgdown: 1.4.1 +pkgdown_sha: ~ articles: [] diff --git a/docs/reference/dkde1d.html b/docs/reference/dkde1d.html index a05c856..9a347b3 100644 --- a/docs/reference/dkde1d.html +++ b/docs/reference/dkde1d.html @@ -70,7 +70,7 @@ kde1d - 1.0.0 + 1.0.1

    @@ -154,7 +154,7 @@

    Arg quasi

    logical; the default (FALSE) returns pseudo-random numbers, use TRUE for quasi-random numbers (generalized Halton, see -qrng::ghalton()).

    +randtoolbox::sobol()).

    @@ -172,12 +172,12 @@

    Details is the length of the numerical argument for the other functions.

    See also

    -

    kde1d()

    +

    kde1d()

    Examples

    set.seed(0) # for reproducibility
 x <- rnorm(100) # simulate some data
-fit <- kde1d(x) # estimate density
+fit <- kde1d(x) # estimate density
 dkde1d(0, fit) # evaluate density estimate (close to dnorm(0))
 pkde1d(0, fit) # evaluate corresponding cdf (close to pnorm(0))
    #> [1] 0.5008513
    qkde1d(0.5, fit) # quantile function (close to qnorm(0))
    #> [1] -0.001893213
    hist(rkde1d(100, fit)) # simulate
    @@ -201,7 +201,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.9000.

    +

    Site built with pkgdown 1.4.1.

    diff --git a/docs/reference/equi_jitter.html b/docs/reference/equi_jitter.html index a9f0425..908695d 100644 --- a/docs/reference/equi_jitter.html +++ b/docs/reference/equi_jitter.html @@ -70,7 +70,7 @@ kde1d - 1.0.0 + 1.0.1 @@ -177,7 +177,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.9000.

    +

    Site built with pkgdown 1.4.1.

    diff --git a/docs/reference/index.html b/docs/reference/index.html index 7dc1011..066f71f 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -67,7 +67,7 @@ kde1d - 1.0.0 + 1.0.1 @@ -174,7 +174,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.9000.

    +

    Site built with pkgdown 1.4.1.

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    Contents

    -

    Site built with pkgdown 1.4.1.9000.

    +

    Site built with pkgdown 1.4.1.

    diff --git a/docs/reference/kde1d.html b/docs/reference/kde1d.html index 438af5c..f0ec6d4 100644 --- a/docs/reference/kde1d.html +++ b/docs/reference/kde1d.html @@ -70,7 +70,7 @@ kde1d - 1.0.0 + 1.0.1 @@ -179,7 +179,7 @@

    Details log-transform is used if there is only one boundary (see, Geenens and Wang, 2018); a probit transform is used if there are two (see, Geenens, 2014).

    Discrete variables are handled via jittering (see, Nagler, 2018a, 2018b). -A specific form of deterministic jittering is used, see equi_jitter().

    +A specific form of deterministic jittering is used, see equi_jitter().

    References

    Geenens, G. (2014). Probit transformation for kernel density @@ -201,15 +201,15 @@

    R Statistical Society, Series B, 53, 683–690.

    See also

    -

    dkde1d(), pkde1d(), qkde1d(), rkde1d(), -plot.kde1d(), lines.kde1d()

    +

    dkde1d(), pkde1d(), qkde1d(), rkde1d(), +plot.kde1d(), lines.kde1d()

    Examples

    
 ## unbounded data
 x <- rnorm(500) # simulate data
 fit <- kde1d(x) # estimate density
-dkde1d(0, fit) # evaluate density estimate
+dkde1d(0, fit) # evaluate density estimate
 summary(fit) # information about the estimate
    #> kernel density estimate ('kde1d'), log-quadratic
 #> -----------------------------------------------------------------
 #> nobs = 500, bw = 0.71, loglik = -712.05, d.f. = 4.65
    plot(fit) # plot the density estimate
    curve(dnorm(x),
@@ -219,10 +219,10 @@ 
    Examp
 ## bounded data, log-linear
 x <- rgamma(500, shape = 1) # simulate data
 fit <- kde1d(x, xmin = 0, deg = 1) # estimate density
-dkde1d(seq(0, 5, by = 1), fit) # evaluate density estimate
+dkde1d(seq(0, 5, by = 1), fit) # evaluate density estimate
 summary(fit) # information about the estimate
    #> kernel density estimate ('kde1d'), log-linear with bounded support (xmin = 0)
 #> -----------------------------------------------------------------
-#> nobs = 500, bw = 0.3, loglik = -449.06, d.f. = 12.9
    plot(fit) # plot the density estimate
    curve(dgamma(x, shape = 1), # add true density
+#> nobs = 500, bw = 0.3, loglik = -450.93, d.f. = 12.89
    plot(fit) # plot the density estimate
    curve(dgamma(x, shape = 1), # add true density
   add = TRUE, col = "red",
   from = 1e-3
 )
    
@@ -230,7 +230,7 @@ 
    Examp
 x <- rbinom(500, size = 5, prob = 0.5) # simulate data
 x <- ordered(x, levels = 0:5) # declare as ordered
 fit <- kde1d(x) # estimate density
-dkde1d(sort(unique(x)), fit) # evaluate density estimate
+dkde1d(sort(unique(x)), fit) # evaluate density estimate
 summary(fit) # information about the estimate
    #> (jittered) kernel density estimate ('kde1d'), log-quadratic
 #> -----------------------------------------------------------------
 #> nobs = 500, bw = 1.12, loglik = -789.98, d.f. = 10.08
    plot(fit) # plot the density estimate
    points(ordered(0:5, 0:5), # add true density
@@ -264,7 +264,7 @@ 
    Contents
    
 
    
 
 
    
-  
    Site built with pkgdown 1.4.1.9000.
    
+  
    Site built with pkgdown 1.4.1.
    
 
    
 
       
diff --git a/docs/reference/plot.kde1d.html b/docs/reference/plot.kde1d.html
index d083bb4..e79632b 100644
--- a/docs/reference/plot.kde1d.html
+++ b/docs/reference/plot.kde1d.html
@@ -69,7 +69,7 @@
       
       
         kde1d
-        1.0.0
+        1.0.1
       
     
 
@@ -138,20 +138,20 @@ 
    Arg
 
     
    See also
    
 
-    
    kde1d()
    
+    
    kde1d()
    
 
     
    Examples
    
     
    ## continuous data
 x <- rbeta(100, shape1 = 0.3, shape2 = 0.4) # simulate data
-fit <- kde1d(x) # unbounded estimate
+fit <- kde1d(x) # unbounded estimate
 plot(fit, ylim = c(0, 4)) # plot estimate
    curve(dbeta(x, 0.3, 0.4), # add true density
   col = "red", add = TRUE
-)
    fit_bounded <- kde1d(x, xmin = 0, xmax = 1) # bounded estimate
+)
    fit_bounded <- kde1d(x, xmin = 0, xmax = 1) # bounded estimate
 lines(fit_bounded, col = "green")
    
 ## discrete data
 x <- rpois(100, 3) # simulate data
 x <- ordered(x, levels = 0:20) # declare variable as ordered
-fit <- kde1d(x) # estimate density
+fit <- kde1d(x) # estimate density
 plot(fit, ylim = c(0, 0.25)) # plot density estimate
    points(ordered(0:20, 0:20), # add true density values
   dpois(0:20, 3),
   col = "red"
@@ -175,7 +175,7 @@ 
    Contents
    
 
    
 
 
    
-  
    Site built with pkgdown 1.4.1.9000.
    
+  
    Site built with pkgdown 1.4.1.
    
 
    
 
       
diff --git a/inst/include/kde1d/dpik.hpp b/inst/include/kde1d/dpik.hpp
index 2dad465..d62586d 100644
--- a/inst/include/kde1d/dpik.hpp
+++ b/inst/include/kde1d/dpik.hpp
@@ -161,6 +161,9 @@ PluginBandwidthSelector::select_bw(size_t deg)
   } catch (...) {
     bw = 4.0 * 1.06 * scale_ * std::pow(n, -1.0 / (bwpow + 1));
   }
+  if (std::isnan(bw)) {
+    bw = 4.0 * 1.06 * scale_ * std::pow(n, -1.0 / (bwpow + 1));
+  }
 
   return bw;
 }
diff --git a/inst/include/kde1d/interpolation.hpp b/inst/include/kde1d/interpolation.hpp
index 7809f69..6c8cb84 100644
--- a/inst/include/kde1d/interpolation.hpp
+++ b/inst/include/kde1d/interpolation.hpp
@@ -1,8 +1,8 @@
 #pragma once
 
+#include "tools.hpp"
 #include 
 #include 
-#include "tools.hpp"
 
 namespace kde1d {
 
@@ -28,15 +28,13 @@ class InterpolationGrid1d
   Eigen::VectorXd integrate(const Eigen::VectorXd& u,
                             bool normalize = false) const;
 
-  Eigen::VectorXd get_values() const {return values_;}
-  Eigen::VectorXd get_grid_points() const {return grid_points_;}
+  Eigen::VectorXd get_values() const { return values_; }
+  Eigen::VectorXd get_grid_points() const { return grid_points_; }
 
 private:
   // Utility functions for spline Interpolation
-  double cubic_poly(const double& x,
-                    const Eigen::VectorXd& a) const;
-  double cubic_indef_integral(const double& x,
-                              const Eigen::VectorXd& a) const;
+  double cubic_poly(const double& x, const Eigen::VectorXd& a) const;
+  double cubic_indef_integral(const double& x, const Eigen::VectorXd& a) const;
   double cubic_integral(const double& lower,
                         const double& upper,
                         const Eigen::VectorXd& a) const;
@@ -47,7 +45,6 @@ class InterpolationGrid1d
   Eigen::MatrixXd values_;
 };
 
-
 //! Constructor
 //!
 //! @param grid_points an ascending sequence of grid points.
@@ -69,7 +66,8 @@ inline InterpolationGrid1d::InterpolationGrid1d(
 //! renormalizes the estimate to integrate to one
 //!
 //! @param times how many times the normalization routine should run.
-inline void InterpolationGrid1d::normalize(int times)
+inline void
+InterpolationGrid1d::normalize(int times)
 {
   double x_max = grid_points_(grid_points_.size() - 1);
   double int_max;
@@ -81,16 +79,14 @@ inline void InterpolationGrid1d::normalize(int times)
 
 //! Interpolation
 //! @param x vector of evaluation points.
-inline Eigen::VectorXd InterpolationGrid1d::interpolate(
-    const Eigen::VectorXd& x) const
+inline Eigen::VectorXd
+InterpolationGrid1d::interpolate(const Eigen::VectorXd& x) const
 {
   Eigen::VectorXd tmp_coefs(4);
-  ptrdiff_t m = grid_points_.size();
-
-  auto interpolate_one = [&] (const double& xx) {
+  auto interpolate_one = [&](const double& xx) {
     int k = find_cell(xx);
-    double xev = (xx - grid_points_(k)) /
-      (grid_points_(k + 1) - grid_points_(k));
+    double xev =
+      (xx - grid_points_(k)) / (grid_points_(k + 1) - grid_points_(k));
 
     // use Gaussian tail for extrapolation
     if (xev <= 0) {
@@ -109,8 +105,8 @@ inline Eigen::VectorXd InterpolationGrid1d::interpolate(
 //!
 //! @param x a vector  of evaluation points
 //! @param normalize whether to normalize the integral to a maximum value of 1.
-inline Eigen::VectorXd InterpolationGrid1d::integrate(const Eigen::VectorXd& x,
-                                                      bool normalize) const
+inline Eigen::VectorXd
+InterpolationGrid1d::integrate(const Eigen::VectorXd& x, bool normalize) const
 {
   Eigen::VectorXd res(x.size());
   auto ord = tools::get_order(x);
@@ -147,7 +143,7 @@ inline Eigen::VectorXd InterpolationGrid1d::integrate(const Eigen::VectorXd& x,
     }
 
     // integrate over partial cell
-    if (upr < grid_points_(m - 1)) {  // only if still in interior
+    if (upr < grid_points_(m - 1)) { // only if still in interior
       tmp_coefs = find_cell_coefs(k);
       tmp_eps = (grid_points_(k + 1) - grid_points_(k));
       upr = (upr - grid_points_(k)) / tmp_eps;
@@ -178,8 +174,8 @@ inline Eigen::VectorXd InterpolationGrid1d::integrate(const Eigen::VectorXd& x,
 //!
 //! @param x evaluation point.
 //! @param a polynomial coefficients
-inline double InterpolationGrid1d::cubic_poly(const double& x,
-                                              const Eigen::VectorXd& a) const
+inline double
+InterpolationGrid1d::cubic_poly(const double& x, const Eigen::VectorXd& a) const
 {
   double x2 = x * x;
   double x3 = x2 * x;
@@ -190,8 +186,9 @@ inline double InterpolationGrid1d::cubic_poly(const double& x,
 //!
 //! @param x evaluation point.
 //! @param a polynomial coefficients.
-inline double InterpolationGrid1d::cubic_indef_integral(
-        const double& x, const Eigen::VectorXd& a) const
+inline double
+InterpolationGrid1d::cubic_indef_integral(const double& x,
+                                          const Eigen::VectorXd& a) const
 {
   double x2 = x * x;
   double x3 = x2 * x;
@@ -204,16 +201,16 @@ inline double InterpolationGrid1d::cubic_indef_integral(
 //! @param lower lower limit of the integral.
 //! @param upper upper limit of the integral.
 //! @param a polynomial coefficients.
-inline double InterpolationGrid1d::cubic_integral(const double& lower,
-                                                  const double& upper,
-                                                  const Eigen::VectorXd& a)
-  const
+inline double
+InterpolationGrid1d::cubic_integral(const double& lower,
+                                    const double& upper,
+                                    const Eigen::VectorXd& a) const
 {
   return cubic_indef_integral(upper, a) - cubic_indef_integral(lower, a);
 }
 
-
-inline int InterpolationGrid1d::find_cell(const double& x0) const
+inline int
+InterpolationGrid1d::find_cell(const double& x0) const
 {
   int low = 0, high = grid_points_.size() - 1;
   int mid;
@@ -231,7 +228,8 @@ inline int InterpolationGrid1d::find_cell(const double& x0) const
 //! Calculate coefficients for cubic intrpolation spline
 //!
 //! @param k the cell index.
-inline Eigen::VectorXd InterpolationGrid1d::find_cell_coefs(const int& k) const
+inline Eigen::VectorXd
+InterpolationGrid1d::find_cell_coefs(const int& k) const
 {
   // indices for cell and neighboring grid points
   int k0 = std::max(k - 1, 0);
@@ -262,7 +260,7 @@ inline Eigen::VectorXd InterpolationGrid1d::find_cell_coefs(const int& k) const
 
   // ensure positivity (Schmidt and Hess, DOI:10.1007/bf01934097)
   dx1 = std::max(dx1, -3 * values_(k));
-  dx2 = std::min(dx2,  3 * values_(k2));
+  dx2 = std::min(dx2, 3 * values_(k2));
 
   // compute coefficents
   Eigen::VectorXd a(4);
diff --git a/inst/include/kde1d/kde1d.hpp b/inst/include/kde1d/kde1d.hpp
index abbcd05..8bce809 100644
--- a/inst/include/kde1d/kde1d.hpp
+++ b/inst/include/kde1d/kde1d.hpp
@@ -129,7 +129,7 @@ inline Kde1d::Kde1d(const Eigen::VectorXd& x,
   Eigen::VectorXd w = weights;
   tools::remove_nans(xx, w);
   if (w.size() > 0)
-    w /= w.sum();
+    w /= w.mean();
   if (nlevels_ > 0)
     xx = stats::equi_jitter(xx);
   xx = boundary_transform(xx);
@@ -155,7 +155,6 @@ inline Kde1d::Kde1d(const Eigen::VectorXd& x,
   loglik_ = grid_.interpolate(x).cwiseMax(1e-20).array().log().sum();
 
   // calculate effective degrees of freedom
-  double n = x.size();
   interp::InterpolationGrid1d infl_grid(
     grid_points, fitted.col(1).cwiseMin(2.0).cwiseMax(0), 0);
   edf_ = infl_grid.interpolate(x).sum();
@@ -353,7 +352,7 @@ Kde1d::fit_lp(const Eigen::VectorXd& x,
                                grid_points(0),
                                grid_points(m - 1),
                                m - 1,
-                               wbin);
+                               Eigen::VectorXd::Ones(x.size()));
     wbin = wcount.cwiseQuotient(count);
   }
 
@@ -411,7 +410,7 @@ Kde1d::calculate_infl(const size_t& n,
     M(1, 0) = M(0, 1);
     M(1, 1) = f0 * bw2 + f0 * bw2 * bw2 * b2;
     M_inverse00 = M.inverse()(0, 0);
-  } else if (deg_ == 2) {
+  } else {
     Eigen::Matrix3d M;
     M(0, 0) = f0;
     M(0, 1) = f0 * b;
@@ -457,7 +456,7 @@ Kde1d::boundary_transform(const Eigen::VectorXd& x, bool inverse)
       // two boundaries -> probit transform
       auto rng = xmax_ - xmin_;
       x_new = stats::pnorm(x).array() + xmin_ - 5e-5 * rng;
-      x_new *=  (xmax_ - xmin_ + 1e-4 * rng);
+      x_new *= (xmax_ - xmin_ + 1e-4 * rng);
     } else if (!std::isnan(xmin_)) {
       // left boundary -> log transform
       x_new = x.array().exp() + xmin_ - 1e-5;
@@ -499,7 +498,11 @@ Kde1d::boundary_correct(const Eigen::VectorXd& x, const Eigen::VectorXd& fhat)
     corr_term.fill(1.0);
   }
 
-  return fhat.array() * corr_term.array();
+  Eigen::VectorXd f_corr = fhat.cwiseProduct(corr_term);
+  if (std::isnan(xmin_) & !std::isnan(xmax_))
+    f_corr.reverseInPlace();
+
+  return f_corr;
 }
 
 //! constructs a grid later used for interpolation
@@ -510,12 +513,10 @@ Kde1d::construct_grid_points(const Eigen::VectorXd& x)
 {
   Eigen::VectorXd rng(2);
   rng << x.minCoeff(), x.maxCoeff();
-  if (std::isnan(xmin_) && std::isnan(xmax_))
-      rng(0) -= 4 * bw_;
-  if (std::isnan(xmax_))
-      rng(1) += 4 * bw_;
-  if (std::isnan(xmin_) && !std::isnan(xmax_))
-    std::swap(rng(0), rng(1));
+  if (std::isnan(xmin_) && std::isnan(xmax_)) {
+    rng(0) -= 4 * bw_;
+    rng(1) += 4 * bw_;
+  }
   auto zgrid = Eigen::VectorXd::LinSpaced(401, rng(0), rng(1));
   return boundary_transform(zgrid, true);
 }
@@ -525,6 +526,8 @@ Kde1d::construct_grid_points(const Eigen::VectorXd& x)
 inline Eigen::VectorXd
 Kde1d::finalize_grid(Eigen::VectorXd& grid_points)
 {
+  if (std::isnan(xmin_) & !std::isnan(xmax_))
+    grid_points.reverseInPlace();
   if (!std::isnan(xmin_))
     grid_points(0) = xmin_;
   if (!std::isnan(xmax_))
@@ -533,7 +536,6 @@ Kde1d::finalize_grid(Eigen::VectorXd& grid_points)
   return grid_points;
 }
 
-
 //  Bandwidth for Kernel Density Estimation
 //' @param x vector of observations
 //' @param bw bandwidth parameter, NA for automatic selection.
diff --git a/inst/include/kde1d/kdefft.hpp b/inst/include/kde1d/kdefft.hpp
index ca712e0..997d4ac 100644
--- a/inst/include/kde1d/kdefft.hpp
+++ b/inst/include/kde1d/kdefft.hpp
@@ -1,7 +1,7 @@
 #pragma once
 
-#include "tools.hpp"
 #include "stats.hpp"
+#include "tools.hpp"
 #include 
 
 namespace kde1d {
@@ -30,7 +30,7 @@ class KdeFFT
   Eigen::VectorXd bin_counts_;
   double lower_;
   double upper_;
-  size_t num_bins_{ 400 };
+  static constexpr size_t num_bins_{ 400 };
 };
 
 //! @param x vector of observations.
@@ -43,9 +43,9 @@ inline KdeFFT::KdeFFT(const Eigen::VectorXd& x,
                       double lower,
                       double upper,
                       const Eigen::VectorXd& weights)
-                      : bw_(bw)
-                      , lower_(lower)
-                      , upper_(upper)
+  : bw_(bw)
+  , lower_(lower)
+  , upper_(upper)
 {
   if (weights.size() > 0 && (weights.size() != x.size()))
     throw std::runtime_error("x and weights must have the same size");
@@ -59,7 +59,6 @@ inline KdeFFT::KdeFFT(const Eigen::VectorXd& x,
   bin_counts_ = tools::linbin(x, lower_, upper_, num_bins_, w);
 }
 
-
 //! Binned kernel density derivative estimate
 //! @param drv order of derivative.
 //! @return estimated derivative evaluated at the bin centers.
diff --git a/inst/include/kde1d/stats.hpp b/inst/include/kde1d/stats.hpp
index af16768..16dc113 100644
--- a/inst/include/kde1d/stats.hpp
+++ b/inst/include/kde1d/stats.hpp
@@ -1,13 +1,13 @@
 #pragma once
 
 #define BOOST_MATH_PROMOTE_DOUBLE_POLICY false
+#include "tools.hpp"
 #include 
 #include 
 #include 
 #include 
 #include 
 #include 
-#include "tools.hpp"
 
 namespace kde1d {
 
@@ -120,7 +120,6 @@ quantile(const Eigen::VectorXd& x,
 
   auto x2 = x;
   auto wcum = w;
-  auto wrank = Eigen::VectorXd::Constant(n, 0.0);
   double wacc = 0.0;
   for (size_t i = 0; i < n; ++i) {
     x2(i) = x(ind[i]);
diff --git a/inst/include/kde1d/tools.hpp b/inst/include/kde1d/tools.hpp
index d1d441f..00fd3a3 100644
--- a/inst/include/kde1d/tools.hpp
+++ b/inst/include/kde1d/tools.hpp
@@ -10,7 +10,8 @@ namespace tools {
 //! @param x function argument.
 //! @param func function to be applied.
 template
-Eigen::MatrixXd unaryExpr_or_nan(const Eigen::MatrixXd &x, const T& func)
+Eigen::MatrixXd
+unaryExpr_or_nan(const Eigen::MatrixXd& x, const T& func)
 {
   return x.unaryExpr([&func](double y) {
     if (std::isnan(y)) {
@@ -31,12 +32,12 @@ Eigen::MatrixXd unaryExpr_or_nan(const Eigen::MatrixXd &x, const T& func)
 //! @param n_iter the number of iterations for the bisection.
 //!
 //! @return \f$ f^{-1}(x) \f$.
-inline Eigen::VectorXd invert_f(
-  const Eigen::VectorXd &x,
-  std::function f,
-  const double lb,
-  const double ub,
-  int n_iter)
+inline Eigen::VectorXd
+invert_f(const Eigen::VectorXd& x,
+         std::function f,
+         const double lb,
+         const double ub,
+         int n_iter)
 {
   Eigen::VectorXd xl = Eigen::VectorXd::Constant(x.size(), lb);
   Eigen::VectorXd xh = Eigen::VectorXd::Constant(x.size(), ub);
@@ -51,12 +52,12 @@ inline Eigen::VectorXd invert_f(
   return x_tmp;
 }
 
-
 //! remove rows of a matrix which contain nan values or have zero weight
 //! @param x the matrix.
 //! @param a vector of weights that is either empty or whose size is equal to
 //!   the number of columns of x.
-inline void remove_nans(Eigen::VectorXd& x, Eigen::VectorXd& weights)
+inline void
+remove_nans(Eigen::VectorXd& x, Eigen::VectorXd& weights)
 {
   if ((weights.size() > 0) & (weights.size() != x.rows()))
     throw std::runtime_error("sizes of x and weights don't match.");
@@ -82,8 +83,8 @@ inline void remove_nans(Eigen::VectorXd& x, Eigen::VectorXd& weights)
     weights.conservativeResize(last + 1);
 }
 
-inline Eigen::Matrix get_order(
-    const Eigen::VectorXd& x)
+inline Eigen::Matrix
+get_order(const Eigen::VectorXd& x)
 {
   Eigen::Matrix order(x.size());
   for (size_t i = 0; i < x.size(); ++i)
@@ -91,27 +92,31 @@ inline Eigen::Matrix get_order(
   std::stable_sort(
     order.data(),
     order.data() + order.size(),
-    [&] (const size_t& a, const size_t& b) { return (x[a] < x[b]); }
-  );
+    [&](const size_t& a, const size_t& b) { return (x[a] < x[b]); });
   return order;
 }
 
 //! Computes bin counts for univariate data via the linear binning strategy.
 //! @param x vector of observations
 //! @param weights vector of weights for each observation.
-inline Eigen::VectorXd linbin(const Eigen::VectorXd& x,
-                              double lower,
-                              double upper,
-                              size_t num_bins,
-                              const Eigen::VectorXd& weights)
+inline Eigen::VectorXd
+linbin(const Eigen::VectorXd& x,
+       double lower,
+       double upper,
+       size_t num_bins,
+       const Eigen::VectorXd& weights)
 {
   Eigen::VectorXd gcnts = Eigen::VectorXd::Zero(num_bins + 1);
   double rem, lxi, delta;
+  size_t li;
+
+  if (upper <= lower)
+    throw std::runtime_error("upper != lower");
 
   delta = (upper - lower) / num_bins;
   for (size_t i = 0; i < x.size(); ++i) {
     lxi = (x(i) - lower) / delta;
-    size_t li = static_cast(lxi);
+    li = static_cast(lxi);
     rem = lxi - li;
     if (li < num_bins) {
       gcnts(li) += (1 - rem) * weights(i);
diff --git a/man/dkde1d.Rd b/man/dkde1d.Rd
index 187f7bf..2b5e6ed 100644
--- a/man/dkde1d.Rd
+++ b/man/dkde1d.Rd
@@ -30,7 +30,7 @@ rkde1d(n, obj, quasi = FALSE)
 
 \item{quasi}{logical; the default (\code{FALSE}) returns pseudo-random
 numbers, use \code{TRUE} for quasi-random numbers (generalized Halton, see
-\code{\link[qrng:ghalton]{qrng::ghalton()}}).}
+\code{\link[randtoolbox:sobol]{randtoolbox::sobol()}}).}
 }
 \value{
 The density, distribution function or quantile functions estimates