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Tilted and Data-Sharpened Nonparametric Density Estimation

install.packages("tiltdens")
CRAN 30-day downloads, method
518+350% up on the 30 days before, the better direction
Test coverage, method
88%of lines run by tests

High-order nonparametric density estimators built by perturbing a conventional kernel estimator, either by re-weighting the observations ("tilting") or by moving them ("data sharpening").

Full description

High-order nonparametric density estimators built by perturbing a conventional kernel estimator, either by re-weighting the observations ("tilting") or by moving them ("data sharpening"). The perturbation is chosen so that the estimator inherits the fast convergence rate of an infinite-order kernel estimator, such as the sinc or trapezoidal flat-top estimator, while remaining a proper non-negative density without the oscillatory tails those estimators suffer from. Two criteria are provided: minimising the L2 distance to an infinite-order comparator, following Doosti and Hall (2016) <doi:10.1111/rssb.12112>, and minimising a cross-validation criterion that needs no comparator and is much faster, following Doosti, Hall and Mateu (2018) <doi:10.1016/j.jspi.2017.12.003>.

Maintainer
Hassan Doosti
Latest release
2 Oct 2026
First release
21 Sep 2026

Compare with another package

More details
Releases
3
Needs compilation
No
Needs R
>= 3.5.0
Config/testthat/edition
3
Language
en-GB
RoxygenNote
7.3.1
Subjects (1) Method, Subjects
Cite

Package

Run in R for the authors' preferred citation:

citation("tiltdens")
APA
Doosti, H. (2026). tiltdens: Tilted and Data-Sharpened Nonparametric Density Estimation (Version 0.2.1) [Computer software]. https://doi.org/10.32614/CRAN.package.tiltdens
About

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

The repository also keeps a CITATION.cff.

R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA
Balamuta, J. J. (2026). R Observatory: Metrics for tiltdens version 0.2.1 [Data set]. HJJB, LLC. Data release v2026-10-28. https://doi.org/10.5281/zenodo.21843040
About

From data release v2026-10-28, which the citation names so these numbers can be found later.

All 13 check platforms pass.

  • r-devel-linux-x86_64-debian-clang

    The unreleased R on Linux, x86_64, debian-clang

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    Version checked
    0.2.1
    Time taken
    368 s
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    0.2.1
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    The unreleased R on Windows, x86_64

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    Version checked
    0.2.1
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    516 s
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    0.2.1
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    The previous release of R on macOS, x86_64

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    0.2.1
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    The previous release of R on Windows, x86_64

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    Version checked
    0.2.1
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    The patched release of R on Linux, x86_64

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    0.2.1
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    359 s
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    The current release of R on Linux, x86_64

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  • 13 OK

Check results on CRAN

Total downloads in the last 30 days
528It is the sum of 2 channels: CRAN and r2u. autoCRAN was counted over another window, so it is not added.

CRAN rank

5,941 of 26,530

CRAN

518 downloads in the last 30 days

Rank
5,941 of 26,530
Change
+350% up on the 30 days before, the better direction

It is in the total. Take it out

r2u

10 downloads in the last 30 days

Rank
6,605 of 30,526

It is in the total. Take it out

autoCRAN

0 downloads in the last 7 days

It is not added because it has no figure for the last 30 days.

Over time

Compare downloads

Needs
3
Dependent packages
0
Suggested by
0
Needs (3)
Installing it adds
1 package, 35 KB of source
Of those, need compilation
1
Optional extras (3)
Functions
85
Public exports
18
Lines of R code
3,098
Median complexity
3
Functions (85)
Size and composition

Lines by kind (5,529)

KindLinesShare
R code3,09856%
Tests98418%
Documentation1,12420%
Guides3236%

Lines by language

LanguageLinesShare
R4,08274%
Rd1,12420%
Vignettes3236%

Languages are counted from file extensions.

Files
64
R functions
85
Object systems
S3: 11 methods
Complexity
Median
3
Mean
4.15
Highest
20
Median lines per function
15
About complexity

Cyclomatic complexity counts the independent paths through a function: 1 is a straight line, higher is harder to test. R functions only.

Call graph figures
R functions
85
Calls
149
Unconnected
10
Groups
1
Growth (3 releases)
Total lines by release
4,831 on 21 Sep 20265,529 on 2 Oct 2026
Release changes

The tests ran and coverage was measured.

Lines run by tests
88.3%
Expressions run by tests
87.7%
Test cases
84
Exports named in tests
94.4%
Coverage by file (16)
Coverage run
Lines counted
1,267
Lines the tests ran
1,119
Tests passed
Yes
Measured with
covr 3.6.5 on R 4.6.1
Test suite
Has tests
Yes
Test frameworks
testthat
Test cases
84
Lines of tests
984
Test lines per line of code
0.32
Exports documented
100%
Arguments documented
77.3%
Return value documented
100%
Help pages with examples
73.7%
Examples run in checks
100%
Guides (1)
Other documentation
README
No
Release notes file
Yes
Help pages
19
Lines of documentation
1,124
  • Hassan Doosti

    Maintainer since 0.1.1 (2026), author, copyright holder

DoostiH / tiltdens on github.com

Stars
0
Forks
0
Open issues
0
Last commit
2 Oct 2026
Activity
Commits
12
Open pull requests
0
Tagged releases
1
Latest tagged release
v0.2.1, 21 Oct 2026
Issues closed in
–
Pull requests closed in
–
Pull requests merged
–
Created
9 Sep 2026
Licence file reads as
–
Development practices (5)
KindFound
Continuous integrationGitHub Actions, R CMD check workflow
The R CMD check workflow runs on Linux, macOS and Windows, and on R-devel
Governance and communityContributing guide
CRAN release processcran-comments.md
Research and citationCITATION.cff

Method, repository practices

Across DoostiH

R releases
  • On CRAN
  • Release
VersionReleasedR
0.2.1 current2 Oct 20264.6.1
0.2.028 Sep 20264.6.1
0.1.121 Sep 20264.6.1
Release notes (0.2.1)
  • make_kernel() now checks that the kernel is a symmetric probability density and stops with an explanation if it is not; check = FALSE builds an unsuitable kernel for inspection only. tilt_density(), tilt_density_cv(), tilt_cv() and sharpen_density() refuse such a kernel, so it cannot be used for estimation by mistake.
  • Kernel objects gain summary() and plot() methods. summary() returns the properties check_kernel() reports, as an object; check_kernel() is now a thin wrapper that prints it.