Package: sshist 0.2.6

sshist: Optimal Density Estimation via Shimazaki-Shinomoto Method

Implements the Shimazaki-Shinomoto method for optimizing the bin width of histograms and the bandwidth of kernel density estimators. The framework minimizes the expected Mean Integrated Squared Error (MISE) and supports both 1D and 2D distributions, fixed and locally adaptive estimators, bootstrap confidence intervals, and 'OpenMP'-accelerated 'C++' 'backends'. Ideally suited for time-dependent rate estimation and identifying intrinsic data structures. For more details see Shimazaki and Shinomoto (2007) <doi:10.1162/neco.2007.19.6.1503> and Shimazaki and Shinomoto (2010) <doi:10.1007/s10827-009-0180-4>.

Authors:Daniil Popov [aut, cre]

sshist_0.2.6.tar.gz
sshist_0.2.6.zip(r-4.7-x86_64)sshist_0.2.6.zip(r-4.7-arm64)sshist_0.2.6.zip(r-4.6-x86_64)sshist_0.2.6.zip(r-4.6-arm64)sshist_0.2.6.zip(r-4.5-x86_64)
sshist_0.2.6.tgz(r-4.6-x86_64)sshist_0.2.6.tgz(r-4.6-arm64)sshist_0.2.6.tgz(r-4.5-x86_64)sshist_0.2.6.tgz(r-4.5-arm64)
sshist_0.2.6.tar.gz(r-4.7-arm64)sshist_0.2.6.tar.gz(r-4.7-x86_64)sshist_0.2.6.tar.gz(r-4.6-arm64)sshist_0.2.6.tar.gz(r-4.6-x86_64)
sshist_0.2.6.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
sshist/json (API)

# Install 'sshist' in R:
install.packages('sshist', repos = c('https://celebithil.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/celebithil/sshist/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

cppopenmp

5.08 score 2 stars 300 downloads 6 exports 2 dependencies

Last updated from:c58a56a595. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK124
linux-devel-x86_64OK156
source / vignettesOK239
linux-release-arm64OK100
linux-release-x86_64OK175
macos-release-arm64OK115
macos-release-x86_64OK238
macos-oldrel-arm64OK116
macos-oldrel-x86_64OK337
windows-devel-arm64OK276
windows-devel-x86_64OK146
windows-release-arm64OK223
windows-release-x86_64OK114
windows-oldrel-x86_64OK114
wasm-releaseOK95

Exports:sshistsshist_2dsskernelsskernel2dssvkernelssvkernel2d

Dependencies:bootRcpp

Introduction to sshist
1. Optimal 1D Histogram: sshist | Comparison with the standard approach | The S3 object | 2. Optimal 1D Kernel Density: sskernel & ssvkernel | Fixed Global Bandwidth (sskernel) | Locally Adaptive Bandwidth (ssvkernel) | Estimating Uncertainty with Bootstrap | 3. Optimal 2D Density Estimation | 2D Histogram: sshist_2d | 2D Kernel Density: sskernel2d and ssvkernel2d | References
Rendered fromintroduction.Rmdusingknitr::rmarkdown

Last update: 2026-09-29
Started: 2026-02-09

Advanced Density Visualization with ggplot2
1. Univariate (1D) Visualizations | 1.1 Optimal 1D Histogram | 1.2 Fixed vs. Adaptive 1D Kernel Density (sskernel & ssvkernel) | 1.3 Comparing Window Functions (WinFunc) in Adaptive KDE | 2. Bivariate (2D) Visualizations | 2.1 Optimal 2D Histogram (sshist_2d) | 2.2 Fixed vs. Adaptive 2D Kernel Density (sskernel2d & ssvkernel2d)
Rendered fromggplot2_visualization.Rmdusingknitr::rmarkdown

Last update: 2026-07-14
Started: 2026-07-09