Package: dsdp 0.1.1

Satoshi Kakihara

dsdp: Density Estimation with Semidefinite Programming

The models of probability density functions are Gaussian or exponential distributions with polynomial correction terms. Using a maximum likelihood method, 'dsdp' computes parameters of Gaussian or exponential distributions together with degrees of polynomials by a grid search, and coefficient of polynomials by a variant of semidefinite programming. It adopts Akaike Information Criterion for model selection. See a vignette for a tutorial and more on our 'Github' repository <https://github.com/tsuchiya-lab/dsdp/>.

Authors:Satoshi Kakihara [aut, cre], Takashi Tsuchiya [aut]

dsdp_0.1.1.tar.gz
dsdp_0.1.1.tar.gz(r-4.5-noble)dsdp_0.1.1.tar.gz(r-4.4-noble)
dsdp_0.1.1.tgz(r-4.4-emscripten)dsdp_0.1.1.tgz(r-4.3-emscripten)
dsdp.pdf |dsdp.html✨
dsdp/json (API)
NEWS

# Install 'dsdp' in R:
install.packages('dsdp', repos = 'https://cloud.r-project.org')

Bug tracker:https://github.com/tsuchiya-lab/dsdp/issues0 issues

Pkgdown site:https://tsuchiya-lab.github.io

Uses libs:
  • openblas– Optimized BLAS
  • fortran– Runtime library for GNU Fortran applications
Datasets:
  • mix2gauss - Datasets of Mixture of 2 Gaussian Distributions
  • mix2gaussHist - Dataset of Mixture of 2 Gaussian Distributions: Histogram version
  • mix3gauss - Datasets of Mixture of 3 Gaussian Distributions
  • mixExpGammaHist - Dataset of Mixture of Exponential Distribution and Gamma Distribution: Histogram Version
  • mixexpgamma - Dataset of Mixture of Exponential Distribution and Gamma Distribution

On CRAN:

Conda:

fortranopenblas

2.70 score 183 downloads 24 exports 28 dependencies

Last updated 2 years agofrom:53f709d862. Checks:1 OK, 2 WARNING. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 24 2025
R-4.5-linux-x86_64WARNINGMar 24 2025
R-4.4-linux-x86_64WARNINGMar 24 2025

Exports:cdf_expmodelcdf_gaussmodeldatabinningdatastatsestimateeval_polyexp_estexpmodelfuncgauss_estgaussmodelhistmeanigammaigammacmix2gauss_funmix2gauss_genmix3gauss_funmix3gauss_genmixexpgamma_funmixexpgamma_genpdf_expmodelpdf_gaussmodelpolyaxbprintf

Dependencies:clicolorspacefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerrlangscalestibbleutf8vctrsviridisLitewithr

A Tutorial for dsdp

Satoshi Kakihara^[National Graduate Institute for Policy Studies, skakihara@gmail.com], Takashi Tsuchiya^[National Graduate Institute for Policy Studies, tsuchiya@grips.ac.jp]

Rendered fromTutorial.Rmdusingknitr::rmarkdownon Mar 24 2025.

Last update: 2022-12-05
Started: 2022-12-05

Readme and manuals

dsdp

The goal of dsdp is to estimate probability density functions from a data set using a maximum likelihood method. The models of density functions in use are familiar Gaussian or exponential distributions with polynomial correction terms. To find an optimal model, we adopt a grid search for parameters of base functions and degrees of polynomials, together with semidefinite programming for coefficients of polynomials, and then model selection is done by Akaike Information Criterion.

Installation

## Install from CRAN
install.packages(dsdp)

You can install the development version of dsdp from this repository:

## Install from github
devtools::install_github("tsuchiya-lab/dsdp")

To install from source codes, the user needs an appropriate compiler toolchain, for example, rtools in Windows, to build dsdp, along with devtools package.

Usage

Please refer to the tutorial and the reference in tsuchiya-lab.github.io/dsdp/.

Pdf version of articles are also available: A Tutorial for dsdp, Problem Formulations for dsdp.

Acknowledgements

This research was supported in part with Grant-in-Aid for Scientific Research(B) JP18H03206, JP21H03398 and Grant-in-Aid for Exploratory Research JP20K21792 from the Japan Society for the Promotion of Sciences.

Help Manual

Help pageTopics
Cumulative distribution function of Expomemtial-based modelcdf_expmodel
Cumulative distribution function of Gaussian-based modelcdf_gaussmodel
Reduce a data set to representatives of bins and their frequenciesdatabinning
Compute the mean and the standard deviation of a data setdatastats
dsdp: Density Estimation using Semidefinite Programmingdsdp
Generic Method for estimationestimate
Estimate Exponential-based model 'expmodel'estimate.expmodel
Estimate Gaussian-based model 'gaussmodel'estimate.gaussmodel
Evaluate a polynomialeval_poly
Estimate coefficients of a polynomial in Exponential-based Modelexp_est
Constructor for S3 class 'expmodel'expmodel
Generic Method for evaluate the estimatefunc
Return the evaluation of a vector with Exponential-based modelfunc.expmodel
Return the evaluation of a vector with Gaussian-based modelfunc.gaussmodel
Estimate coefficients of a polynomial in Gaussian-based modelgauss_est
Constructor for S3 class 'gaussmodel'gaussmodel
Compute the mean of a data sethistmean
Incomplete Gamma Functionigamma
Complementary Incomplete Gamma Functionigammac
Datasets of Mixture of 2 Gaussian Distributionsmix2gauss
A density function of mixed Gaussian distributionsmix2gauss_fun
Generate mixed Gaussian random numbersmix2gauss_gen
Dataset of Mixture of 2 Gaussian Distributions: Histogram versionmix2gaussHist
Datasets of Mixture of 3 Gaussian Distributionsmix3gauss
A density function of mixed gaussian distributionmix3gauss_fun
Generate Mixed Gaussian Random Numbersmix3gauss_gen
Dataset of Mixture of Exponential Distribution and Gamma Distributionmixexpgamma
A density function of Mixed Exponential and Gamma Distributionsmixexpgamma_fun
Generate random numbers of Mixed Exponential and Gamma Distributionsmixexpgamma_gen
Dataset of Mixture of Exponential Distribution and Gamma Distribution: Histogram VersionmixExpGammaHist
Probability density function of Exponential-based modelpdf_expmodel
Probability density function of Gaussian-based modelpdf_gaussmodel
Plot a histogram and estimated densities/distributions of Exponential-based model objectplot.expmodel
Plot a histogram and estimated densities/distributions of Gaussian-based model objectplot.gaussmodel
Substitute a coefficient of polynomialpolyaxb
printfprintf
Summary of Exponential-based 'expmodel' object.summary.expmodel
Summary of Gaussian-based model 'gaussmodel' objectsummary.gaussmodel