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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.

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Version

Install

install.packages('dsdp')

Monthly Downloads

174

Version

0.1.1

License

MIT + file LICENSE

Maintainer

Satoshi Kakihara

Last Published

February 11th, 2023

Functions in dsdp (0.1.1)

estimate.expmodel

Estimate Exponential-based model expmodel
datastats

Compute the mean and the standard deviation of a data set
databinning

Reduce a data set to representatives of bins and their frequencies
dsdp

dsdp: Density Estimation using Semidefinite Programming
eval_poly

Evaluate a polynomial
exp_est

Estimate coefficients of a polynomial in Exponential-based Model
estimate.gaussmodel

Estimate Gaussian-based model gaussmodel
estimate

Generic Method for estimation
cdf_gaussmodel

Cumulative distribution function of Gaussian-based model
cdf_expmodel

Cumulative distribution function of Expomemtial-based model
func.expmodel

Return the evaluation of a vector with Exponential-based model
mix2gauss

Datasets of Mixture of 2 Gaussian Distributions
func.gaussmodel

Return the evaluation of a vector with Gaussian-based model
gaussmodel

Constructor for S3 class gaussmodel
gauss_est

Estimate coefficients of a polynomial in Gaussian-based model
histmean

Compute the mean of a data set
igammac

Complementary Incomplete Gamma Function
igamma

Incomplete Gamma Function
expmodel

Constructor for S3 class expmodel
mix2gaussHist

Dataset of Mixture of 2 Gaussian Distributions: Histogram version
mixexpgamma

Dataset of Mixture of Exponential Distribution and Gamma Distribution
mixExpGammaHist

Dataset of Mixture of Exponential Distribution and Gamma Distribution: Histogram Version
func

Generic Method for evaluate the estimate
mix3gauss_fun

A density function of mixed gaussian distribution
mix2gauss_fun

A density function of mixed Gaussian distributions
mixexpgamma_fun

A density function of Mixed Exponential and Gamma Distributions
mix3gauss_gen

Generate Mixed Gaussian Random Numbers
pdf_gaussmodel

Probability density function of Gaussian-based model
mixexpgamma_gen

Generate random numbers of Mixed Exponential and Gamma Distributions
pdf_expmodel

Probability density function of Exponential-based model
plot.gaussmodel

Plot a histogram and estimated densities/distributions of Gaussian-based model object
plot.expmodel

Plot a histogram and estimated densities/distributions of Exponential-based model object
printf

printf
polyaxb

Substitute a coefficient of polynomial
summary.gaussmodel

Summary of Gaussian-based model gaussmodel object
mix2gauss_gen

Generate mixed Gaussian random numbers
mix3gauss

Datasets of Mixture of 3 Gaussian Distributions
summary.expmodel

Summary of Exponential-based expmodel object.