dsdp0.1.1 package

Density Estimation with Semidefinite Programming

mix3gauss_fun

A density function of mixed gaussian distribution

mix3gauss_gen

Generate Mixed Gaussian Random Numbers

mixexpgamma_fun

A density function of Mixed Exponential and Gamma Distributions

mixexpgamma_gen

Generate random numbers of Mixed Exponential and Gamma Distributions

cdf_expmodel

Cumulative distribution function of Expomemtial-based model

cdf_gaussmodel

Cumulative distribution function of Gaussian-based model

databinning

Reduce a data set to representatives of bins and their frequencies

datastats

Compute the mean and the standard deviation of a data set

dsdp

dsdp: Density Estimation using Semidefinite Programming

estimate.expmodel

Estimate Exponential-based model expmodel

estimate.gaussmodel

Estimate Gaussian-based model gaussmodel

estimate

Generic Method for estimation

eval_poly

Evaluate a polynomial

exp_est

Estimate coefficients of a polynomial in Exponential-based Model

expmodel

Constructor for S3 class expmodel

func.expmodel

Return the evaluation of a vector with Exponential-based model

func.gaussmodel

Return the evaluation of a vector with Gaussian-based model

func

Generic Method for evaluate the estimate

gauss_est

Estimate coefficients of a polynomial in Gaussian-based model

gaussmodel

Constructor for S3 class gaussmodel

histmean

Compute the mean of a data set

igamma

Incomplete Gamma Function

igammac

Complementary Incomplete Gamma Function

mix2gauss_fun

A density function of mixed Gaussian distributions

mix2gauss_gen

Generate mixed Gaussian random numbers

pdf_expmodel

Probability density function of Exponential-based model

pdf_gaussmodel

Probability density function of Gaussian-based model

plot.expmodel

Plot a histogram and estimated densities/distributions of Exponential-...

plot.gaussmodel

Plot a histogram and estimated densities/distributions of Gaussian-bas...

polyaxb

Substitute a coefficient of polynomial

printf

printf

summary.expmodel

Summary of Exponential-based expmodel object.

summary.gaussmodel

Summary of Gaussian-based model gaussmodel object

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/>.

  • Maintainer: Satoshi Kakihara
  • License: MIT + file LICENSE
  • Last published: 2023-02-11