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Title: Quantile Maximization Likelihood Estimation and Bayesian Ex-Gaussian Estimation
Version: 0.1.2
Description: Presents two methods to estimate the parameters 'mu', 'sigma', and 'tau' of an ex-Gaussian distribution. Those methods are Quantile Maximization Likelihood Estimation ('QMLE') and Bayesian. The 'QMLE' method allows a choice between three different estimation algorithms for these parameters : 'neldermead' ('NEMD'), 'fminsearch' ('FMIN'), and 'nlminb' ('NLMI'). For more details about the methods you can refer at the following list: Brown, S., & Heathcote, A. (2003) <doi:10.3758/BF03195527>; McCormack, P. D., & Wright, N. M. (1964) <doi:10.1037/h0083285>; Van Zandt, T. (2000) <doi:10.3758/BF03214357>; El Haj, A., Slaoui, Y., Solier, C., & Perret, C. (2021) <doi:10.19139/soic-2310-5070-1251>; Gilks, W. R., Best, N. G., & Tan, K. K. C. (1995) <doi:10.2307/2986138>.
License: GPL-2
Encoding: UTF-8
LazyLoad: true
RoxygenNote: 7.2.3
Imports: pracma, stats, nloptr, invgamma, dlm, fitdistrplus, gamlss.dist
NeedsCompilation: no
Packaged: 2023-10-06 06:57:48 UTC; jean
Suggests: testthat (≥ 3.0.0)
Config/testthat/edition: 3
Author: Yousri SLAOUI ORCID iD [aut], Abir EL HAJ ORCID iD [aut, ctb], Alandra ZAKKOUR [aut], Caroline BORDES [aut, ctb], Cyril PERRET ORCID iD [aut], Jean DUMONCEL ORCID iD [aut, cre]
Maintainer: Jean DUMONCEL <jean.dumoncel@univ-poitiers.fr>
Repository: CRAN
Date/Publication: 2023-10-06 07:20:02 UTC

Bayesian Ex-gaussian Estimate

Description

Estimates the mu, sigma, and tau parameters of an ex-Gaussian distribution using a bayesian method.

Usage

BayesianExgaussian(n, x, nSamples = 5000, Ti = 2500)

Arguments

n

the data size

x

the data. Must be a vector, with no missing values

nSamples

number of Samples

Ti

burn-in

Value

BayesianExgaussian() returns an object "theta" which is a list with components: estimates of mu, sigma, and tau

References

Brown, S., & Heathcote, A. (2003). QMLE: Fast, robust, and efficient estimation of distribution functions based on quantiles. Behavior Research Methods, Instruments, & Computers, 35, 485-492.

McCormack, P. D., & Wright, N. M. (1964). The positive skew observed in reaction time distributions. Canadian Journal of Psychology/Revue canadienne de psychologie, 18, 43-51.

Van Zandt, T. (2000). How to fit a response time distribution. Psychonomic Bulletin & Review, 7, 424-465.

El Haj, A., Slaoui, Y., Solier, C., & Perret, C. (2021). Bayesian Estimation of The Ex-Gaussian distribution. Statistics, Optimization & Information Computing, 9(4), 809-819.

Gilks, W. R., Best, N. G., & Tan, K. K. C. (1995). Adaptive rejection Metropolis sampling within Gibbs sampling. Journal of the Royal Statistical Society: Series C (Applied Statistics), 44, 455-472.

Examples

library(gamlss.dist)
set.seed(2703)
data<-rexGAUS(n=100, mu = 500, sigma = 150, nu = 100)
BayesianExgaussian(n = 100, x = data)


Ex-Gaussian Quantile Maximum Likelihood Estimate

Description

Estimates the mu, sigma, and tau parameters of an ex-Gaussian distribution. 3 different algorithms can be used : neldermead ('NEMD'), fminsearch ('FMIN') and nlminb ('NLMI').

Usage

QMLEEstim(y, func)

Arguments

y

the data. Must be a vector with no missing values

func

the function selected for the estimation. 'NEMD' for neldermead, 'FMIN' for fminsearch, and 'NLMI' for nlminb.

Value

QMLEEstim() returns an object "valEstim" which is a list with components: estimates of mu, sigma, and tau

References

Brown, S., & Heathcote, A. (2003). QMLE: Fast, robust, and efficient estimation of distribution functions based on quantiles. Behavior Research Methods, Instruments, & Computers, 35, 485-492.

McCormack, P. D., & Wright, N. M. (1964). The positive skew observed in reaction time distributions. Canadian Journal of Psychology/Revue canadienne de psychologie, 18, 43-51.

Van Zandt, T. (2000). How to fit a response time distribution. Psychonomic Bulletin & Review, 7, 424-465.

El Haj, A., Slaoui, Y., Solier, C., & Perret, C. (2021). Bayesian Estimation of The Ex-Gaussian distribution. Statistics, Optimization & Information Computing, 9(4), 809-819.

Gilks, W. R., Best, N. G., & Tan, K. K. C. (1995). Adaptive rejection Metropolis sampling within Gibbs sampling. Journal of the Royal Statistical Society: Series C (Applied Statistics), 44, 455-472.

Examples

library(gamlss.dist)
set.seed(2703)
data<-rexGAUS(n=100, mu = 500, sigma = 150, nu = 100)
QMLEEstim(data, 'NEMD')


These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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