lbenitesanchez/ssmn

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2
Language
R
Created over 7 years ago
Updated over 7 years ago

lbenitesanchez/ssmn

lbenitesanchez

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Performs the EM algorithm for regression models using Skew Scale Mixtures of Normal Distributions.

ALDqr

EM algorithm for estimation of parameters and other methods in a quantile regression.

bssn

It provides the density, distribution function, quantile function, random number generator, reliability function, failure rate, likelihood function, moments and EM algorithm for Maximum Likelihood estimators, also empirical quantile and generated envelope for a given sample, all this for the three parameter Birnbaum-Saunders model based on Skew-Normal Distribution. Additionally, it provides the random number generator for the mixture of Birnbaum-Saunders model based on Skew-Normal distribution.

FMsmsnReg

Fit linear regression models where the random errors follow a finite mixture of of Skew Heavy-Tailed Errors.

CensMixReg

Fit censored linear regression models where the random errors follow a finite mixture of Normal or Student-t distributions. Fit censored linear models of finite mixture multivariate Student-t and Normal distributions.

ssmsn

It provides the density and random number generator for the Scale-Shape Mixtures of Skew-Normal Distributions proposed by Jamalizadeh and Lin (2016) <doi:10.1007/s00180-016-0691-1>.

lqr

It fits a robust linear quantile regression model using a new family of zero-quantile distributions for the error term. This family of distribution includes skewed versions of the Normal, Student's t, Laplace, Slash and Contaminated Normal distribution. It also performs logistic quantile regression for bounded responses as shown in Bottai et.al.(2009) <doi:10.1002/sim.3781>. It provides estimates and full inference. It also provides envelopes plots for assessing the fit and confidences bands when several quantiles are provided simultaneously.