Victor Lachos

Victor Lachos

20 packages on CRAN

ALDqr

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

BayesCR

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Propose a parametric fit for censored linear regression models based on SMSN distributions, from a Bayesian perspective. Also, generates SMSN random variables.

qrLMM

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Quantile regression (QR) for Linear Mixed-Effects Models via the asymmetric Laplace distribution (ALD). It uses the Stochastic Approximation of the EM (SAEM) algorithm for deriving exact maximum likelihood estimates and full inference results for the fixed-effects and variance components. It also provides graphical summaries for assessing the algorithm convergence and fitting results.

mixsmsn

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Functions to fit finite mixture of scale mixture of skew-normal (FM-SMSN) distributions.

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Fits linear regression models for censored spatial data. Provides different estimation methods as the SAEM (Stochastic Approximation of Expectation Maximization) algorithm and seminaive that uses Kriging prediction to estimate the response at censored locations and predict new values at unknown locations. Also offers graphical tools for assessing the fitted model.

tlmec

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Fit a linear mixed effects model for censored data with Student-t or normal distributions. The errors are assumed independent and identically distributed.

qrNLMM

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Quantile regression (QR) for Nonlinear Mixed-Effects Models via the asymmetric Laplace distribution (ALD). It uses the Stochastic Approximation of the EM (SAEM) algorithm for deriving exact maximum likelihood estimates and full inference results for the fixed-effects and variance components. It also provides graphical summaries for assessing the algorithm convergence and fitting results.

TTmoment

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Computing the first two moments of the truncated multivariate t (TMVT) distribution under the double truncation. Appling the slice sampling algorithm to generate random variates from the TMVT distribution.

ald

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It provides the density, distribution function, quantile function, random number generator, likelihood function, moments and Maximum Likelihood estimators for a given sample, all this for the three parameter Asymmetric Laplace Distribution defined in Koenker and Machado (1999). This is a special case of the skewed family of distributions available in Galarza et.al. (2017) <doi:10.1002/sta4.140> useful for quantile regression.

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It estimates the parameters of a partially linear regression censored model via maximum penalized likelihood through of ECME algorithm. The model belong to the semiparametric class, that including a parametric and nonparametric component. The error term considered belongs to the scale-mixture of normal (SMN) distribution, that includes well-known heavy tails distributions as the Student-t distribution, among others. To examine the performance of the fitted model, case-deletion and local influence techniques are provided to show its robust aspect against outlying and influential observations. This work is based in Ferreira, C. S., & Paula, G. A. (2017) <doi:10.1080/02664763.2016.1267124> but considering the SMN family.

StempCens

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It estimates the parameters of a censored or missing data in spatio-temporal models using the SAEM algorithm (Delyon et al., 1999 <doi:10.1214/aos/1018031103>). This algorithm is a stochastic approximation of the widely used EM algorithm and an important tool for models in which the E-step does not have an analytic form. Besides the expressions obtained to estimate the parameters to the proposed model, we include the calculations for the observed information matrix using the method developed by Louis (1982) <https://www.jstor.org/stable/2345828>. To examine the performance of the fitted model, case-deletion measure are provided.

ARCensReg

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It fits an univariate left or right censored linear regression model with autoregressive errors under the normal distribution. It provides estimates and standard errors of the parameters, prediction of future observations and it supports missing values on the dependent variable. It also performs influence diagnostic through local influence for three possible perturbation schemes.

CensMixReg

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Fit censored linear regression models where the random errors follow a finite mixture of Scale Mixture Normal distributions. Fit censored linear models of finite mixture multivariate Student-t and Normal distributions. Fit censored mixture regression models based on scale mixture of normal distributions.

CensRegMod

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Fits univariate censored linear regression model under Normal or Student-t distribution

FMsmsnReg

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

ARpLMEC

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It fits left, right or interval censored mixed-effects linear model with autoregressive errors of order p using the EM algorithm. It provides estimates, standard errors of the parameters and prediction of future observations.

nlsmsn

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Fit univariate non-linear scale mixture of skew-normal(NL-SMSN) regression.

lqr

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It fits a robust linear quantile regression model using a new family of zero-quantile distributions for the error term as in Galarza et.al.(2017) <doi:10.1002/sta4.140>. 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.

SMNCensReg

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Fit univariate right, left or interval censored regression model under the scale mixture of normal distributions

MomTrunc

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It computes the raw moments for the folded and truncated multivariate normal, Skew-normal (SN), Extended skew normal (ESN) and Student's t-distribution. It also offers specific functions to compute the mean and variance-covariance matrix as well as the cumulative distribution function (cdf) for the folded normal, SN, ESN, and folded t-distribution. Algorithms are extensions based on Kan, R., & Robotti, C. (2017) <doi:10.1080/10618600.2017.1322092>.