Curriculum Vitae

Google Scholar Citation

Web of Science ResearcherID

List of Publications (Updated: August, 24th, 2023)

Packages for R

  1. mixsmsn: Fitting finite mixture of scale mixture of skew-normal distributions (2010)
  2. tlmec: Linear Student-t Mixed-Effects Models with Censored Data (2011)
  3. nlsmsn: Fitting univariate non-linear scale mixture of skew-normal regression models. (2012)
  4. CensRegMod: Fitting Normal and Student-t censored regression models. (2012)
  5. SMNCensReg: Fitting univariate censored regression model under the scale mixture of normal distributions. (2013)
  6. ALDqr: Quantile Regression Using Asymmetric Laplace Distribution. (2013)
  7. BayesCR: Bayesian analysis of censored linear regression models with scale mixtures of normal (SMN) distributions (2013)
  8. qrLMM: Quantile Regression for Linear Mixed-Effects Models (2015)
  9. ald: The Asymmetric Laplace Distribution (2015)
  10. CensMixReg: Censored Linear Mixture Regression Models (2015)
  11. lqr: Robust Linear Quantile Regression (2016)
  12. FMsmsnReg: Regression Models with Finite Mixtures of Skew Heavy-Tailed Errors (2016)
  13. ARCensReg: Fitting Univariate Censored Linear Regression Model with Autoregressive Errors (2016)
  14. CensSpatial: Censored Spatial Models (2016)
  15. MomTrunc: Moments of Folded and Doubly Truncated Multivariate Distributions (2018)
  16. PartCensReg: Partially Censored Regression Models Based on Heavy-Tailed Distributions (2018)
  17. StempCens: Spatio-Temporal Estimation and Prediction for Censored/Missing Responses (2019)
  18. CensMFM: Finite Mixture of Multivariate Censored/Missing Data (2019)
  19. skewlmm: Scale Mixture of Skew-Normal Linear Mixed Models (2020)
  20. OBASpatial: Objective Bayesian Analysis for Spatial Regression Models (2020)

Submitted/in Progress

  1. Zhong, K., Zhang P., Castro, L.M.,  and Lachos, V.H. (2023). Bayesian analysis of autoregressive linear mixed models for censored responses using the multivariate t distribution. (In Progress)
  2. R. Retnam, S. Srivastava, D. Bandyopadhyay, and V.H. Lachos (2023). A divide-and-conquer EM algorithm for large non-Gaussian longitudinal data with irregular follow-ups. (In Progress)
  3. Jorge L. Bazán, Marcos O. Prates, V. H. Lachos, C. L. Azevedo (2023). A new class of binary regression model for unbalanced data with applications in medical data. (In Progress)
  4. K.S. Conceição, M.G. Andrade, V.H. Lachos & N. Ravishanker (2023).  k-Modified Distributions for Count Data. (In Progress)
  5. Fusheng Yang and V.H. Lachos (2023). Comparison of Zero-Inflated and Hurdle INAR(1) Processes for Modeling Count Data (In progress)
  6. Galarza, C. and Lachos, V.H. (2023+). Finite mixture modeling of censored and missing data using the multivariate skew-t distribution. (In progress)
  7. D.C.R. Oliveira, D. Liu & V.H. Lachos (2023). The use of the EM algorithm for regularization problems in high-dimensional censored linear mixed-effects models. (In progress).
  8. Brisilda Nbreka, D. Dey and VH Lachos (2023). Bayesian Estimation of Contagion Effect: An Application of Friendship Networks and Alcohol Behavior (In progress).
  9. Ordonez, J.A, Galarza, C. E. and Lachos, V.H. (2022). Spatial Censored Regression Models in R: The CensSpatial Package.  Preprint arXiv:2110.05570 (Submitted).
  10. F.L. Schumacher, L.A. Matos & V.H. Lachos (2022). skewlmm: An R Package for Fitting Skewed and Heavy-Tailed Linear Mixed Models. (submitted)  
  11. Medina, F., Garay, A.W. and Lachos, V.H. (2022). Bayesian analysis of censored/missing regression models with autoregressive errors and symmetrical distributions. (Submitted).
  12. M.S. Oliveira, C. Galarza, M.O. Prates & V.H. Lachos (2023). Influence Diagnostics for Heckman selection-t Models. (Submitted). 
  13. D.C.R. Oliveira, F. Schumacher & V.H. Lachos (2023). The use of the EM algorithm for regularization problems in high-dimensional linear mixed-effects models. (Submitted).
  14. D. Liu, D.C.R. Oliveira,  V.H. Lachos and L.M. Castro (2023). Lasso regularization for censored regression and high dimensional predictors. (Submitted).