Curriculum Vitae

Google Scholar Citation

Web of Science ResearcherID

List of Publications (Updated: July, 30th, 2022)

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. Schumacher, F. L., Lachos, V.H.  Castro, L.M.C. and Matos, L. A. (2021).  A censored time series model for responses in the unit interval. (submitted)
  2. Ordonez, J.A, Galarza, C. E. and Lachos, V.H. (2021). Spatial Censored Regression Models in R: The CensSpatial Package.  Preprint arXiv:2110.05570 (Submitted).
  3. K.S. Conceição, M.G. Andrade, V.H. Lachos & N. Ravishanker (2021).  k-Modified Distributions for Count Data. (Submitted)
  4. Padilla*, J.L., Azevedo, C.L. and Lachos, V.H. (2022). Parameter recovery for a skew multidimensional item response model: a comparison of MCMC algorithms and measurement of some effects of interest. (submitted)
  5. K. Zhong, R.C. Olivari, A.M. Garay and V.H. Lachos (2022). Mixed-effects models for censored data with autoregressive errors using the multivariate Student’s t-distribution. (Submitted)
  6. Ordoñez*, A.C., Prates, M.O.  Matos, L.A. and Lachos V.H. (2022). Objective Bayesian analysis for spatial Student-t regression models.  Preprint arXiv:2004.04341 (Submitted)
  7. Lachos, V.H.,  Galea-Rojas, M. and  Zeller, C.B. (2022). Likelihood inference-based for mixed-effects models using the generalized hyperbolic distribution. (Submitted)
  8. Park*, J., Dey, D. and Lachos V.H. (2022). Finite mixture of regression models for censored data based on the skew-t distribution. (submitted)
  9. F.L. Schumacher, L.A. Matos & V.H. Lachos (2022). Skewlmm: An R Package for Fitting Skewed and Heavy-Tailed Linear Mixed Models. (submitted)  
  10.  Valeriano, K.A.L.,  Galarza, C.E., Matos, L.A. and Lachos, V.H. (2022). Likelihood-based inference for multivariate skew-t censored regression models.
  11. Medina, F., Garay, A.W. and Lachos, V.H. (2022). Bayesian analysis of censored/missing regression models with autoregressive errors and  symmetrical distributions.
  12. Zhong, K., Zhang P., Castro, L.M.,  and Lachos, V.H. (2022). Bayesian analysis of autoregressive linear mixed models for censored responses using the multivariate t distribution.
  13. R. Retnam, S. Srivastava, D. Bandyopadhyay and V.H. Lachos (2022). A divide-and-conquer EM algorithm for large non-Gaussian longitudinal data with irregular followups.
  14. Jorge L. Bazán, Marcos O. Prates, V. H. Lachos, C. L. Azevedo (2022). A new class of binary regression model for unbalance data with applications in medical data.