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CensMFM  

Finite Mixture of Multivariate Censored/Missing Data
View on CRAN: Click here


Download and install CensMFM package within the R console
Install from CRAN:
install.packages("CensMFM")

Install from Github:
library("remotes")
install_github("cran/CensMFM")

Install by package version:
library("remotes")
install_version("CensMFM", "3.1")



Attach the package and use:
library("CensMFM")
Maintained by
Francisco H. C. de Alencar
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-10-16
Latest Update: 2024-05-14
Description:
It fits finite mixture models for censored or/and missing data using several multivariate distributions. Point estimation and asymptotic inference (via empirical information matrix) are offered as well as censored data generation. Pairwise scatter and contour plots can be generated. Possible multivariate distributions are the well-known normal, Student-t and skew-normal distributions. This package is an complement of Lachos, V. H., Moreno, E. J. L., Chen, K. & Cabral, C. R. B. (2017) for the multivariate skew-normal case.
How to cite:
Francisco H. C. de Alencar (2019). CensMFM: Finite Mixture of Multivariate Censored/Missing Data. R package version 3.1, https://cran.r-project.org/web/packages/CensMFM. Accessed 10 Oct. 2026.
Previous versions and publish date:
1.4 (2019-10-16 13:50), 1.5 (2019-10-23 22:10), 2.0 (2020-01-18 06:10), 2.11 (2020-05-02 16:20), 3.0 (2023-01-30 15:00), (2026-07-09 08:00)
Other packages that cited CensMFM R package
View CensMFM citation profile
Other R packages that CensMFM depends, imports, suggests or enhances
Complete documentation for CensMFM
Functions, R codes and Examples using the CensMFM R package
Some associated functions: CensMFM-package . fit.FMMSNC . rMMSN.contour . rMMSN . rMSN . 
Some associated R codes: AUX_prob_opt.R . AUX_tlrmvnmvt_adapter.R . EM.MSNC.R . USER_fit.FMMSNC.R . USER_rMMSN.R . USER_rMMSN.contour.R . USER_rMSN.R . functions.fm.MSNC.R . generating_aux.R . im.new.R .  Full CensMFM package functions and examples
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