Other packages > Find by keyword >

mcglm  

Multivariate Covariance Generalized Linear Models
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("mcglm", "0.9.0")



Attach the package and use:
library("mcglm")
Maintained by
Wagner Hugo Bonat
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-06-09
Latest Update:
Description:
Fitting multivariate covariance generalized linear models (McGLMs) to data. McGLM is a general framework for non-normal multivariate data analysis, designed to handle multivariate response variables, along with a wide range of temporal and spatial correlation structures defined in terms of a covariance link function combined with a matrix linear predictor involving known matrices. The models take non-normality into account in the conventional way by means of a variance function, and the mean structure is modelled by means of a link function and a linear predictor. The models are fitted using an efficient Newton scoring algorithm based on quasi-likelihood and Pearson estimating functions, using only second-moment assumptions. This provides a unified approach to a wide variety of different types of response variables and covariance structures, including multivariate extensions of repeated measures, time series, longitudinal, spatial and spatio-temporal structures. The package offers a user-friendly interface for fitting McGLMs similar to the glm() R function. See Bonat (2018) , for more information and examples.
How to cite:
Wagner Hugo Bonat (2016). mcglm: Multivariate Covariance Generalized Linear Models. R package version 0.9.0, https://cran.r-project.org/web/packages/mcglm. Accessed 05 Aug. 2026.
Previous versions and publish date:
(2026-07-09 06:28), 0.3.0 (2016-06-09 20:23), 0.4.0 (2018-04-11 00:01), 0.5.0 (2019-06-24 21:30), 0.6.0 (2020-06-13 17:20), 0.7.0 (2021-07-11 09:40), 0.8.0 (2022-09-15 21:36)
Other packages that cited mcglm R package
View mcglm citation profile
Other R packages that mcglm depends, imports, suggests or enhances
Complete documentation for mcglm
Functions, R codes and Examples using the mcglm R package
Some associated functions: ESS . GOSHO . Hunting . NewBorn . RJC . ahs . anova.mcglm . coef.mcglm . confint.mcglm . covprod . fit_mcglm . fitted.mcglm . gof . mc_anova_disp . mc_bias_corrected_std . mc_build_C . mc_build_F . mc_build_bdiag . mc_build_omega . mc_build_sigma . mc_build_sigma_between . mc_car . mc_complete_data . mc_compute_rho . mc_conditional_test . mc_core_pearson . mc_correction . mc_cross_sensitivity . mc_cross_variability . mc_derivative_C_rho . mc_derivative_cholesky . mc_derivative_expm . mc_derivative_sigma_beta . mc_dexp_gold . mc_dglm . mc_dist . mc_expm . mc_getInformation . mc_id . mc_initial_values . mc_link_function . mc_list2vec . mc_ma . mc_manova . mc_manova_disp . mc_matrix_linear_predictor . mc_mixed . mc_ns . mc_pearson . mc_quasi_score . mc_remove_na . mc_robust_std . mc_rw . mc_sandwich . mc_sensitivity . mc_sic . mc_sic_covariance . mc_transform_list_bdiag . mc_twin . mc_updateBeta . mc_updateCov . mc_variability . mc_variance_function . mcglm . pAIC . pBIC . pKLIC . plogLik . plot.mcglm . print.mcglm . residuals.mcglm . soil . soya . summary.mcglm . vcov.mcglm . 
Some associated R codes: RcppExports.R . fit_mcglm.R . mc_KLIC.R . mc_RJC.R . mc_S3_methods.R . mc_anova_disp.R . mc_auxiliar.R . mc_bias_correct_std.R . mc_build_C.R . mc_build_F.R . mc_build_bdiag.R . mc_build_omega.R . mc_build_sigma.R . mc_build_sigmab.R . mc_car.R . mc_complete_data.R . mc_compute_rho.R . mc_conditional_test.R . mc_core_cross_variability.R . mc_core_pearson.R . mc_correction.R . mc_cross_sensitivity.R . mc_cross_variability.R . mc_derivative_C_rho.R . mc_derivative_cholesky.R . mc_derivative_expm.R . mc_derivative_sigma_beta.R . mc_dexp_gold.R . mc_dexpm.R . mc_dglm.R . mc_dist.R . mc_ess.R . mc_getInformation.R . mc_gof.R . mc_gosho.R . mc_id.R . mc_initial_values.R . mc_link_function.R . mc_list2vec.R . mc_ma.R . mc_main_function.R . mc_manova.R . mc_manova_disp.R . mc_matrix_linear_predictor.R . mc_mixed.R . mc_ns.R . mc_pAIC.R . mc_pBIC.R . mc_pearson.R . mc_plogLik.R . mc_quasi_score.R . mc_remove_na.R . mc_robust_std.R . mc_rw.R . mc_sensitivity.R . mc_sic.R . mc_sic_covariance.R . mc_transform_list_bdiag.R . mc_twin.R . mc_updatedBeta.R . mc_updatedCov.R . mc_variability.R . mc_variance_function.R . mcglm.R . zzz_onAttach.R .  Full mcglm package functions and examples
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

dhReg  
Dynamic Harmonic Regression
Building and forecasting time series data with multiple seasonality using Dynamic Harmonic Regressio ...
Download / Learn more Package Citations See dependency  
quickcode  
Quick and Essential 'R' Tricks for Better Scripts
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to im ...
Download / Learn more Package Citations See dependency  
kernelPSI  
Post-Selection Inference for Nonlinear Variable Selection
Different post-selection inference strategies for kernelselection as described in kernelPSI a Post-S ...
Download / Learn more Package Citations See dependency  
dcov  
A Fast Implementation of Distance Covariance
Efficient methods for computing distance covariance and relevant statistics. See Sz ...
Download / Learn more Package Citations See dependency  
noisyr  
Noise Quantification in High Throughput Sequencing Output
Quantifies and removes technical noise from high-throughput sequencing data. Two approaches are use ...
Download / Learn more Package Citations See dependency  

28,083

R Packages

239,283

Dependencies

74,457

Author Associations

28,084

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA