Fit joint mean-covariance models for longitudinal data. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the 'Armadillo' C++ library for numerical linear algebra and 'RcppArmadillo' glue.
|Author||Jianxin Pan [aut, cre], Yi Pan [aut]|
|Date of publication||2016-10-20 00:13:34|
|Maintainer||Jianxin Pan <Jianxin.Pan@manchester.ac.uk>|
|License||GPL (>= 2)|
ACD-class: Class ACD
acd_estimation: Fit Joint Mean-Covariance Models based on ACD
aids: Aids Data
bootcurve: Plot Fitted Curves and Corresponding Confidence Interval...
cash-ACD-method: Extract parts of ACD.
cash-HPC-method: Extract parts of HPC.
cash-MCD-method: Extract parts of MCD.
cattle: Cattle Data
getJMCM: Extract or Get Generalized Components from a Fitted Joint...
HPC-class: Class HPC
hpc_estimation: Fit Joint Mean-Covariance Models based on HPC
jmcm: Fit Joint Mean-Covariance Models
jmcmControl: Control of Joint Mean Covariance Model Fitting
jmcmMod-class: Class "jmcmMod" of Fitted Joint Mean-Covariance Models.
MCD-class: Class MCD
mcd_estimation: Fit Joint Mean-Covariance Models based on MCD
meanplot: Plot Fitted Mean Curves
modular: Modular Functions for Joint Mean Covariance Model Fits
regressogram: Plot Sample Regressograms and Fitted Curves
show-jmcmMod-method: Print information for jmcmMod-class