Scale Mixture of Skew-Normal Linear Mixed Models
Extract model residuals from smn.lmm and smsn.lmm objects
Summary of a smn.clmm object
Summary of a smsn.lmm object
Update for SMSN/SMN/SMNclmm objects
Weight plot for smn.lmm or smsn.lmm object
Autocorrelation function for smn.lmm or smsn.lmm residuals
Extract confidence intervals from lmmBoot object
Parametric bootstrap for SMSN/SMN objects
Extract coefficients from smsn.lmm, smn.lmm and smn.clmm objects
Extract smsn.lmm fitted values
Extract estimated fixed effects from smsn.lmm, smn.lmm and smn.clmm ob...
Formula from an smn.lmm and smsn.lmm models
Plot ACF for smn.lmm or smsn.lmm residuals
Extract the number of observations from smn.lmm and smsn.lmm fitted mo...
Computes confidence intervals from smn.lmm and smsn.lmm fitted models
Extracts criteria for model comparison of SMSN/SMN/SMNclmm objects
Error scale matrix associated with times
Extract smn.lmm fitted values
Extract smn.clmm fitted values
Healy-type plot from a smn.lmm or smsn.lmm object
Control options for smsn.lmm(), smn.lmm() and smn.clmm()
Log-likelihood of an smn.lmm and smsn.lmm models
Likelihood-ratio test for SMSN/SMN objects
Mahalanobis distance from a smn.lmm or smsn.lmm object
Mahalanobis distance from a smn.clmm object
Plot Mahalanobis distance for a fitted smn.lmm or smsn.lmm
Plot Mahalanobis distance for a fitted smn.clmm
Plot a smn.lmm or smsn.lmm object
Plot a smn.clmm object
Prediction of future observations from an smn.lmm object
Prediction of future observations from an smn.clmm object
Prediction of future observations from an smsn.lmm object
Print a smn.lmm object
Print a smn.clmm object
Print a smsn.lmm object
Extract random effects from smsn.lmm, smn.lmm and smn.clmm objects
Extract model residuals from smn.clmm objects
Generate data from SMSN-CLMM with censored responses
Generate data from SMSN-LMM
Residual standard deviation from smn.lmm and smsn.lmm objects
ML estimation of scale mixture of normal linear mixed models with cens...
ML estimation of scale mixture of normal linear mixed models
ML estimation of scale mixture of skew-normal linear mixed models
Summary of a smn.lmm object
It fits scale mixture of skew-normal linear mixed models using either an expectation–maximization (EM) type algorithm or its accelerated version (Damped Anderson Acceleration with Epsilon Monotonicity, DAAREM), including some possibilities for modeling the within-subject dependence. Details can be found in Schumacher, Lachos and Matos (2021) <doi:10.1002/sim.8870>.
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