Linear Mixed Effects Models with Non-Stationary Stochastic Processes

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Documentation for package ‘lmenssp’ version 1.2

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lmenssp-package Linear Mixed Effects Models with Non-stationary Stochastic Processes
boot.nm A function to calculate bootstrap standard errors
data.sim.ibm A simulated data set under a mixed model with random intercept, integrated Brownian motion and multivariate normal response distribution
data.sim.ibm.heavy A simulated data set under a mixed model with random intercept, integrated Brownian motion and multivariate t response distribution
filtered A function for filtering under multivariate normal response distribution
lmenssp Function to obtain the maximum likelihood estimates of the parameters for linear mixed effects models with random intercept and a stationary or non-stationary stochastic process component, under multivariate normal response distribution
lmenssp.heavy Function to obtain the maximum likelihood estimates of the parameters for linear mixed effects models with random intercept and a stationary or non-stationary stochastic process component, under multivariate t response distribution
qqplot.t Quantile-quantile plot for univariate t distribution
smoothed A function for smoothing under multivariate normal response distribution
smoothed.heavy A function for smoothing under multivariate t response distribution
var.inspect A function for calculating empirical variances with respect to time for data sets with regularly or irregularly spaced follow-up time points
variogram A function for calculating the empirical variogram for data sets with regularly or irregularly spaced follow-up time points