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lmenssp (version 1.2)

Linear Mixed Effects Models with Non-Stationary Stochastic Processes

Description

Contains functions to estimate model parameters and filter, smooth and forecast random effects coefficients for mixed models with stationary and non-stationary stochastic processes under multivariate normal and t response distributions, diagnostic checks, bootstrap standard error calculation, etc.

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Version

Install

install.packages('lmenssp')

Monthly Downloads

30

Version

1.2

License

GPL (>= 2)

Maintainer

Ozgur Asar

Last Published

July 23rd, 2016

Functions in lmenssp (1.2)

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
smoothed

A function for smoothing under multivariate normal response distribution
variogram

A function for calculating the empirical variogram for data sets with regularly or irregularly spaced follow-up time points
filtered

A function for filtering under multivariate normal response distribution
qqplot.t

Quantile-quantile plot for univariate t distribution
smoothed.heavy

A function for smoothing under multivariate t response distribution
data.sim.ibm

A simulated data set under a mixed model with random intercept, integrated Brownian motion and multivariate normal response distribution
lmenssp-package

Linear Mixed Effects Models with Non-stationary Stochastic Processes
boot.nm

A function to calculate bootstrap standard errors
var.inspect

A function for calculating empirical variances with respect to time for data sets with regularly or irregularly spaced follow-up time points
data.sim.ibm.heavy

A simulated data set under a mixed model with random intercept, integrated Brownian motion and multivariate t response distribution