Nothing

```
################################################################################
# This file is part of growthrate. #
# #
# growthrate is free software: you can redistribute it and/or modify #
# it under the terms of the GNU General Public License as published by #
# the Free Software Foundation, either version 3 of the License, or #
# (at your option) any later version. #
# #
# growthrate is distributed in the hope that it will be useful, #
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
# GNU General Public License for more details. #
# #
# You should have received a copy of the GNU General Public License #
# along with growthrate. If not, see <http://www.gnu.org/licenses/>. #
################################################################################
growth <- function(data,tobs,sigma,d) {
data = as.matrix(data);
obsdata = data;
dim(obsdata);
## obsdata is the data matrix of dimension N (number of subjects) times n (number of time points).
N = dim(obsdata)[1];
n = dim(obsdata)[2];
## calculate the difference of consecutive time points
dobspts = diff(tobs);
## calculate the vector y in the paper
YI = matrix(nrow=N,ncol=(n-1));
dobsdatak = rep(0,(n-1));
for (k in 1:N) {
YI[k,] = diff(obsdata[k,])/dobspts;
}
## Estimate the prior mean based on nonparametric approach and obtain Xtilda (ybar in the paper)
resulmuprior = priormeanobs(YI,tobs);
muprior = resulmuprior$muprior;
Xtilda = resulmuprior$Xtilda;
## Estimate the prior precision based on CLIME
re.clime = clime(t(Xtilda),perturb=TRUE);
re.cv = cv.clime(re.clime,loss=c("likelihood", "tracel2"));
re.clime.opt <- clime(t(Xtilda), re.cv$lambdaopt, perturb =TRUE);
Sigma0inv <- re.clime.opt$Omegalist[[1]];
## Get the posterior mean and variance-covariance matrix at observed time points
resulpost = posteriorobs(Sigma0inv,sigma,muprior,Xtilda,tobs,YI);
muhatMatrix = resulpost$muhatMatrix;
Sigmahat = resulpost$Sigmahat;
## The posterior mean curves (muhatcurve) and variance covariance operator (Khat) evaluated at the fine grid
r = posteriordistribcurve(muhatMatrix,Sigmahat,sigma,tobs,d,YI);
tgrid = r$tgrid;
muhatcurve = r$muhatcurvematrix;
Khat = Khatf(tgrid,tobs,sigma,Sigmahat);
## Return the muhat curves for each subject, variance-covariance operator and the fine grid.
result = list(muhatcurve=muhatcurve,Khat=Khat,tgrid=tgrid);
return(result);
}
```

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