Nothing

```
buildMetadata <- function(observations)
{
# Retrieve variance for each error model
obsErrVar = as.double(observations$oevar)
# If no variances provided, then there is no observation error
# Otherwise, check for bias
if (!is.null(obsErrVar))
{
# Retrieve bias for each error model - this can be omitted by the user
obsErrBias = as.double(observations$oebias)
# If no bias provided, use zero bias for each error model
if (is.null(obsErrBias) || length(obsErrBias) == 0) {
obsErrBias = rep(0.0, length(obsErrVar))
}
# Check that we have enough variances and biases, i.e.
# we need at least max(observations$oeid) error models where oeid
# is a vector of indexes giving the error model used for each
# observation
nErrorModels = max(observations$oeid,0)
if ( length(obsErrVar) < nErrorModels || length(obsErrBias) < nErrorModels )
{
print("Observation error index exceeds number of error models")
print("Make sure that the length of observations$oevar (and observations$oebias ")
print("if provided) is at least equal to the number of error ")
print("models requested (as given by max(observations$oeid))")
print("Observation error is ignored in the rest of the procedure")
}
else
{
# Build list of (Gaussian) model descriptors
metaData = mapply(FUN = function(...) paste("GAUSSIAN",...,sep=","),
obsErrBias,obsErrVar)
# Append sensor model.
# The sensor model goes at the end of the metadata and, for the
# moment, should always be empty (this is what the C++ library
# expects). In the future, when we add support for sensor models,
# this will allow MathML expressions (if I understand correctly)
# to be passed in.
sensorModel = length(obsErrVar) + 1
metaData[sensorModel] = ""
# Convert vector to list
metaData = as.list(metaData);
}
}
metaData
}
```

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