Nothing

```
nbinomDeviance <- function(y,mean,dispersion=0,weights=NULL)
# Residual deviances for row-wise negative binomial glms.
# y is a matrix and a deviance is computed for each row.
# A vector y is taken to be a matrix with one row; in this case mean and dispersion must also be vectors.
# Original version (deviances.function) 23 November 2010.
# Renamed to nbinomDeviance 9 Dec 2013.
# Last modified 21 June 2017.
{
out <- .compute_nbdeviance(y=y, mean=mean, dispersion=dispersion, weights=weights, dosum=TRUE)
names(out) <- rownames(y)
out
}
nbinomUnitDeviance <- function(y,mean,dispersion=0)
# Unit deviance for the negative binomial distribution.
# Created 9 Dec 2013. Last modified 18 Mar 2018.
{
y[] <- .compute_nbdeviance(y=y, mean=mean, dispersion=dispersion, weights=NULL, dosum=FALSE)
y
}
.compute_nbdeviance <- function(y, mean, dispersion, weights, dosum)
# Created 3 Oct 2016.
# Last modified 27 Apr 2018.
{
# Check y. May be matrix or vector.
if(is.matrix(y)) {
if(!is.matrix(mean)) stop("y is a matrix but mean is not")
} else {
n <- length(y)
y <- matrix(y,1L,n)
if(is.matrix(mean)) {
stop("mean is a matrix but y is not")
} else {
if(length(mean)==n || length(mean==1L)) {
mean <- matrix(mean,1L,n)
} else {
stop("length of mean differs from that of y")
}
}
if(is.matrix(dispersion)) {
stop("dispersion is a matrix but y is not")
} else {
if(length(dispersion)==n || length(dispersion==1L)) {
dispersion <- matrix(dispersion,1L,n)
} else {
stop("length of dispersion differs from that of y")
}
}
}
# Check mean
if(!identical(dim(y),dim(mean))) stop("mean should have same dimensions as y")
if(!is.double(mean)) storage.mode(mean) <- "double"
# Check dispersion (can be tagwise (rowwise) or observation-wise).
dispersion <- .compressDispersions(y, dispersion)
# Check weights.
weights <- .compressWeights(y, weights)
# Compute matrix of unit deviances, or residual deviance per gene, depending on 'dosum'.
.Call(.cxx_compute_nbdev, y, mean, dispersion, weights, as.logical(dosum))
}
```

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