R help - Apr 2018 - mgcv::gamm error when combining random smooths and correlation/autoregressive term

I am having difficulty fitting a mgcv::gamm model that includes both a random
smooth term (i.e. 'fs' smooth) and autoregressive errors.  Standard
smooth terms with a factor interaction using the 'by=' option work fine.
Both on my actual data and a toy example (below) I am getting the same error so
am inclined to wonder if this is either a bug or a model that gamm is simply
unable to fit?

Any insight or suggestions would be much appreciated.

M


Example:

library('mgcv')
set.seed(1)
df = data.frame(index=rep(1:10,5), x=runif(50,0,1), subject =
as.factor(sort(rep(1:5,10))))

# random intercept
m1 = gamm(x~s(index), random=list(subject=~1), data=df, method = 'REML')

#factor interaction, random intercept, AR errors
m2 = gamm(x~s(index, by=subject), random=list(subject=~1),
correlation=corAR1(form=~index|subject), data=df, method = 'REML')

#factor interaction, random intercept and slope, AR errors
m3 = gamm(x~s(index, by=subject), random=list(subject=~index),
correlation=corAR1(form=~index|subject), data=df, method = 'REML')

#random smooth on its own works ok
m4 = gamm(x~s(index, subject, bs='fs'), data=df, method =
'REML')


#combination of 'fs' smooth and AR term generates the error: "Error
in matrix(0, size.cg[i], size.cg[i]) : object 'size.cg' not found"
#the grouping term ( |subject ) is redundant in corAR1 as the mgcv documentation
indicates that gamm will assume the grouping
#from the random smooth term; the same error happens irrespective of whether the
grouping is included in corAR1 or not.

m5 = gamm(x~s(index, subject, bs='fs'),
correlation=corAR1(form=~index|subject), data=df, method = 'REML')