Enabling random intercepts in the rms r-band for mixed effects of logistic regression

Question

Frank Harrell rms R-Pack is a great tool for implementing multiple logistic regression. However, I want to know how / if random effects can be included in a model passing through rms. I know that rms can work through nlme, but only a generalized least squares function (Gls), and not an lme function that allows the inclusion of random effects. Mixed effect models can be problematic for analysis / interpretation, but are sometimes necessary to account for nested effects in models.

I'm not sure if this is useful in this case, but I copied some code from the rms help files that uses a simple logistic regression model and added a line showing the mixed effects regression logic model passing through the glmmPQL MASS package.

n <- 1000    # define sample size
require(rms)
set.seed(17) # so can reproduce the results
age            <- rnorm(n, 50, 10)
blood.pressure <- rnorm(n, 120, 15)
cholesterol    <- rnorm(n, 200, 25)
sex            <- factor(sample(c('female','male'), n,TRUE))
label(age)            <- 'Age'      # label is in Hmisc
label(cholesterol)    <- 'Total Cholesterol'
label(blood.pressure) <- 'Systolic Blood Pressure'
label(sex)            <- 'Sex'
units(cholesterol)    <- 'mg/dl'   # uses units.default in Hmisc
units(blood.pressure) <- 'mmHg'
ch <- cut2(cholesterol, g=40, levels.mean=TRUE) # use mean values in intervals
table(ch)
f <- lrm(ch ~ age)
require(MASS)
f1<-glmmPQL(ch~age, random=~1|sex, family=binomial)
summary(f1)

I would be interested to know if random effects in rms can be enabled for both logistic regression (lrm) and the nlme run for linear regression.

Thank you all