I have just uploaded version 0.3 of my libraries to the CRAN site so
hopefully they should soon be available.
  A reminder that I am happy to supply examples of any of the
procedures: R code, data, and my output, plus a postscript file of the
relevant chapter(s) of my repeated measures text.
  The main changes follow (see also the changes file in rmutil).
  Jim

1. Thanks to Brian Ripley, the Fortran routines have been modified so
   that they should work with more compilers, including g77, and
   several bugs corrected.

2. Hidden Markov models (hidden) now include categorical (multinomial)
   data so that DNA and protein sequence analysis can be performed.
   This function now also include many continuous distributions.
   Probabilities of being in the various states can be plotted. A new
   function, chidden, does all of this in continuous time as well.

3. Matrix exponentiation (mexp) now uses spectral decomposition by
   default.

4. Numerical integration (int) now uses Romberg integration by default
   and is vectorized. This allows, for example, calculation of
   cumulative distribution functions simultaneously for a vector of
   data, for example for Levy, power exponential, and generalized
   inverse Gauss distributions for use in nonlinear regression with
   censored survival data (gnlr and gnlr3).

5. Levy, gamma count, and Pareto distributions have been added for
   nonlinear regression (gnlr and fmr). Student t nonlinear regression
   has been moved from gnlr to gnlr3.

6. Levy, Pareto, generalized inverse Gauss, and power exponential
   distributions have been added to generalized autoregression (gar).

7. Density and probability functions for several distributions,
   including beta binomial, Box-Cox, Burr, double binomial, double
   Poisson, generalized extreme value, generalized gamma, generalized
   inverse Gaussian, generalized logistic, generalized Weibull, gamma
   count, Hjorth, inverse Gaussian, Laplace, Levy, multiplicative
   binomial, multiplicative Poisson, Pareto, and power exponential.
   (Nonlinear regression can be performed with all of these and others
   using gnlr or gnlr3.)

8. Several minor improvements and bug fixes.
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