I spend most of my time making the other people on this site miserable through my endless requests for code, their time, etc. It's impressive that they continue to collaborate with me.
Projects

Bayesian MAR(1) model
by brice.semmens, last updated 7/8/08, sharing set to Public
This set of Matlab scripts conducts a first order MAR(1) model to estimate interactive effects, and covariate effects, on a time series of data from a community.It uses a Gibbs sampler to estimate parameters, and currently is set up with diffuse priors on all parameters for the model. It is pretty basic at this point, but it works.
Note on the Gamma distribution:
Going back and forth between Matlab/R/WinBUGS can be confusing, because of the different parameterizations of the gamma pdf. Here's the Matlab/BUGS forms and the R equivalents:
Matlab: X ~ g(a,b) E[X] = ab
R equivalent: X ~ g(shape=a,scale=b)
BUGS: X ~ g(a,b) E[X] = a/b
R equivalent: X ~ g(shape=a,rate=b) OR
X ~ g(shape=a,scale=1/b)

Gibbs sampler for multiple linear regression
THE FOLLOWING FUNCTION CONDUCTS MULTIPLE LINEAR REGRESSION PARAMETER ESTIMATION VIA MCMC USING GIBBS SAMPLING.
The point of this code is to show how to do it with a conjugate distribution such that the method can easily be plugged into LAMBDA.
The function assumes that:
1)the B coefficients are normally distributed ~N(prior_mean,prior_s2)and thus have a normal distribution prior. Large prior variances produce suitably "diffuse" priors
2) the model variance is gamma distributed Ga(alpha, beta), with an associated prior. Small values of alpha and beta (<.001) produce a suitably "diffuse" prior. Note that Matlab's parameterization of their gamma function is a bit wonky compared to the standard. Basically, Matlab uses Ga(alpha,1/beta) ie. inverse of the standard scale parameter.
The R code contains 2 versions of Bayesian linear regression. The first (univariate.all) uses the multivariate normal distribution to sample regression coefficients; the second (univariate.vat) does variable at a time sampling. Both R versions are nearly identical, allow a flexible range of priors for B, and allow the prior variance to be specified either as the shapescale (e.g. gamma, scaled inv chi sq) or in terms of the meanvariance. Output includes saved parameter draws, which can be fed into CODA, and the calculation of the log(Bayes Factor) to be used for model selection. 
MARSS Dev Site
by e2holmes, last updated 8/30/12, sharing set to Public
This is the DEVELOPMENT site for the MARSS. For the current MARSS release go to CRAN or download straight from the R GUI using "Install Packages" menu.
MARSS fits mulitvariate autoregressive statespace (MARSS) models with Gaussian errors to multivariate time series data. A MARSS model is:
x(t) = B(t) x(t1) + u(t) + C(t)c(t) + v(t), v(t)~MVN(0,Q)
y(t) = Z(t) x(t) + a(t) + D(t)d(t) + w(t), w(t)~MVN(0,R)
Project news (Feb 26, 2013): MARSS 3.4 uploaded to CRAN. I fixed MARSSkfas to work with the new KFAS package in order to use the Koopman/Durbin filter/smoother algorithms. I also coded up a lagone covariance smoother using an augmented statespace model that you can then run through the smoother to get the lagone covariances. I added a coef() and residuals() method to improve output.
Developers: Eli Holmes, Eric Ward, Mark Scheuerell and Kellie Wills
Current known issues:
 When variance is "unconstrained", the covariances can be set to 0 in the degen.test() and this leads to not posdef matrix and error. Need to block setting to 0 when this happens, or block covariances set to zero? Currently, deal with this by setting allow.degen=FALSE when covariances are estimated.
 demean.states=TRUE is causing the EM algorithm to give drops in logLik. This is not really a bug but perhaps a property of demean.states. Removed the demean.states option in vrs 3.3.
MARSS 4.0 in progress:
 4.0 involves a substantial change in the model object structurehowever the user should not notice the difference. The change allows the developers to more easily code up new model structures.
 Progress continues on writing functions for standard output, e.g. predict function added.
 Integration with LateX begun so that output can be sent to a tex or pdf file instead of just the console.