'setwd.jm' sets the current working directory to its argument, 'dir2', just as 'setwd' does.  In addition, if there is a 'data.rda' attached to the search path prior to changing the working directory, this file is detached from the search path.  If there exists a 'data.rda' file in the new working directory ('dir2'), then it is loaded onto the search command.  If there is no 'data.rda' file in the new working directory, then 'data.rda' is created by saving a default character vector 'File.Description' in 'data.rda' and saving it.  'File.Description describes the purpose of 'data.rda'. Change 'File.Description' to something more descriptive if you like.

If 'data.rda' does not already contain an object called 'loc.data.rda', then 'loc.data.rda' is created and moved to 'data.rda'.  'loc.data.rda' is a character string that specifies the location of 'data.rda' in the directory structure.  Its main use is to avoid a situation where a function moves an object to a 'data.rda' file which happens to be different from the intended 'data.rda' file.  

'setwd.jm' is designed to identify the intended directory on either the X220 laptop or on the UHouse computer.

'show.bugs' displays basic information about 'bugs' output, specifically, it shows which BUGS program produced the output, the names for the parameters, and the number of iterations, the number of burnin samples, and the number of chains of samples.

'smry.spss' converts 'summary' results of an 'lme' computation into
the form in which SPSS reports these results.  This facilitates
comparison of the results produced with 'lme' to the results produced
by the SPSS MIXED procedure.
** The 'sample.sizes' component states the number of fixed effect parameters,
number of covariance parameters, and the total sample size.
** The 'fit.stats' component states -2*(Log Likelihood) of the fit,
and AIC and BIC.  R and SPSS use different formulas for AIC and BIC.
R.AIC = -2*(Log Likelihood) + 2*(# fixed parameters + # covariance parameters).
SPSS.AIC = -2*(Log Likelihood) + 2*(# covariance parameters).
BIC = -2*(Log Likelihood) + params*(Log N)
In R, params = (# fixed parameters + # covariance parameters)
In SPSS, params = # covariance parameters.
** The 'var.components' component shows the standard deviation and
variance estimates for individual random effects, and the correlations
and covariances for pairs of random effects.  It also shows the
residual standard deviation and variance.