'update.jm.utility.fns' copies the JM utility functions named in 'jm.utilities.cur' from 'jmfuns.rda' to 'jm.utilities.rda'.  Any previous versions of 'jm.utilities.rda' are destroyed in the process.  It also copies 'jm.utility.fns.pdf' and 'rnote.pdf' from '/r' to a series of mirror locations.  

WARNING:  update.jm.utility.fns assumes that the source files should be copied to the target files.  It DOES NOT check that the date/time of the source files is more recent then the date/time of the target files.  This is because it is assumed that the source files are the "official" files.  Even if the target file happened to be revised at a later date/time, this is assumed to be an accident.  JM still wants the source file to replace the target file.

Set 'feedback = TRUE' for feedback regarding which functions were successfully copied to 'jm.utilities.rda'.  Set 'feedback = FALSE' (default) to omit this feedback.

'update.jmbugs' copies the JM utility functions named in 'jmbugs.cur' from 'jmfuns.rda' to 'jmbugs.rda'.  Any previous version of 'jmbugs.rda' is destroyed in the process.  It also copies 'bugs.fns.pdf' from 'e:/r/' to a series of mirror locations, and it copies 'e:/bugs/installing.bugs.jags.pdf' to a series of mirror locations.  

WARNING:  update.jmbugs assumes that the source files should be copied to the target files.  It DOES NOT check that the date/time of the source files is more recent then the date/time of the target files.  This is because it is assumed that the source files are the "official" files.  Even if the target file happened to be revised at a later date/time, this is assumed to be an accident.  JM still wants the source file to replace the target file.

Set 'feedback = TRUE' for feedback regarding which functions were successfully copied to 'jmbugs.rda'.  Set 'feedback = FALSE' (default) to omit this feedback.

'v.names' extracts the variable names from the output object of a
call to 'lm'.  The output of 'v.names' is a dataframe with 3 columns
called 'Names', 'Type', and 'On.Dataframe'.  Suppose that the
dataframe has been assigned the name 'N.out'.  Then
'N.out$Names[1] = 'All vars on search path' if the call to 'lm'
did not have a 'data' argument.  Otherwise, 'N.out$Names[1]' is
the name of the dataframe that was assigned to the 'data' argument
of the 'lm' call.  'N.out$Names[2]' is the name of the dependent
variable, and 'N.out$Names[3]' to 'N.out$Names[3 + k]' are the
names of the k+1 predictor variables.
'N.out$Type[1]' is always 'data.source', and 'N.out$Type[2]' is
always 'dv'.  For the remaining predictor variables, 'N.out$Type[i]'
is either 'factor' or 'quant' depending on whether the variable is
treated as a factor or a quantitative variable.
'N.out$On.Dataframe' is TRUE for variables that are in the
dataframe that was names by 'N.out$Names[1]', and FALSE
otherwise (in which case the variable must have been on the
search path when 'lm' was called.