R packages - Apr 2006 - Version 1.0 of hapassoc now available from CRAN

Version 1.0 of hapassoc now available from CRAN

hapassoc is an R package for likelihood inference of trait associations 
with SNP haplotypes and other attributes using the EM Algorithm. Recent 
changes include the addition of anova and logLik methods for the 
class hapassoc, to allow users to perform likelihood ratio tests of 
haplotype effects. Other changes include bug-fixes and improvements to 
the documentation, such as the addition of a package vignette. See the 
exerpt from the ChangeLog for more details.

ChangeLog

2006-04-04 Version 1.0

 * hapassoc(): the "baseline" argument is documented to have default
   equal to the most common haplotype, but the code to implement this
   default was lost and needed to be replaced.
 * hapassoc(): added a "verbose" flag. Default is verbose=FALSE. If
   TRUE users see the iteration number and value of the convergence
   criterion at each iteration of the EM algorithm.
 * pre.hapassoc(): added a "verbose" flag. Default is verbose=TRUE.
   If TRUE users see a list of the SNP genotypes used to form
   haplotypes and a list of the other "non-haplotype" variables.
 * Package vignette "hapassoc" added. After loading the package, type
   vignette("hapassoc") to view.

2006-03-22

 * Overall addition of the log-likelihood functions
 * hapassoc(): function now returns log-likelihood and model
 * logLik.hapassoc(): New function to extract the log-likelihood from
   a hapassoc object
 * anova.hapassoc(): New function to perform likelihood ratio test on
   two hapassoc objects.

2006-02-02 Minor changes:

 * EMvar(): fixed a bug occurring when all haplotype phases are known.
 * RecodeHaplos(): fixed a bug where a single column of non-haplotype
   data in a non-allelic data set was losing its name.
 * hapassoc(): Change "..." argument of hapassoc to "start".
   Previously the only intended use of "..." was to allow the user to
   pass in "start" for starting values to the glm function, rather
   than to allow the user to pass in other optional arguments to glm.
   We have now made this more explicit by making this argument more
   specific.

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