2006-07-20 Version 1.1
* fixed bug to enable formula = dependent ~ 1 in hapassoc() 
  and summary.hapassoc()
* hapassoc() now also returns the function call 
* summary.hapassoc() now also returns the hapassoc function call, 
  the number of subjects used in the analysis, the name of the 
  family and the log-likelihood. The returned object is printed nicely.

2006-04-28 Version 1.0-1
* added the following second citation:
Burkett K, Graham J and McNeney B (2006). hapassoc: Software for
Likelihood Inference of Trait Associations with SNP Haplotypes and Other
Attributes. Journal of Statistical Software, 16(2), 1-19

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.

2005-07-13 Version 0.7-1
* handleMissings(), pre.hapassoc(): instead of casting to a 
  data.frame use indexing argument drop=FALSE.

2005-06-30 Version 0.7
* hapassoc(): use initial weights in the glm to get initial parameter estimates.
* hapassoc(), pre.hapassoc(): replaced weights calcuation with a C function.
  Speed up computing time for large data sets.

2005-05-31 Version 0.6-2
* handleMissings(): fixed a bug for SNPs with a rare allele that is in no
  instance in a homozygous state.

2005-05-09 Version 0.6-1

* Added this ChangeLog file
* Added inst/CITATION file

2005-04-06 Version 0.6

* RecodeHaplos(): Allow input SNP data as alphabetic alleles (e.g.A,G,C,T),
  and for genotypes to be input either in a single two-character column
  ("genotypic format"), or as a pair of columns (the original "allelic
  format" from earlier versions of hapassoc)

* pre.hapassoc(): Added allelic argument to indicate whether SNP data are
  input in either genotypic fromat or allelic format

* RecodeHaplos(): Check for the number of alleles at each locus. If the
  check finds loci with >2 alleles, stop execution and print an error
  message that tells the user that only diallelic loci are allowed.

* RecodeHaplos(): Convert all missing data in "" format to NA.

* happasoc(): The convergence criteria for the EM algorithm has been
  tightened. We now require both absolute and relative changes in the
  parameter estimates from one iteration to the next to be below the
  user-specified tolerance.

* summary.hapassoc(): Added a check for converged FALSE, and now print a
  warning (used to just give a cryptic error).

* happasoc(): Changed the name of the variable 'gamma' to 'freq' and the
  name of the variable 'initGamma' to initFreq'
* pre.hapassoc(): Changed the name of the returned variable 'initGamma' to
  'initFreq'

pre.hapassoc man page 

* pre.hapassoc(): Added a new example of how to use pre.hapassoc with SNPs
  in the new genotypic format

* Added documentation to describe how single-locus genotypes may now be
  specified as a single two-character column ("genotypic format") in the
  input data frame

* Added a Note to alert users to ignore the possible warnings related to
  row.names being duplicated when there are missing genotypes on some of the
  loci for an individual

* Updated the reference to Burkett et al. to give journal volume and page
  numbers

hapassoc man page

* Added a Note to alert users to the warning they'll see when fitting
  logistic regression models (non-integer #successes...)

* Added more comments to the examples to make the coding of columns in
  haploDM more obvious, and added an example of a non-multiplicative
  logistic regression model

* Updated the reference to Burkett et al.

summary.hapassoc man page

* Updated the reference to Burkett et al.


2004-11-03 Version 0.5-1

* handleMissings() assignment to nonSNPdat changed to accomodate rbind of
  data frames which contain factors.

* hapassoc(): Assigned  response<-regr$y  
  We used to use model.response to extract the response variable, but this
  lead to problems in calculating the residuals in the pYgivenX function.
  It is better to fit the model with the glm function (see code) and then
  extract the response from the fitted model object


2004-09-29 Version 0.5

* Changed file name from PreEM.R to PreHap.R
* Changed function name from EM to hapassoc
* Changed function name from PreEM to pre.hapassoc
* changed function name from summary.EM to summary.hapassoc