  CHANGES IN BayesFactor VERSION 0.9.3

CHANGES
  *  Restricted to R 3.0.0 (due to vignette compilation). (0.9.2 will be the last version supported on R 2.x)


  CHANGES IN BayesFactor VERSION 0.9.2

CHANGES
  *  Full support for linear models: continuous and categorical covariates can now be included in the same model using lmBF()
  *  Minor changes to the BayesFactor output to make it clearer
  *  Fixed display of very large and very small Bayes factors; no longer will display read something like "Inf (2%)" or the like
  *  Clearer labels on MCMC output
  *  When error is missing from the BayesFactor object, plot prints "?" next to the bar to indicate no error estimate is available
  *  Default prior scale setting changed for continuous covariates; scale now defaults to sqrt(2)/4, which corresponds to the ANOVA "medium" setting (and will give the same Bayes factor in special cases where they should)
  *  Default prior scale setting in one-sample t changed to 1/2 (it was erroneously changed to sqrt(2)/2). Two-sample t test default setting remains the same, at sqrt(2)/2
  *  Added new prior scale settings for random effects; default to "nuisance", which is the same as the old default (r=1)  
  *  New prior scale setting: "ultrawide"
  *  Fixed bug with BFManual() which caused it not to start if dynamic help had not been started yet
  *  When doing an Bayes factor analysis that requires sampling, the new default setting (method="auto") will automatically try to select the best sampler for you so that you get the most efficient samples.



  CHANGES IN BayesFactor VERSION 0.9.1

CHANGES
  *  Vignette compilation changed for compatibility with R 3.0.0


  CHANGES IN BayesFactor VERSION 0.9.0

CHANGES
  *  New S4 classes representing Bayes factors, models, and MCMC chains. The output of all functions will now be objects of these classes
  *  Error estimates are now given for all Bayes factor outputs
  *  To accomodate the new system for creating and manipulating Bayes factors, the main function names have all changed. ANOVA is done via anovaBF(), multiple regression is done via regressionBF() and both can be done through lmBF()
  *  Posterior sampling is supported by calling the new posterior() method on Bayes factor objects. The result is an BayesFactor MCMC object, which inherits methods for for mcmc objects from the coda class
  *  New recompute() method will allow the reestimation of Bayes factors (for Bayes factor objects) and restimation of posteriors (for BayesFactor MCMC objects)
  *  New cleaned-up code base

