v0.2.6

• Added the function to extract the SNP effects from the MCMC iterations: extractMCMCeff
• Added the function to read the eigen matrix: readEig
• Fixed various bugs, e.g. error when NAN existed in the tuning; report all the issues from the annotation at once; remove NAN values in the imputation function.
• Change the outputs, put files not quite useful to .mcmcsamples folder.

(don't need to rerun, if you are not interested in the effects in MCMC iterations)

v0.2.5

• Update the sampling for Var_e, it sometimes breaks if Meta-ed data has some errors.
  (Don't need to re-run, if your results make sense in previous versions. However, if the median Var_e is very small (< 0.5), a re-run is suggested)

v0.2.4

• Added prs function, that can calculate the PRS in SBayesRC easily
• Added sbrcMulti function, to perform the SBayesRC-Multi
• Fixed bug in variance explained by each binary annotation (don't need to rerun, if you're not interested in this)

v0.2.3

Updated the threshold of outlier detection (Do not need to rerun usually if you run 0.2.2)

v0.2.2

Add a new flag to exclude variants from modeling (exclude).

v0.2.1

Improved robustness by removing the SNPs with error in iterations.
(A re-run is suggested for summary statistics from meta-analysis)

v0.2.0

What's new:

• Updated SBayesRC using summary-data-based tuning to improve prediction accuracy
• Added generating LD function
• Performance enhancement
• Updated the documents

Note: the parameter "annotFile" in sbayesrc function has been changed to "annot". We changed the default behaviour of functions, use the parameter log2file=TRUE (as in the example) to output the log to file.

If you're running out-of-sample prediction, we suggest to re-run with this version.

v0.1.5

Add thin flag outFreq, bug fix and potencial enhancement.
The results is not affected if running successfully in previous version.

v0.1.4b

Fix the LD format issue with EAS in impute function.

v0.1.4a

Fix the installation error "undefined symbol: sgecon_", when no BLAS packages exist. Thanks for the report from @shinichi.