Bayz in R - the BayzR package
We have changed bayz to an R package (BayzR) and the first version is
now available for download (see below).
If you are new to Bayz in R have a look at the Quick start
section to see some examples and features of using bayz in R.
Installing the BayzR package can now be done in three ways, for most
users in the following order of preference:
- Install the binary package if it suits your computer. We have now a version
available for Windows 10 and Mac OS (10.15). For Windows 10 run the following commands
in the R console:
install.packages("Rcpp")
install.packages("lme4")
install.packages("coda")
install.packages("http://www.bayz.biz/BayzR_0.0.7.zip", repos = NULL, type = "win.binary")
For Mac OS modify the last install to:
install.packages("http://www.bayz.biz/BayzR_0.0.7.tgz", repos = NULL, type = "mac.binary")
- If you have other systems than Windows or Mac (or for other reasons the above doesn't work),
you will have to download the source code
from github.com/ljanss/BayzR
and compile it
on your own computer using the R devtools package. See the github
page for details (the dowloading and installation can be done from within R as explained).
- If you are interested in co-developing and submitting changes
in the source code, then clone our development repository
github.com/MarniTausen/BayzR.
The second and third option are more demanding, e.g. you need a C-compiler (Rtools in Windows,
Xcode developer tools on Mac) and make sure it is working correctly with devtools.
Have a look at the
Quick start to run a first
dummy example in bayz and to get some examples of other features, and look
for more details in the other sections.