A new version of my DSE package for multi-variate time series analysis is now available at <www.bank-banque-canada.ca/pgilbert>. I am trying to sort out some minor glitches with the new R 0.90 graphics before I submit it to CRAN. Much of the underlying code has been re-worked and reorganized in the new version. In particular, the use of model and data constructors has been more formalized and the internal structure of these objects, while still documented, is considered "opaque." I have separated out a "syskern" package which tries to provide a kernel of routines to isolate OS differences and a few Splus and R differences. For most people, the most interesting part of this is probably the approach to RNG, which provides a convenient way to generate the same random experiments in R and Splus. I have also separated out a "tframe" package which provides a kernel of routines for programming time series methods. These allow a programmer to write most code in a way that is independent of the time representation (i.e. the class of the time series data). So, for example, the use of tsp() is avoided because it is specific to certain classes of time series. The changes to the underlying structure necessitated a substantial re-write of the user's guide, so I am taking the opportunity to bring the guide up-to-date with respect to R. A draft of the guide is available at the above web site in postscript and pdf files. The first nine sections, which cover material in the previous version of the guide, are now mostly complete and I hope correct. I would certainly appreciate comments. Some later sections, which cover new material, are still missing or incomplete. The help has largely been converted to integrate with R's help. I have not yet worked through all the examples, many of which were written with fictitious data in mind, so it will be some time before "R CMD check dse" works, however, most of my tests work in both Linux and Solaris. These include: random.number.test() tframe.function.tests() dse1.function.tests() dse2.function.tests() dse3.function.tests() dse4.function.tests() guide.example.tests.part1() guide.example.tests.part2() which cover a large part of the package. The comparison tolerances on some of the tests had to be relaxed in order to pass with R on Linux and R and Splus 3.3 in Solaris. I would appreciate feedback about how the tests work on other platforms. Paul Gilbert -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-announce mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-announce-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._