Hi all, I've written three new packages for Julia, and am interested in getting some feedback/comments from the community as well as determining whether there is sufficient interest to register them officially. The packages are:
[DependentBootstrap](https://github.com/colintbowers/DependentBootstrap.jl) [KernelStat](https://github.com/colintbowers/KernelStat.jl) [RARIMA](https://github.com/colintbowers/RARIMA.jl) and can be pulled using Pkg.clone("URL_HERE"). I don't have any problems compiling them on v0.3, but would be very interested in hearing of any problems compiling on v0.4 (or v0.3 for that matter). The first package, DependentBootstrap, implements the iid bootstrap, stationary bootstrap, circular block bootstrap, moving block bootstrap, tapered block bootstrap, and nonoverlapping block bootstrap, as well as the block length selection procedures in Politis and White (2004) (including the correction provided in Patton, Politis and White (2009)), and Paparoditis and Politis (2002). The main thing it doesn't do (yet) is work with multivariate data. So just 1-dimensional time-series for now. This package is implemented entirely in Julia. The second package, KernelStat, just implements some kernel functions from statistics, and includes three bandwidth estimation procedures, including the adaptive choice method discussed in Politis (2003). The main purpose of this package for now is to provide the bandwidth estimates needed by the block length selection procedures in the DependentBootstrap package, but in the future it could be merged with other packages to provide a general package for kernel-based nonparametric statistics. This package is implemented entirely in Julia. The third package, RARIMA, implements ARIMA estimation, forecast and simulation. Unfortunately, I didn't have time to implement all the functions in this package in Julia. To be honest, it is probably a task better suited to someone more knowledgeable about the ins-and-outs of ARIMA models and state space representations than I. So instead, what I've done with this package is use the Julia package RCall to wrap the ARIMA functionality in R, hence the package name RARIMA. Currently, the simulation functions in RARIMA are implemented in Julia, there is a version of the forecast functions implemented in Julia (but they are not capable of including confidence intervals), and a version of the forecast functions that wrap R functionality (these do provide confidence intervals). Finally, all estimation functions wrap R routines. I would welcome any comments, feedback, recommendations, pull requests, etc. I would be particularly interested in any suggestions to improve the performance of the functions in the DependentBootstrap or KernelStat package. Cheers, Colin
