Changelog

Source: NEWS.md

convexjlr 0.8.0.9000 Unreleased

  • Updates for Julia v0.7 and v1.0.
  • Drop XRJulia support, as it does not work with Julia v0.7 and v1.0.

convexjlr 0.7.1.9000 Unreleased

  • Default SCS solver doesn’t have verbose = FALSE default option any more.
  • Users can choose ECOS as the solver for convex problems.
  • Users can set a bunch of options for both SCS and ECOS solvers.

convexjlr 0.7.0.9000 Unreleased

  • The users can set maximal iteration times for the convex problem solver in cvx_optim.
  • Bug correction for handling of diag.

convexjlr 0.7.0 2018-04-29

  • Remove deprecated setup function.
  • Use JuliaCall as the default backend.

convexjlr 0.6.1.9000 Unreleased

  • Fix deprecation warnings from JuliaCall backend.
  • Fix some little bugs.
  • Add the option in convex_setup to set the path to julia binary.

convexjlr 0.6.1 2017-10-01

  • The second release on CRAN.

convexjlr 0.6.0.9000 Unreleased

  • Supports multiple ways to connect to julia, one way is through package XRJulia, and the other way is to use package JuliaCall. The difference is as follows:
    • XRJulia connects to julia, which is the default for convexjlr, the advantage is the simplicity of the installation process, once you have a working R and working julia, it should be okay to use convexjlr in this way. Note that if you have the latest Julia version (v0.6.0) installed, then you have to use the latest version of XRJulia.
    • JuliaCall embeds julia in R, the advantage is the performance, for example, if your convex problem involves large matrice or long vectors, you may wish to use JuliaCall backend for convexjlr; the disadvantage is the installation process, since embedding julia needs compilations.

convexjlr 0.5.1.9000 Unreleased

  • Added a NEWS.md file to track changes to the package.
  • Re-organize tests.
  • Deprecate setup, should use convex_setup.

convexjlr 0.5.1 2017-06-21

  • A patch release on CRAN.

convexjlr 0.5.0 2017-06-19

  • The first release on CRAN.