quadrupen 0.2-8 (2020-11-04)

+   Avoid warning in C++ to pass CRAN checks, thanks to a PR of Dirk Eddelbuettel <edd@debian.org>
+   Minors changes in examples, default mc.cores to 2

quadrupen 0.2-7 (2019-08-27)

+   change sort(, 1) to sort(, "descend") in C++ code to avoid warnings during checks

quadrupen 0.2-6 (2018-04-30)

+   change & to && in C++ code to avoid warnings during checks

quadrupen 0.2-5 (2017-03-06)

+   change "package = " to "PACKAGE = "

quadrupen 0.2-4 (2014-01-16)

+   memory leak corrected (sp_mat declaration)
+   linking to Rcpp/RcppArmadillo headers (requires R 3.0-2)

quadrupen 0.2-3 (2013-08-26)

+   added back the 'normalize' parameter
+   standardization is performed within the C++ code
+   use of sparse conversion from Matrix to Armadillo
+   corrected bug with the 'intercept' and 'residuals' components of the quadrupen class
+   added more tests in the inst directory
+   correction in the documentation
+   added r.squared to the quadrupen class

quadrupen 0.2-2 (2013-04-08)

+   minor fix to comply with recent ggplot2 updates.

quadrupen 0.2-1 (2013-02-27)

+   minor fix to pass CRAN check on Windows operating systems.

quadrupen 0.2-0 (2013-02-26)

+   added bounded regression (regression penalized by infinity norm + structered l2 norm)
+   added corresponding functionalies for cross-validation and stability path
+   corrected wrong annotations of the stability path (PFER)
+   handled normalization internally ('normalize' is no longer a parameter)
+   more simple internal handling of penscales and correction of the rescaling of the intercept
+   better use of multicore features
+   handled runtime error exception in RcppArmadillo when the system is singular (end of the solution path)
    A consequence is quadrupen is less likely to crash due to user's "bad" parametrization
+   simplification of the C++ code, bugs corrected, probably new ones added :-'(
+   added 'examples' and 'tests' directories

quadrupen 0.1-0 (2012-10-09)

+   first build: structured elastic-net with (weighted) quadratic loss, cross-validation and stability selection methods.