Relaxed Lasso is a generalisation of the Lasso shrinkage
technique for linear regression. Both variable selection and
parameter estimation is achieved by regular Lasso, yet both
steps do not necessarily use the same penalty parameter. The
results include all standard Lasso solutions but allow often
for sparser models while having similar or even slightly better
predictive performance if many predictor variables are present.
The package depends on the LARS package.

This library contains R-functions for the comparison
of distributions. This includes nonparametric estimation of
the relative distribution PDF and CDF and numerical summaries
as described in
"Relative Distribution Methods in the Social Sciences"
by Mark S. Handcock and Martina Morris,
Springer-Verlag, 1999, Springer-Verlag, ISBN 0387987789

R functions for the comparison of distributions.
This includes nonparametric estimation of the relative distribution
PDF and CDF and numerical summaries as described in
"Relative Distribution Methods in the Social Sciences"
by Mark S. Handcock and Martina Morris,
Springer-Verlag, 1999, Springer-Verlag, ISBN 0387987789

R functions for the comparison of distributions.  This
includes nonparametric estimation of the relative distribution
PDF and CDF and numerical summaries as described in "Relative
Distribution Methods in the Social Sciences" by Mark S.
Handcock and Martina Morris, Springer-Verlag, 1999,
Springer-Verlag, ISBN 0387987789

Tools for the comparison of distributions. This includes nonparametric estimation of the relative distribution PDF and CDF and numerical summaries as described in "Relative Distribution Methods in the Social Sciences" by Mark S. Handcock and Martina Morris, Springer-Verlag, 1999, Springer-Verlag, ISBN 0387987789.

R functions for the comparison of distributions. This
includes nonparametric estimation of the relative distribution
PDF and CDF and numerical summaries as described in "Relative
Distribution Methods in the Social Sciences" by Mark S.
Handcock and Martina Morris, Springer-Verlag, 1999,
Springer-Verlag, ISBN 0387987789