R (>= 2.7.0), lattice (>= 0.16-3), latticeExtra (>= 0.5-2), cairoDevice, gWidgetsRGtk2, grid
RGtk2 (>= 2.10.11), gWidgets, gridBase, grDevices, graphics, utils
A GTK+ graphical user interface for exploring and editing R plots.
Also includes latticist, a Lattice-based tool for exploratory visualisation.

R (>= 2.5.0), lattice (>= 0.16), grid, gWidgets, gWidgetsRGtk2, cairoDevice
RGtk2 (>= 2.10.11), gridBase, graphics, grDevices, utils,
A graphical user interface for viewing and interacting with R plots.
It tries to work out the structure of a plot, in order to interact with it.
The built-in features include: zooming the data space, identifying data points,
editing and annotating the plot, and saving to a file. New tools can be defined.
This package is based on RGtk2, and so requires the GTK+ libraries.

R (>= 2.7.0), lattice (>= 0.16-3), latticeExtra (>= 0.5-3), cairoDevice, gWidgetsRGtk2 (>= 0.0-45), grid
RGtk2 (>= 2.10.11), gWidgets (>= 0.0-30), gridBase, grDevices, graphics, stats, utils

hexbin, deldir, tripack, zoo, MASS, DAAG, ggplot2

R (>= 2.7.0), lattice (>= 0.16-3), cairoDevice, gWidgetsRGtk2 (>= 0.0-45), grid
A GTK+ graphical user interface for exploring and editing R plots.

A GTK+ graphical user interface for editing and interacting with R plots.

R (>= 2.7.0), lattice (>= 0.16-3), cairoDevice, gWidgetsRGtk2
(>= 0.0-45), grid
RGtk2 (>= 2.10.11), gWidgets (>= 0.0-30), gridBase, grDevices,
graphics, stats, utils
A GTK+ graphical user interface for editing and
interacting with R plots.

RGtk2 (>= 2.20.1), gWidgets (>= 0.0-30), gridBase, grDevices,
graphics, stats, utils

RGtk2 (>= 2.10.11), gridBase, graphics, grDevices, utils

Penalized Log-Density Estimation Using Legendre Polynomials
JungJun Lee, Jae-Hwan Jhong, Young-Rae Cho, SungHwan Kim, Ja-yong Koo
We present a penalized log-density estimation method using Legendre polynomials with lasso penalty to adjust estimate's smoothness. Re-expressing the logarithm of the density estimator via a linear combination of Legendre polynomials, we can estimate parameters by maximizing the penalized log-likelihood function. Besides, we proposed an implementation strategy that builds on the coordinate decent algorithm, together with the Bayesian information criterion (BIC).