Imports: FNN, ggplot2, graphics, grDevices, gridExtra, gtools, plyr,

Imports: FNN, ggplot2, graphics, grDevices, gridExtra, plyr, proxy,

Description: muscor is a package that solves a number of convex/non-convex sparse regularization formulations for regression or binary classification using multi-stage convex relaxation algorithm, described in the paper "Multi-stage Convex Relaxation for Learning with Sparse Regularization". Loss functions include least squares, logistic, truncated ls/huber. Regularization conditions include Lp + L2 (p in (0,1]), or capped-L1.
Packaged: Sat Nov  8 10:22:36 2008; hornik

Title: Learn and Experiment with Music Theory
Authors@R: person(given = "Efstathios D.", family = "Gennatas", role = c("aut", "cre"), email = "Efstathios.Gennatas@ucsf.edu")
Author: Efstathios D. Gennatas [aut, cre]
Maintainer: Efstathios D. Gennatas <Efstathios.Gennatas@ucsf.edu>
Description: An aid for learning and using music theory. You can build chords, scales, and chord progressions using 12-note equal temperament tuning (12-ET) or user-defined tuning. Includes functions to visualize notes on a piano using ASCII plots in the console and to plot waveforms using base graphics. It allows simple playback of notes and chords using the 'audio' package.

Authors@R: person(given = "Efstathios D.", family = "Gennatas", role = c("aut", "cre"), email = "gennatas@gmail.com")
Maintainer: Efstathios D. Gennatas <gennatas@gmail.com>

Author: Stefano Cacciatore, Edoardo Saccenti, Mario Piccioli
Title: Conversion of Nuclear Magnetic Resonance spectrum in audio file
Description: This package is a collection of function for converting and modifying mono dimensional nuclear magnetic resonance spectra.

Title: Multiscale Climate Model Assessment
Authors@R: person('Martin', 'Hanel', email = 'hanel@fzp.czu.cz', role = c('aut', 'cre'))
Description: Provides functions allowing for (1) easy aggregation of multivariate time series into custom time scales, (2) comparison of statistical summaries between different data sets at multiple time scales (e.g. observed and bias-corrected data), (3) comparison of relations between variables and/or different data sets at multiple time scales (e.g. correlation of precipitation and temperature in control and scenario simulation) and (4) transformation of time series at custom time scales.