Author: Jens J. Rogmann, University of Hamburg, Department of
Maintainer: Jens J. Rogmann <Jens.Rogmann@uni-hamburg.de>
Description: Computes ordinal alternatives to mean comparison (Cliff's

Author: Jens J. Rogmann, University of Hamburg, Department of Psychology, Germany
Description: Computes ordinal alternatives to mean comparison (Cliff's Delta)

Delta, Vargha and Delaney's A, Common Language Effect Size /

Description: Computes ordinal, nonparametric alternatives to mean
comparison (Cliff's delta or success rate difference (SRD),
Vargha and Delaney's A or success rate difference (SRD), the
descrete type of McGraw & Wong's Common Language Effect Size or
Grissom & Kims Probability of Superiority, and the Number

Description: Computes ordinal, statistics and effect sizes as an
alternative to mean comparison: Cliff's delta or success rate
difference (SRD), Vargha and Delaney's A or the Area Under a
Receiver Operating Characteristic Curve (AUC), the discrete
type of McGraw & Wong's Common Language Effect Size (CLES) or
Grissom & Kim's Probability of Superiority (PS), and the Number
needed to treat (NNT) effect size. Moreover, comparisons to
Cohen's d are offered based on Huberty & Lowman's Percentage of

Depends: R (>= 2.11.0), methods, graphics, lattice, hash, grid
Author: Andrew Liu, Khanh Nguyen, Dave Kane
Description: Functions for visualizing and retrieving data for the
state of an orderbook at a particular period in time.

Depends: R (>= 2.15.0), methods, graphics, lattice, hash, grid
Author: Andrew Liu, Khanh Nguyen, David Kane

Title: Ordered Lasso and Time-Lag Sparse Regression
Author: Jerome Friedman, Xiaotong Suo and Robert Tibshirani
Description: Ordered lasso and time-lag sparse regression. Ordered Lasso fits a
linear model and imposes an order constraint on the coefficients. It writes
the coefficients as positive and negative parts, and requires positive
parts and negative parts are non-increasing and positive. Time-Lag Lasso
generalizes the ordered Lasso to a general data matrix with multiple
predictors. For more details, see Suo, X.,Tibshirani, R., (2014) 'An
Ordered Lasso and Sparse Time-lagged Regression'.