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
Group (Non-)Overlap considerations.

R (>= 2.11.0), methods, graphics, lattice, hash, grid
Functions for visualizing and retrieving data for the
state of an orderbook at a particular period in time.

R (>= 2.15.0), methods, graphics, lattice, hash, grid

Ordered Lasso and Time-Lag Sparse Regression
Jerome Friedman, Xiaotong Suo and Robert Tibshirani
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'.

Ordered Lasso and Time-lag Sparse Regression

Test, Check, Verify, Investigate the Monotonic Properties of
Vectors
Functions to test/check/verify/investigate the ordering of vectors.
The 'is_[strictly_]*' family of functions test vectors for
'sorted', 'monotonic', 'increasing', 'decreasing' order; 'is_constant'
and 'is_incremental' test for the degree of ordering. `ordering`
provides a numeric indication of ordering -2 (strictly decreasing) to
2 (strictly increasing).

Order, create and store reports from R.  By defining a
lightweight interface around the inputs and outputs of an
analysis, a lot of the repetitive work for reproducible research
can be automated.  We define a simple format for organising and
describing work that facilitates collaborative reproducible
research and acknowledges that all analyses are run multiple
times over their lifespans.
c(person("Rich", "FitzJohn", role = c("aut", "cre"),
email = "rich.fitzjohn@gmail.com"),
person("Robert", "Ashton", role = "aut"),
person("Alex", "Hill", role = "aut"),
person("Martin", "Eden", role = "aut"),
person("Wes", "Hinsley", role = "aut"),
person("Emma", "Russell", role = "aut"),
person("James", "Thompson", role = "aut"),
person("Imperial College of Science, Technology and Medicine",
role = "cph"))
DBI, R6, RSQLite, digest, docopt, fs (>= 1.2.7), ids, withr,
yaml, zip (>= 2.0.0)
httr, jsonlite, knitr, mockery, processx, rmarkdown,
testthat, vaultr (>= 1.0.0)
Rich FitzJohn [aut, cre],
Robert Ashton [aut],
Alex Hill [aut],
Martin Eden [aut],
Wes Hinsley [aut],
Emma Russell [aut],
James Thompson [aut],
Imperial College of Science, Technology and Medicine [cph]