R.cache (>= 0.12.0), matrixStats (>= 0.52.2), aroma.light (>=
SuggestsNote: BioC (>= 3.1), Recommended: Hmisc

R.cache (>= 0.13.0), matrixStats (>= 0.54.0), aroma.light (>=

R.cache (>= 0.15.0), matrixStats (>= 0.61.0), aroma.light (>=
1.22.1), parallel, graphics, grDevices, stats, utils
Suggests: Hmisc, R.rsp (>= 0.44.0), R.devices (>= 2.17.0), ggplot2 (>=
person("Frank E", "Harrell Jr", role="ctb",
comment = "Weighted quantile estimator adopted from Hmisc::wtd.quantile()"))
Frank E Harrell Jr [ctb] (Weighted quantile estimator adopted from

Title: Parent Specific DNA Copy Number Estimation
Author: Hao Chen, Haipeng Xing, and Nancy R. Zhang
Description: This package estimate parent specific DNA copy number for
human genomes using high-density SNP arrays data, that is, the
data has two component, logR and B-allele frequency.

Title: Algorithm for Pattern Sequence Based Forecasting
Description: Various functions in Pattern Sequence Based Forecasting (PSF) takes
time series data as input and assist to forecast the future value with
the help of Algorithm PSF. This algorithm forecasts the behavior of time series
based on similarity of pattern sequences. Initially, clustering is done with the
labeling of samples from database. The labels associated with samples are then
used for forecasting the future behaviour of time series data. The further
technical details and references regarding PSF are discussed in Vignette.

Title: Forecasting of Univariate Time Series Using the Pattern
Author: Neeraj Bokde, Gualberto Asencio-Cortes and Francisco Martinez-Alvarez
Description: Pattern Sequence Based Forecasting (PSF) takes univariate
time series data as input and assist to forecast its future values.
This algorithm forecasts the behavior of time series
Imports: data.table, cluster, knitr, forecast

Packaged: 2017-04-18 19:31:20 UTC; gualberto

Title: Projected Subset Gradient Descent
a greedy projected subset gradient descent algorithm. The sparsity
and diversity tuning parameters are selected by cross-validation.

Title: Portfolio Safeguard: Optimization, Statistics and Risk
Authors@R: c(person("Stan", "Uryasev", role=c("aut", "cre","cph"), email = "stan.uryasev@aorda.com"),
person("Grigoriy", "Zrazhevsky", role = "aut"),
person("Viktor", "Kuzmenko", role = "aut"),
person("Alex", "Zrazhevsky", role = "aut"))
Description: Solves optimization, advanced statistics, and risk management problems. Popular nonlinear functions in financial, statistical, and logistics applications are pre-coded (e.g., Standard Deviation, Entropy, Expected Shortfall (ES), Value-at-Risk (VaR), Conditional Value-at-Risk (CVaR), Probability of Exceedance (POE), Buffered Probability of Exceedance (bPOE), Partial Moment (PM), Drawdown, Mean-Squared Error, see, the list <http://www.aorda.com/html/PSG_Help_HTML/index.html?function.htm> ). 'PSGExpress' is the 'Portfolio Safeguard (PSG)' freeware version with the number of variables in functions less or equal to 10, see <http://www.aorda.com>.