Parent Specific DNA Copy Number Estimation
Hao Chen, Haipeng Xing, and Nancy R. Zhang
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.

Algorithm for Pattern Sequence Based Forecasting
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.

Forecasting of Univariate Time Series Using the Pattern
Sequence-Based Forecasting (PSF) Algorithm
Neeraj Bokde, Gualberto Asencio-Cortes and Francisco Martinez-Alvarez
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
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.

Functions to generate ensembles of generalized linear models using
a greedy projected subset gradient descent algorithm. The sparsity
and diversity tuning parameters are selected by cross-validation.

Portfolio Safeguard: Optimization, Statistics and Risk
Management
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"))
Stan Uryasev [aut, cre, cph],
Grigoriy Zrazhevsky [aut],
Viktor Kuzmenko [aut],
Alex Zrazhevsky [aut]
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>.