Description: Carrying out inferences about any linear combination of proportions and the ratio of two proportions.

Title: Feature (gene) selection based on the Proportional Overlapping
Author: Osama Mahmoud, Andrew Harrison, Aris Perperoglou, Asma Gul, Zardad Khan, Berthold Lausen
Description: A package for selecting the most relevant features (genes) in the high-dimensional binary classification problems. The discriminative features are identified using analyzing the overlap between the expression values across both classes. The package includes functions for measuring the proportional overlapping score for each gene avoiding the outliers effect. The used measure for the overlap is the one defined in the "Proportional Overlapping Score (POS)" technique for feature selection. A gene mask which represents a gene's classification power can also be produced for each gene (feature). The set size of the selected genes might be set by the user. The minimum set of genes that correctly classify the maximum number of the given tissue samples (observations) can be also produced.

Description: Propagation of uncertainty using higher-order Taylor expansion and Monte Carlo simulation.

Depends: R (>= 2.13.0), MASS, tmvtnorm, Rcpp (>= 0.10.1), ff

Depends: R (>= 2.13.0), MASS, tmvtnorm, Rcpp (>= 0.10.1), ff,

Title: Parse Java Properties Files for R Service Bus Applications
Author: Tobias Verbeke, based on read.dcf by R Core Development Team
Description: The properties package allows to parse Java properties files
in the context of R Service Bus applications.

Title: Parse 'Java' Properties Files for 'R Service Bus' Applications
Author: Tobias Verbeke, Daan Seynaeve, based on read.dcf by R Core Development Team
Description: Allows to parse 'Java' properties files in the context of
Packaged: 2018-03-09 12:56:51 UTC; dseynaeve

person("Sean", "Taylor", email = "sjt@fb.com", role = c("cre", "aut")),
person("Ben", "Letham", email = "bletham@fb.com", role = "aut")
Description: Implements a procedure for forecasting time series data based on
an additive model where non-linear trends are fit with yearly and weekly
seasonality, plus holidays.  It works best with daily periodicity data with
at least one year of historical data.  Prophet is robust to missing data,
shifts in the trend, and large outliers.
Imports: dplyr (>= 0.5.0), extraDistr, ggplot2, grid, rstan (>=
2.14.0), scales, stats, tidyr (>= 0.6.1), utils, zoo