Imports: dplyr (>= 0.5.0), extraDistr, ggplot2, grid, rstan, scales,

an additive model where non-linear trends are fit with yearly, weekly, and
daily seasonality, plus holiday effects. It works best with time series
that have strong seasonal effects and several seasons of historical data.
Prophet is robust to missing data and shifts in the trend, and typically
Imports: dplyr (>= 0.5.0), dygraphs (>= 1.1.1.4), extraDistr, ggplot2,
grid, rstan (>= 2.14.0), scales, stats, tidyr (>= 0.6.1), xts

grid, rstan (>= 2.14.0), scales, stats, tidyr (>= 0.6.1),

person("Sean", "Taylor", email = "sjtz@pm.me", role = c("cre", "aut")),
Imports: dplyr (>= 0.7.7), dygraphs (>= 1.1.1.4), extraDistr, ggplot2,

grid, methods, rstan (>= 2.18.1), rstantools (>= 2.0.0),
Suggests: knitr, testthat, readr, rmarkdown

grid, lubridate, methods, RcppParallel (>= 5.0.1), rstan (>=
2.18.1), rstantools (>= 2.0.0), scales, StanHeaders, stats,

Title: Inference on Single Binomial Proportion and Bayesian
Description: Abundant statistical literature has revealed the importance of constructing and evaluating various methods for constructing confidence intervals (CI) for single binomial proportion (p). We comprehensively provide procedures in frequentist (approximate with or without adding pseudo counts or continuity correction or exact) and in Bayesian cultures. Evaluation procedures for CI warrant active computational attention and required summaries pertaining to  four criterion (coverage probability, expected length, p-confidence, p-bias, and error) are implemented.

Packaged: 2017-05-03 17:16:53 UTC; RajeswaranV

Title: Calculating Proportionality Between Vectors of Compositional
person("David", "Lovell", email = "david.lovell@qut.edu.au", role = "aut")
Description: The bioinformatic evaluation of gene co-expression often begins with
correlation-based analyses. However, this approach lacks statistical validity
when applied to relative data, including those biological count data produced by
microarray assays or high-throughput RNA-sequencing. As an alternative, Lovell
et al propose a proportionality metric, phi, derived from compositional data
analysis, a branch of math dealing specifically with relative data. In a
subsequent publication, Erb and Nicodemus expounded these efforts by elaborating
on another proportionality metric, rho. This package introduces a programmatic
framework for the calculation of feature dependence using proportionality and
other compositional data methods discussed in the cited publications.
Suggests: compositions, dendextend, ggplot2, ggthemes, knitr,