dplyr (>= 0.5.0), extraDistr, ggplot2, grid, rstan, scales,
stats, tidyr, utils, zoo

dplyr (>= 0.5.0), extraDistr, ggplot2, grid, rstan (>=
2.14.0), scales, stats, tidyr (>= 0.6.1)

Implements a procedure for forecasting time series data based on
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
handles outliers well.
dplyr (>= 0.5.0), dygraphs (>= 1.1.1.4), extraDistr, ggplot2,
grid, rstan (>= 2.14.0), scales, stats, tidyr (>= 0.6.1), xts

dplyr (>= 0.5.0), dygraphs (>= 1.1.1.4), extraDistr, ggplot2,
grid, rstan (>= 2.14.0), scales, stats, tidyr (>= 0.6.1),
utils, xts

c(
person("Sean", "Taylor", email = "sjtz@pm.me", role = c("cre", "aut")),
person("Ben", "Letham", email = "bletham@fb.com", role = "aut")
)
dplyr (>= 0.7.7), dygraphs (>= 1.1.1.4), extraDistr, ggplot2,
grid, rstan (>= 2.14.0), scales, stats, tidyr (>= 0.6.1), xts

dplyr (>= 0.7.7), dygraphs (>= 1.1.1.4), extraDistr, ggplot2,
grid, methods, rstan (>= 2.18.1), rstantools (>= 2.0.0),
scales, stats, tidyr (>= 0.6.1), xts

dplyr (>= 0.7.7), dygraphs (>= 1.1.1.4), extraDistr, ggplot2,
grid, lubridate, methods, RcppParallel (>= 5.0.1), rstan (>=
2.18.1), rstantools (>= 2.0.0), scales, StanHeaders, stats,
tidyr (>= 0.6.1), xts

Inference on Single Binomial Proportion and Bayesian
Computations
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.