betting_cs: Betting confidence sequence

In WannabeSmith/drconfseq: Confidence sequences for the average treatment effect

Betting confidence sequence

Description

Betting confidence sequence

Usage

betting_cs(
  x,
  alpha,
  breaks = 1000,
  running_intersection = FALSE,
  parallel = FALSE,
  convex_comb = FALSE,
  theta = 1/2,
  trunc_scale = 1/2,
  m_trunc = TRUE
)

Arguments

x	A vector of data (a real vector).
alpha	The desired type-I error level (a real in (0, 1)).
breaks	The number of breaks to use for the CS.
running_intersection	Whether to use the running intersection
parallel	Whether to use parallel processing
convex_comb	Whether to use convex combination
theta	The convex combination parameter
trunc_scale	The truncation scale
m_trunc	Whether to scale truncation based on m

Value

A list with two elements, 'l' and 'u', which are vectors of lower and upper confidence bounds, respectively.

WannabeSmith/drconfseq documentation built on Sept. 13, 2023, 3:05 a.m.