Package: bpbounds 0.1.6

bpbounds: Nonparametric Bounds for the Average Causal Effect Due to Balke and Pearl and Extensions

Implementation of the nonparametric bounds for the average causal effect under an instrumental variable model by Balke and Pearl (Bounds on Treatment Effects from Studies with Imperfect Compliance, JASA, 1997, 92, 439, 1171-1176, <doi:10.2307/2965583>). The package can calculate bounds for a binary outcome, a binary treatment/phenotype, and an instrument with either 2 or 3 categories. The package implements bounds for situations where these 3 variables are measured in the same dataset (trivariate data) or where the outcome and instrument are measured in one study and the treatment/phenotype and instrument are measured in another study (bivariate data).

Authors:Tom Palmer [aut, cre], Roland Ramsahai [aut], Vanessa Didelez [aut], Nuala Sheehan [aut]

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bpbounds.pdf |bpbounds.html
bpbounds/json (API)
NEWS

# Install bpbounds in R:
install.packages('bpbounds', repos = c('https://remlapmot.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/remlapmot/bpbounds/issues

On CRAN:

aceaverage-causal-effectboundsinstrumental-variableivmendelianmendelian-randomisationmendelian-randomizationmendelianrandomisationmendelianrandomizationmrnonparametricnonparametric-boundspearlshiny

2 exports 1.65 score 0 dependencies 242 downloads

Last updated 5 days agofrom:efe276d564aa27f9fadcdc8bfa5795a97ea538f2

Exports:bpboundsrunExample

Dependencies:

Nonparametric bounds for the average causal effect: bpbounds examples

Rendered frombpbounds.Rmdusingknitr::rmarkdownon Jun 13 2024.

Last update: 2023-10-10
Started: 2018-10-25