Package: scqe 1.0.0

scqe: Stability Controlled Quasi-Experimentation

Functions to implement the stability controlled quasi-experiment (SCQE) approach to study the effects of newly adopted treatments that were not assigned at random. This package contains tools to help users avoid making statistical assumptions that rely on infeasible assumptions. Methods developed in Hazlett (2019) <doi:10.1002/sim.8717>.

Authors:Kirsten Landsiedel [cre], Hazlett Chad [aut], Wulf Ami [ctr], Pinkelman Colleen [ctr], Christopher Gandrud [ctr]

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scqe.pdf |scqe.html✨
scqe/json (API)

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

Peer review:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 3 scripts 159 downloads 9 exports 64 dependencies

Last updated 4 years agofrom:8f216f6ba7. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 20 2024
R-4.5-winOKNov 20 2024
R-4.5-linuxOKNov 20 2024
R-4.4-winOKNov 20 2024
R-4.4-macOKNov 20 2024
R-4.3-winOKNov 20 2024
R-4.3-macOKNov 20 2024

Exports:delta_optim_SCQE_2Cdelta.optim.scqedelta.optim.scqe.1cfulldelta.optim.scqe2scqescqe.1cfullscqe.1csummscqe.2cfullscqe.2csumm

Dependencies:abindAERbackportsbootbroomcarcarDataclicolorspacecowplotcpp11DerivdoBydplyrfansifarverFormulagenericsggplot2gluegtableisobandlabelinglatticelifecyclelme4lmtestmagrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrmunsellnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpurrrquantregR6RColorBrewerRcppRcppEigenrlangsandwichscalesSparseMstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithrzoo