Package: localIV 0.3.1
localIV: Estimation of Marginal Treatment Effects using Local Instrumental Variables
In the generalized Roy model, the marginal treatment effect (MTE) can be used as a building block for constructing conventional causal parameters such as the average treatment effect (ATE) and the average treatment effect on the treated (ATT). Given a treatment selection equation and an outcome equation, the function mte() estimates the MTE via the semiparametric local instrumental variables method or the normal selection model. The function mte_at() evaluates MTE at different values of the latent resistance u with a given X = x, and the function mte_tilde_at() evaluates MTE projected onto the estimated propensity score. The function ace() estimates population-level average causal effects such as ATE, ATT, or the marginal policy relevant treatment effect.
Authors:
localIV_0.3.1.tar.gz
localIV_0.3.1.zip(r-4.7)localIV_0.3.1.zip(r-4.6)localIV_0.3.1.zip(r-4.5)
localIV_0.3.1.tgz(r-4.6-any)localIV_0.3.1.tgz(r-4.5-any)
localIV_0.3.1.tar.gz(r-4.7-any)localIV_0.3.1.tar.gz(r-4.6-any)
localIV_0.3.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
localIV/json (API)
NEWS
| # Install 'localIV' in R: |
| install.packages('localIV', repos = c('https://xiangzhou09.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/xiangzhou09/localiv/issues
- toydata - A Hypothetical Dataset for Illustrative Purpose
Last updated from:23d308ab0b. Checks:7 NOTE, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | NOTE | 137 | ||
| source / vignettes | OK | 175 | ||
| linux-release-x86_64 | NOTE | 144 | ||
| macos-release-arm64 | NOTE | 84 | ||
| macos-oldrel-arm64 | NOTE | 84 | ||
| windows-devel | NOTE | 84 | ||
| windows-release | NOTE | 100 | ||
| windows-oldrel | NOTE | 80 | ||
| wasm-release | OK | 130 |
Exports:acemtemte_atmte_localIVmte_normalmte_tilde_at
Dependencies:abindbackportsbootbroomcarcarDataclicolorspacecowplotcpp11DerivdigestdoBydplyrfarverforecastFormulafracdiffgenericsggplot2gluegtableisobandKernSmoothlabelinglatticelifecyclelme4lmtestmagrittrMASSMatrixMatrixModelsmaxLikmgcvmicrobenchmarkminqamiscToolsmodelrmvtnormnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpurrrquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangS7sampleSelectionsandwichscalesSparseMstringistringrsurvivalsystemfittibbletidyrtidyselecttimeDateurcautf8vctrsVGAMviridisLitewithrzoo
