1. What does REndo do | 2. Short Description of REndo's Functions | 3. Using REndo | 4. Examples using Real Data | References
Package: REndo 2.5.0
Raluca Gui
REndo: Fitting Linear Models with Endogenous Regressors using Latent Instrumental Variables
Fits linear models with endogenous regressor using latent instrumental variable approaches. The methods included in the package are Lewbel's (1997) <doi:10.2307/2171884> higher moments approach as well as Lewbel's (2012) <doi:10.1080/07350015.2012.643126> heteroscedasticity approach, Park and Gupta's (2012) <doi:10.1287/mksc.1120.0718> joint estimation method that uses Gaussian copula and Kim and Frees's (2007) <doi:10.1007/s11336-007-9008-1> multilevel generalized method of moment approach that deals with endogeneity in a multilevel setting. These are statistical techniques to address the endogeneity problem where no external instrumental variables are needed. See the publication related to this package in the Journal of Statistical Software for more details: <doi:10.18637/jss.v107.i03>. Note that with version 2.0.0 sweeping changes were introduced which greatly improve functionality and usability but break backwards compatibility.
Authors:
REndo_2.5.0.tar.gz
REndo_2.5.0.zip(r-4.7)REndo_2.5.0.zip(r-4.6)REndo_2.5.0.zip(r-4.5)
REndo_2.5.0.tgz(r-4.6-x86_64)REndo_2.5.0.tgz(r-4.6-arm64)REndo_2.5.0.tgz(r-4.5-x86_64)REndo_2.5.0.tgz(r-4.5-arm64)
REndo_2.5.0.tar.gz(r-4.7-arm64)REndo_2.5.0.tar.gz(r-4.7-x86_64)REndo_2.5.0.tar.gz(r-4.6-arm64)REndo_2.5.0.tar.gz(r-4.6-x86_64)
REndo_2.5.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
REndo/json (API)
| # Install 'REndo' in R: |
| install.packages('REndo', repos = c('https://mmeierer.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/mmeierer/rendo/issues
Datasets:
- dataCopCont - Simulated Dataset with One Endogenous Continuous Regressor
- dataCopCont2 - Simulated Dataset with Two Endogenous Continuous Regressor
- dataCopDis - Simulated Dataset with One Endogenous Discrete Regressor
- dataCopDis2 - Simulated Dataset with Two Endogenous Discrete Regressors
- dataCopDisCont - Simulated Dataset with Two Endogenous Regressors
- dataCopIMABinExo - Simulated Dataset for Copula IMA
- dataCopIMAContExo - Simulated Dataset for Copula IMA - Continuous Exogenous Regressor
- dataCopIMAMultiEndo - Simulated Dataset for Copula IMA - Two Continuous Endogenous Regressors, Intercept and No Exogenous Regressor
- dataHetIV - Simulated Dataset with One Endogenous Continuous Regressor
- dataHigherMoments - Simulated Dataset with One Endogenous Regressor
- dataLatentIV - Simulated Dataset with One Endogenous Continuous Regressor
- dataMultilevelIV - Multilevel Simulated Dataset - Three Levels
Last updated from:382afe135d. Checks:13 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 295 | ||
| linux-devel-x86_64 | OK | 326 | ||
| source / vignettes | OK | 227 | ||
| linux-release-arm64 | OK | 266 | ||
| linux-release-x86_64 | OK | 328 | ||
| macos-release-arm64 | OK | 215 | ||
| macos-release-x86_64 | OK | 425 | ||
| macos-oldrel-arm64 | OK | 207 | ||
| macos-oldrel-x86_64 | OK | 389 | ||
| windows-devel | OK | 256 | ||
| windows-release | OK | 253 | ||
| windows-oldrel | OK | 239 | ||
| wasm-release | OK | 172 |
Exports:copulaCorrectioncopulaIMAhetErrorsIVhigherMomentsIVlatentIVmultilevelIV
Dependencies:abindADGofTestAERbackportsbootbroomcarcarDatacliclustercolorspacecopulacorpcorcowplotcpp11data.tableDerivdoBydplyrfarverFNNforecastFormulafracdiffgenericsggplot2gluegslgtableisobandkernlabKernSmoothkslabelinglatticelifecyclelme4lmtestmagrittrMASSMatrixMatrixModelsmclustmgcvminqamodelrmulticoolmvtnormnlmenloptrnnetnumDerivoptimxpbkrtestpcaPPpillarpkgconfigpracmapsplinepurrrquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangS7sandwichscalesSparseMstablediststringistringrsurvivaltibbletidyrtidyselecttimeDateurcautf8vctrsviridisLitewithrzoo
