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ivmte
View on CRAN: Click
here
Download and install ivmte package within the R console
Install from CRAN:
install.packages("ivmte")
Install from Github:
library("remotes")
install_github("cran/ivmte")
Install by package version:
library("remotes")
install_version("ivmte", "1.4.0")
Attach the package and use:
library("ivmte")
Maintained by
Joshua Shea
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-02-08
Latest Update: 2021-09-17
Description:
The marginal treatment effect was introduced by Heckman and
Vytlacil (2005) to provide a
choice-theoretic interpretation to instrumental variables models that
maintain the monotonicity condition of Imbens and Angrist (1994)
. This interpretation can be used to extrapolate from
the compliers to estimate treatment effects for other subpopulations. This
package provides a flexible set of methods for conducting this
extrapolation. It allows for parametric or nonparametric sieve estimation,
and allows the user to maintain shape restrictions such as monotonicity. The
package operates in the general framework developed by Mogstad, Santos and
Torgovitsky (2018) , and accommodates either point
identification or partial identification (bounds). In the partially
identified case, bounds are computed using either linear programming
or quadratically constrained quadratic programming. Support for
four solvers is provided. Gurobi and the Gurobi R API
can be obtained from . CPLEX can be obtained
from . CPLEX R APIs 'Rcplex'
and 'cplexAPI' are available from CRAN. MOSEK and the MOSEK R API can be
obtained from . The lp_solve library is freely
available from , and is included when
installing its API 'lpSolveAPI', which is available from CRAN.
How to cite:
Joshua Shea (2019). ivmte: Instrumental Variables: Extrapolation by Marginal Treatment Effects. R package version 1.4.0, https://cran.r-project.org/web/packages/ivmte. Accessed 22 Dec. 2024.
Previous versions and publish date:
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Other R packages that ivmte depends,
imports, suggests or enhances
Complete documentation for ivmte
Functions, R codes and Examples using
the ivmte R package
Some associated functions: AE . altDefSplinesBasis . argstring . audit . bX . bound . boundCI . boundPvalue . checkU . classFormula . classList . combinemonobound . constructConstant . criterionMin . design . extractcols . fmtResult . funEval . genBasisSplines . genGamma . genGammaSplines . genGammaSplinesTT . genGammaTT . genSSet . genTarget . genWeight . genboundA . gendist1 . gendist1e . gendist2 . gendist3 . gendist3e . gendist4 . gendist5e . gendist6e . gendistBasic . gendistCovariates . gendistMosquito . gendistSplines . genej . gengrid . genmonoA . genmonoboundA . getXZ . gmmEstimate . interactSplines . isfunctionstring . ivEstimate . ivmte . ivmteEstimate . ivmteSimData . l . lpSetup . lpSetupBound . lpSetupCriterion . lpSetupCriterionBoot . lpSetupEqualCoef . lpSetupInfeasible . lpSetupSolver . mInt . magnitude . matrixTriplets . modcall . momentMatrix . monoIntegral . negationCheck . olsj . optionsCplexAPI . optionsCplexAPISingle . optionsCplexAPITol . optionsGurobi . optionsLpSolveAPI . optionsRmosek . parenthBoolean . permute . permuteN . piv . polyProduct . polyparse . popmean . print.ivmte . propensity . qpSetup . qpSetupBound . qpSetupCriterion . qpSetupInfeasible . removeSplines . rescaleX . restring . rhalton . runCplexAPI . runGurobi . runLpSolveAPI . runMosek . sOls1d . sOls2d . sOls3 . sOlsSplines . sTsls . sTslsSplines . sWald . selectViolations . splineInt . splineUpdate . splinesBasis . statusString . subsetclean . summary.ivmte . symat . tsls . uSplineBasis . uSplineInt . unstring . vecextract . wAttSplines . wate1 . watt1 . watu1 . weights . wgenlate1 . whichforlist . wlate1 .
Some associated R codes: audit.R . callcheck.R . data.R . design.R . ivlike.R . lists.R . lp.R . modcall.R . monobound.R . mst.R . mtr.R . propensity.R . splines.R . sweights.R . testdata.R . testfunctions_covariates.R . testfunctions_splines.R . wweights.R . Full ivmte package functions and examples
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