Package: hdm 0.3.1
hdm: High-Dimensional Metrics
Implementation of selected high-dimensional statistical and econometric methods for estimation and inference. Efficient estimators and uniformly valid confidence intervals for various low-dimensional causal/ structural parameters are provided which appear in high-dimensional approximately sparse models. Including functions for fitting heteroscedastic robust Lasso regressions with non-Gaussian errors and for instrumental variable (IV) and treatment effect estimation in a high-dimensional setting. Moreover, the methods enable valid post-selection inference and rely on a theoretically grounded, data-driven choice of the penalty. Chernozhukov, Hansen, Spindler (2016) <arxiv:1603.01700>.
Authors:
hdm_0.3.1.tar.gz
hdm_0.3.1.zip(r-4.7)hdm_0.3.1.zip(r-4.6)hdm_0.3.1.zip(r-4.5)
hdm_0.3.1.tgz(r-4.6-any)hdm_0.3.1.tgz(r-4.5-any)
hdm_0.3.1.tar.gz(r-4.7-any)hdm_0.3.1.tar.gz(r-4.6-any)
hdm_0.3.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
hdm/json (API)
| # Install 'hdm' in R: |
| install.packages('hdm', repos = c('https://martinspindler.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/martinspindler/hdm/issues
Datasets:
- AJR - AJR data set
- BLP - BLP data set
- cps2012 - Cps2012 data set
- EminentDomain - Eminent Domain data set
- GrowthData - Growth data set
- pension - Pension 401(k) data set
Last updated from:51557106c2. Checks:7 NOTE, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | NOTE | 179 | ||
| source / vignettes | OK | 250 | ||
| linux-release-x86_64 | NOTE | 135 | ||
| macos-release-arm64 | NOTE | 161 | ||
| macos-oldrel-arm64 | NOTE | 141 | ||
| windows-devel | NOTE | 91 | ||
| windows-release | NOTE | 85 | ||
| windows-oldrel | NOTE | 75 | ||
| wasm-release | OK | 129 |
Exports:lambdaCalculationLassoShooting.fitp_adjustprint_coefrlassorlassoATErlassoATETrlassoEffectrlassoEffectsrlassoIVrlassoIVmultrlassoIVselectXrlassoIVselectZrlassoLATErlassoLATETrlassologitrlassologitEffectrlassologitEffectstsls
Dependencies:backportscheckmateclicodetoolscpp11farverforeachFormulaggplot2glmnetgluegtableisobanditeratorslabelinglatticelifecycleMASSMatrixR6RColorBrewerRcppRcppEigenrlangS7scalesshapesurvivalvctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| hdm: High-Dimensional Metrics | hdm-package hdm |
| AJR data set | AJR |
| BLP data set | BLP |
| Coefficients from S3 objects 'rlassoEffects' | coef.rlassoEffects |
| Coefficients from S3 objects 'rlassoIV' | coef.rlassoIV |
| Coefficients from S3 objects 'rlassoIVselectX' | coef.rlassoIVselectX |
| Coefficients from S3 objects 'rlassoIVselectZ' | coef.rlassoIVselectZ |
| cps2012 data set | cps2012 |
| Eminent Domain data set | EminentDomain |
| Growth data set | Example GDP Growth Growth Data GrowthData |
| Function for Calculation of the penalty parameter | lambdaCalculation |
| Shooting Lasso | LassoShooting.fit |
| Multiple Testing Adjustment of p-values for S3 objects 'rlassoEffects' and 'lm' | p_adjust p_adjust.lm p_adjust.rlassoEffects |
| Pension 401(k) data set | 401(k) data pension plans wealth |
| Methods for S3 object 'rlassologit' | methods.rlassologit model.matrix.rlassologit predict.rlassologit print.rlassologit summary.rlassologit |
| Printing coefficients from S3 objects 'rlassoEffects' | print_coef print_coef.rlassoEffects |
| Methods for S3 object 'rlasso' | methods.rlasso model.matrix.rlasso predict.rlasso print.rlasso summary.rlasso |
| Methods for S3 object 'rlassoEffects' | confint.rlassoEffects methods.rlassoEffects plot.rlassoEffects print.rlassoEffects |
| Methods for S3 object 'rlassoIV' | confint.rlassoIV methods.rlassoIV print.rlassoIV summary.rlassoIV |
| Methods for S3 object 'rlassoIVselectX' | confint.rlassoIVselectX methods.rlassoIVselectX print.rlassoIVselectX summary.rlassoIVselectX |
| Methods for S3 object 'rlassoIVselectZ' | confint.rlassoIVselectZ methods.rlassoIVselectZ print.rlassoIVselectZ summary.rlassoIVselectZ |
| Methods for S3 object 'rlassologitEffects' | confint.rlassologitEffects methods.rlassologitEffects print.rlassologitEffects summary.rlassologitEffects |
| Methods for S3 object 'rlassoTE' | confint.rlassoTE methods.rlassoTE print.rlassoTE summary.rlassoTE |
| Methods for S3 object 'tsls' | methods.tsls print.tsls summary.tsls |
| rlasso: Function for Lasso estimation under homoscedastic and heteroscedastic non-Gaussian disturbances | rlasso rlasso.character rlasso.default rlasso.formula |
| Functions for estimation of treatment effects | ATE ate ATET atet LATE late LATET latet rlassoATE rlassoATE.default rlassoATE.formula rlassoATET rlassoATET.default rlassoATET.formula rlassoLATE rlassoLATE.default rlassoLATE.formula rlassoLATET rlassoLATET.default rlassoLATET.formula |
| rigorous Lasso for Linear Models: Inference | rlassoEffect rlassoEffects rlassoEffects.default rlassoEffects.formula |
| Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments | rlassoIV rlassoIV.default rlassoIV.formula rlassoIVmult |
| Instrumental Variable Estimation with Selection on the exogenous Variables by Lasso | rlassoIVselectX rlassoIVselectX.default rlassoIVselectX.formula |
| Instrumental Variable Estimation with Lasso | rlassoIVselectZ rlassoIVselectZ.default rlassoIVselectZ.formula |
| rlassologit: Function for logistic Lasso estimation | rlassologit rlassologit.character rlassologit.default rlassologit.formula |
| rigorous Lasso for Logistic Models: Inference | rlassologitEffect rlassologitEffects rlassologitEffects.default rlassologitEffects.formula |
| Summarizing rlassoEffects fits | print.summary.rlassoEffects summary.rlassoEffects |
| Two-Stage Least Squares Estimation (TSLS) | tsls tsls.default tsls.formula |
