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:Martin Spindler [cre, aut], Victor Chernozhukov [aut], Christian Hansen [aut], Philipp Bach [ctb]

hdm_0.3.1.tar.gz
hdm_0.3.1.zip(r-4.5)hdm_0.3.1.zip(r-4.4)hdm_0.3.1.zip(r-4.3)
hdm_0.3.1.tgz(r-4.4-any)hdm_0.3.1.tgz(r-4.3-any)
hdm_0.3.1.tar.gz(r-4.5-noble)hdm_0.3.1.tar.gz(r-4.4-noble)
hdm_0.3.1.tgz(r-4.4-emscripten)hdm_0.3.1.tgz(r-4.3-emscripten)
hdm.pdf |hdm.html
hdm/json (API)

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

Peer review:

Bug tracker:https://github.com/martinspindler/hdm/issues

Datasets:

On CRAN:

8.12 score 13 stars 4 packages 542 scripts 1.6k downloads 2 mentions 19 exports 39 dependencies

Last updated 4 years agofrom:51557106c2. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 15 2024
R-4.5-winNOTENov 15 2024
R-4.5-linuxNOTENov 15 2024
R-4.4-winNOTENov 15 2024
R-4.4-macNOTENov 15 2024
R-4.3-winOKNov 15 2024
R-4.3-macOKNov 15 2024

Exports:lambdaCalculationLassoShooting.fitp_adjustprint_coefrlassorlassoATErlassoATETrlassoEffectrlassoEffectsrlassoIVrlassoIVmultrlassoIVselectXrlassoIVselectZrlassoLATErlassoLATETrlassologitrlassologitEffectrlassologitEffectstsls

Dependencies:backportscheckmateclicodetoolscolorspacefansifarverforeachFormulaggplot2glmnetgluegtableisobanditeratorslabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcppRcppEigenrlangscalesshapesurvivaltibbleutf8vctrsviridisLitewithr

High-Dimensional Metrics, lasso

Rendered fromhdm.Rnwusingknitr::knitron Nov 15 2024.

Last update: 2019-05-31
Started: 2019-03-15

Readme and manuals

Help Manual

Help pageTopics
hdm: High-Dimensional Metricshdm-package hdm
AJR data setAJR
BLP data setBLP
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 setcps2012
Eminent Domain data setEminentDomain
Growth data setExample GDP Growth Growth Data GrowthData
Function for Calculation of the penalty parameterlambdaCalculation
Shooting LassoLassoShooting.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 set401(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 disturbancesrlasso rlasso.character rlasso.default rlasso.formula
Functions for estimation of treatment effectsATE 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: InferencerlassoEffect rlassoEffects rlassoEffects.default rlassoEffects.formula
Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and InstrumentsrlassoIV rlassoIV.default rlassoIV.formula rlassoIVmult
Instrumental Variable Estimation with Selection on the exogenous Variables by LassorlassoIVselectX rlassoIVselectX.default rlassoIVselectX.formula
Instrumental Variable Estimation with LassorlassoIVselectZ rlassoIVselectZ.default rlassoIVselectZ.formula
rlassologit: Function for logistic Lasso estimationrlassologit rlassologit.character rlassologit.default rlassologit.formula
rigorous Lasso for Logistic Models: InferencerlassologitEffect rlassologitEffects rlassologitEffects.default rlassologitEffects.formula
Summarizing rlassoEffects fitsprint.summary.rlassoEffects summary.rlassoEffects
Two-Stage Least Squares Estimation (TSLS)tsls tsls.default tsls.formula