Package: dmlrs 0.1.0
dmlrs: Fast Double / Debiased Machine Learning Implemented in Rust
Double / debiased machine learning (Chernozhukov et al., 2018, <doi:10.1111/ectj.12097>) for causal inference: partially linear, interactive, instrumental variable and difference-in-differences models with cross-fitted nuisance functions, multiplier bootstrap and sensitivity analysis for omitted confounding. Estimation, including the nuisance learners (lasso, logistic regression, random forests, gradient boosting), runs in Rust through the 'dml-core' crate, in parallel. Results match the Python package 'dml-rs', which is validated against 'DoubleML'.
Authors:
dmlrs_0.1.0.tar.gz
dmlrs_0.1.0.zip(r-4.7-x86_64)dmlrs_0.1.0.zip(r-4.7-arm64)dmlrs_0.1.0.zip(r-4.6-x86_64)dmlrs_0.1.0.zip(r-4.6-arm64)dmlrs_0.1.0.zip(r-4.5-x86_64)
dmlrs_0.1.0.tgz(r-4.6-x86_64)dmlrs_0.1.0.tgz(r-4.6-arm64)dmlrs_0.1.0.tgz(r-4.5-x86_64)dmlrs_0.1.0.tgz(r-4.5-arm64)
dmlrs_0.1.0.tar.gz(r-4.7-arm64)dmlrs_0.1.0.tar.gz(r-4.7-x86_64)dmlrs_0.1.0.tar.gz(r-4.6-arm64)dmlrs_0.1.0.tar.gz(r-4.6-x86_64)
dmlrs_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
dmlrs/json (API)
| # Install 'dmlrs' in R: |
| install.packages('dmlrs', repos = c('https://asadgudzevic94.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/asadgudzevic94/dml-rs/issues
Last updated from:f963926957. Checks:15 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 109 | ||
| linux-devel-x86_64 | OK | 142 | ||
| source / vignettes | OK | 258 | ||
| linux-release-arm64 | OK | 109 | ||
| linux-release-x86_64 | OK | 156 | ||
| macos-release-arm64 | OK | 148 | ||
| macos-release-x86_64 | OK | 367 | ||
| macos-oldrel-arm64 | OK | 148 | ||
| macos-oldrel-x86_64 | OK | 394 | ||
| windows-devel-arm64 | OK | 238 | ||
| windows-devel-x86_64 | OK | 217 | ||
| windows-release-arm64 | OK | 252 | ||
| windows-release-x86_64 | OK | 125 | ||
| windows-oldrel-x86_64 | OK | 139 | ||
| wasm-release | OK | 163 |
Exports:dml_diddml_iivmdml_irmdml_plivdml_plrlrn_gradient_boostinglrn_hist_gradient_boostinglrn_lassolrn_lasso_cvlrn_logisticlrn_logistic_cvlrn_olslrn_random_forestlrn_regression_forestsensitivity_analysis
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Confidence intervals for DML estimates | confint.dmlrs_fit |
| Difference in differences | dml_did |
| Interactive IV model | dml_iivm |
| Interactive regression model | dml_irm |
| Partially linear IV regression | dml_pliv |
| Partially linear regression | dml_plr |
| Built-in nuisance learners | learners lrn_gradient_boosting lrn_hist_gradient_boosting lrn_lasso lrn_lasso_cv lrn_logistic lrn_logistic_cv lrn_ols lrn_random_forest lrn_regression_forest |
| Sensitivity analysis for omitted confounding | sensitivity_analysis |