### R/`sl3`

*An R package providing a modern re-implementation
of the Super Learner algorithm for ensemble modeling and stacked regression
based on machine learning pipelines.*

Joint work with Jeremy Coyle, Ivana
Malenica, and Oleg
Sofrygin.

[Docs] |
[GitHub]

### R/`origami`

*An R package providing a general framework for
the application of various cross-validation schemes to arbitrary functions,
facilitating the extension of cross-validation procedures to numerous
applications.*

Joint work with Jeremy Coyle.

[Docs] |
[GitHub] |
[CRAN]

### R/`hal9001`

*An R package providing a fast and efficient
implementation of the Highly Adaptive Lasso (HAL), a nonparametric regression
estimator with optimality guarantees useful in semiparametric inference.*

Joint work with Jeremy Coyle.

[Docs] |
[GitHub]

### R/`tmle3shift`

*An R package implementing efficient targeted
maximum likelihood estimators of the effects of stochastic interventions
framed as additive shifts on the treatment scale. Estimators are provided for
the effect of a single shift as well as for a grid of proposed shifts, along
with working marginal structural models for performing variable importance
analysis using the Targeted Learning
framework.*

Joint work with Jeremy Coyle and Mark van
der Laan.

[Docs] |
[GitHub]

### R/`medshift`

*An R package implementing several estimators for
the natural (in)direct effect under stochastic interventions, whether in the
form of incremental propensity score shifts or modified treatment policies.
These estimators flexibly extend causal mediation analysis to settings
involving stochastic interventions.*

Joint work with Iván Díaz.

[Docs] |
[GitHub]

### R/`txshift`

*An R package for efficient estimation of and
nonparametric inference on the effects of stochastic interventions, including
in settings with multi-stage sampling designs. This provides a flexible way
to perform nonparametric variable importance analysis on continuous
quantities using the Targeted
Learning framework.*

Joint work with David Benkeser.

[Docs] |
[GitHub]

### R/`survtmle`

*An R package providing facilities for estimation
and inference in right-censored survival analysis settings with and without
competing risks, including extensions for data-adaptive target parameters,
using Targeted Learning.*

Joint work with David Benkeser.

[Docs] |
[GitHub] |
[CRAN]

### R/`methyvim`

*An R package implementing a framework for using
Targeted Learning to assess evidence
for differential methylation across the genome by estimating variable
importance measures at the level of CpG sites and related functional
units.*

Joint work with Mark van der
Laan and Alan
Hubbard.

[Docs] |
[GitHub] |
[Bioconductor]

### R/`biotmle`

*An R package implementing a set of techniques
for discovering biomarkers from biological sequencing data using a
combination of Targeted Learning and a
generalization of moderated statistics for variance stabilization in finite
samples.*

Joint work with Alan Hubbard.

View the package documentation and related information
[Docs] |
[GitHub] |
[Bioconductor] |

### R/`adaptest`

*An R package for data-adaptive hypothesis testing
in high-dimensional settings. The approach allows for effects to be discovered
(“mined”) from data without loss of valid statistical inference using the
framework of Targeted Learning.*

Joint work with Weixin Cai and Alan
Hubbard.

[GitHub] |
[Bioconductor]

### R/`nima`

*An R package housing Nima’s personal R toolbox,
largely containing miscellaneous convenience functions written to make
statistical computing for research easier.*

[Docs] |
[GitHub] |
[CRAN]