hal9001
🤠📿 The Highly Adaptive Lasso (by tlverse)
tmlenet
Targeted Maximum Likelihood Estimation for Network Data (by osofr)
| hal9001 | tmlenet | |
|---|---|---|
| 1 | 1 | |
| 48 | 20 | |
| - | - | |
| 5.2 | 0.0 | |
| 14 days ago | over 6 years ago | |
| R | R | |
| GNU General Public License v3.0 only | - |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
hal9001
Posts with mentions or reviews of hal9001.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-09-12.
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[Q] Should G-methods, IPTW always be used over traditional regression?
Another approach is to make your own SL learner. It turns out to be not as difficult as it may seem to do this. You still pass in the same character string to the SuperLearner functions (e.g. "SL.customlearner") and it will extract the function "SL.customlearner" from your R environment. Here is one example: https://github.com/tlverse/hal9001/blob/devel/R/sl_hal9001.R
tmlenet
Posts with mentions or reviews of tmlenet.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-09-12.
What are some alternatives?
When comparing hal9001 and tmlenet you can also consider the following projects:
causalglm - Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning
yaglm - A python package for penalized generalized linear models that supports fitting and model selection for structured, adaptive and non-convex penalties.
modeltime - Modeltime unlocks time series forecast models and machine learning in one framework
lmtp - :package: Non-parametric Causal Effects Based on Modified Treatment Policies :crystal_ball:
modeltime.resample - Resampling Tools for Time Series Forecasting with Modeltime