hermiter
Efficient Sequential and Batch Estimation of Univariate and Bivariate Probability Density Functions and Cumulative Distribution Functions along with Quantiles (Univariate) and Nonparametric Correlation (Bivariate) (by MikeJaredS)
causalglm
Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning (by tlverse)
| hermiter | causalglm | |
|---|---|---|
| 3 | 2 | |
| 15 | 17 | |
| - | - | |
| 4.5 | 0.0 | |
| about 2 months ago | about 2 years ago | |
| R | R | |
| GNU General Public License v3.0 or later | 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.
hermiter
Posts with mentions or reviews of hermiter.
We have used some of these posts to build our list of alternatives
and similar projects.
causalglm
Posts with mentions or reviews of causalglm.
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] Sensitivity of (Causal) Inference to Nonlinear Functional Form
Why not both? https://tlverse.org/causalglm/ (Will replace this with a more informative comment when I have free time later today)
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[Q] Should G-methods, IPTW always be used over traditional regression?
This package: https://github.com/tlverse/causalglm was recently developed to fill the gap between fully black box causal learning methods for heterogeneous treatment effects and fully parametric generalized linear model approaches. It allows for both semiparametric and nonparametric robust causal inference for user defined “working parametric models” for the estimands of interest. It is still black box in that non relevant features of the data distribution are estimated using machine learning but the relevant conditional parameters are modeled fully parametrically (with nonparametric robust inference when misspecified). It is very new so use with caution.