hal9001
modeltime.resample
| hal9001 | modeltime.resample | |
|---|---|---|
| 1 | 1 | |
| 48 | 17 | |
| - | - | |
| 5.2 | 6.2 | |
| 14 days ago | 4 months ago | |
| R | R | |
| GNU General Public License v3.0 only | GNU General Public License v3.0 or later |
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
-
[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
modeltime.resample
What are some alternatives?
causalglm - Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning
timetk - Time series analysis in the `tidyverse`
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
tmlenet - Targeted Maximum Likelihood Estimation for Network Data
modeltime.ensemble - Time Series Ensemble Forecasting
modeltime.gluonts - GluonTS Deep Learning with Modeltime
lmtp - :package: Non-parametric Causal Effects Based on Modified Treatment Policies :crystal_ball:
boostime - The Tidymodels Extension for Time Series Boosting Models
modeltime.h2o - Forecasting with H2O AutoML. Use the H2O Automatic Machine Learning algorithm as a backend for Modeltime Time Series Forecasting.