hal9001
modeltime
| hal9001 | modeltime | |
|---|---|---|
| 1 | 5 | |
| 48 | 501 | |
| - | 1.0% | |
| 5.2 | 8.4 | |
| 15 days ago | 4 months ago | |
| R | R | |
| GNU General Public License v3.0 only | GNU General Public License v3.0 or later |
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hal9001
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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
modeltime
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Cross Validating Time Series Models in R
Check out the ModelTime package: https://business-science.github.io/modeltime/
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Good package or tidy way of sliding time series forecasting windows for backtesting?
I was looking for something similar a bit ago and settled on timetk and modeltime. It's been a while since I worked with these and I never got deep enough in my own project to fully explore them, so unfortunately all I can offer are the links; however this should get you what you're looking for
- Has anyone taken Matt Dancho's courses?
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Time Series in R
It’s actually completely different than what existed. You can see my roadmap here and how much has went into the Modeltime project. https://github.com/business-science/modeltime/issues/5
What are some alternatives?
causalglm - Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning
fable - Tidy time series forecasting
yaglm - A python package for penalized generalized linear models that supports fitting and model selection for structured, adaptive and non-convex penalties.
Deep_XF - Package towards building Explainable Forecasting and Nowcasting Models with State-of-the-art Deep Neural Networks and Dynamic Factor Model on Time Series data sets with single line of code. Also, provides utilify facility for time-series signal similarities matching, and removing noise from timeseries signals.
tmlenet - Targeted Maximum Likelihood Estimation for Network Data
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.resample - Resampling Tools for Time Series Forecasting with Modeltime
healthyR.ts - A time-series companion package to healthyR
Auto_TS - Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Created by Ram Seshadri. Collaborators welcome.