Side by side
forestci vs sjmisc
Both stand on statistical analysis software. Which one you can start free with, which one lets you read the code, and what each costs once you pay.
Free plan APILinux
forestci adds uncertainty calculations directly to supported scikit-learn random-forest estimators. Its calibration option and tree-count requirements require judgment when interpreting results.
Visit forestci Free plan LinuxMacWindows
sjmisc focuses on practical data preparation rather than statistical modeling. Its labelled-data support and tidyverse-compatible transformations make it a focused utility package for R workflows.
Visit sjmiscThe facts
| forestci | sjmisc | |
|---|---|---|
| Free plan | Yes | Yes |
| Open source | Not published | Not published |
| Runs on | API, Linux | Linux, Mac, Windows |
| Lowest paid plan | Free or on request | Free or on request |
| Plans published | 1 | 1 |
| Maker | Not published | Not published |
| Website | contrib.scikit-learn.org | strengejacke.github.io |
| On statistical analysis software | #42 of 106 | #38 of 106 |
forestci: what it does well
- Free MIT-licensed package
- Works with regression and classification forests
- Optional Empirical Bayes calibration
- Supports scikit-learn 1.0 through 1.9
- Includes regression and classification examples
What to know first
- Requires numpy, scipy, and scikit-learn
- Confidence intervals need many trees
- Calibration may worsen empirical agreement
- Linux API package
sjmisc: what it does well
- Free GPL-3 licensed R package
- Supports labelled SPSS and Stata data
- Works with dplyr and tidyverse pipes
- Handles recoding and missing values
- Available through CRAN or GitHub
What to know first
- Requires R version 3.4 or later
- Provides no statistical tests
- Provides no regression models
- Provides no Bayesian analysis
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