Side by side
IKPLS vs logKDE
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 LinuxMacWindows
IKPLS provides PLS algorithms with NumPy and optional JAX implementations, plus scikit-learn integration and accelerated computation. Its preprocessing, weighting, transformation, and differentiation features cover varied modeling workflows.
Visit IKPLS Free plan MacWindows
logKDE focuses on positive-valued kernel density estimation in R, with direct and FFT-based functions plus several kernels and bandwidth options. It does not provide statistical tests, regression models, or Bayesian analysis.
Visit logKDEThe facts
| IKPLS | logKDE | |
|---|---|---|
| Free plan | Yes | Yes |
| Open source | Not published | Not published |
| Runs on | Linux, Mac, Windows | Mac, Windows |
| Lowest paid plan | Not published | Free or on request |
| Plans published | None | 1 |
| Maker | Not published | Not published |
| Website | ikpls.readthedocs.io | andrewthomasjones.com.au |
| On statistical analysis software | #52 of 106 | #56 of 106 |
IKPLS: what it does well
- Free package with Apache-2.0 license
- CPU, GPU, and TPU implementations
- Scikit-learn-conformant wrapper
- Fast cross-validation with leakage-aware preprocessing
- Supports weighted models and reconstruction transforms
What to know first
- Requires Python 3.10 through 3.14
- JAX implementations require an optional extra
- Desktop package requires Python installation
logKDE: what it does well
- Designed for positive-domain data
- Offers direct and FFT estimation
- Six kernel choices listed
- Multiple bandwidth methods
- Available from CRAN
What to know first
- No statistical tests
- No regression models
- No Bayesian analysis
- Estimates only positive values