Package: classifly 0.4.1.9000
classifly: Explore Classification Models in High Dimensions
Given $p$-dimensional training data containing $d$ groups (the design space), a classification algorithm (classifier) predicts which group new data belongs to. Generally the input to these algorithms is high dimensional, and the boundaries between groups will be high dimensional and perhaps curvilinear or multi-faceted. This package implements methods for understanding the division of space between the groups.
Authors:
classifly_0.4.1.9000.tar.gz
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classifly.pdf |classifly.html✨
classifly/json (API)
NEWS
| # Install 'classifly' in R: |
| install.packages('classifly', repos = c('https://hadley.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/hadley/classifly/issues
- olives - Olives
Last updated 3 years agofrom:d7608e0c97. Checks:1 OK, 7 NOTE. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Feb 02 2025 |
| R-4.5-win | NOTE | Feb 02 2025 |
| R-4.5-mac | NOTE | Feb 02 2025 |
| R-4.5-linux | NOTE | Feb 02 2025 |
| R-4.4-win | NOTE | Feb 02 2025 |
| R-4.4-mac | NOTE | Feb 02 2025 |
| R-4.3-win | NOTE | Feb 02 2025 |
| R-4.3-mac | NOTE | Feb 02 2025 |
Exports:advantageclassiflyclassifyexploregenerate_classification_dataknnfposteriorsimvarvariables
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Calculate the advantage the most likely class has over the next most likely. | advantage |
| Classifly provides a convenient method to fit a classification function and then explore the results in the original high dimensional space. | classifly package-classifly |
| Default method for exploring objects | explore |
| Generate classification data. | generate_classification_data |
| Generate new data from a data frame. | generate_data |
| A wrapper function for 'knn' to allow use with classifly. | knnf |
| Olives | olives |
| Extract posterior group probabilities | posterior |
| Simulate observations from a vector | simvar |
| Extract predictor and response variables for a model object. | variables |
