Version History¶
0.1.3¶
- Fixed an bug causing a float error
- Fixed an bug caused by numpy not rounding Euclidean Distances of 0 to 0 (resulting in negative Euclidean distances that cannot be square-rooted)
0.1.2¶
- More Python 3.x compatibility
- Typos in examples
0.1.1¶
- More Python 3.x compatibility
- Fixed the transform function to not alter the original data matrix
0.1.0¶
- Updated the diversity function
- div_partition function for calculating alpha, beta, and gamma diversity
- spatial_median function for calculation multivariate medians
- fixed a bug in MDS function that provided incorrect results when using monotone transformation
- beta_dispersion function for assessing homogeneity of variances of distance matrices
0.0.9¶
- Missing data imputation
- nls Python 3 compatibility
- Gower’s Euclidean distance for missing data
- ord_plot function for convex hull and line plots of ordination results
- Fully incorporated non-linear regression, including documentation
- Incorporated partial Mantel test in Mantel class
- Global tests of RDA significance
- Updated CCA to include correspondence analysis of residual (unconstrained) variance
- Global tests of CCA significance
0.0.8¶
- Updated PCA to use SVD instead of eigen decomposition
0.0.7¶
- CCor
- CCA
- RDA
- RLQ analysis
- Hill and Smith ordination
- weighted mean, variance, scaling
0.0.6¶
- procrustes test of matrix associations
- anosim class for analysis of similarity
- mantel class for Mantel tests
- corner4 class for fourth corner analysis
- load_data function for importing datasets
0.0.5¶
- poca class for princple coordinate analysis
- MDS class for multidimensional scaling (uses isotonic regression from scikit-learn)
- small changes and fixes to previous functions
0.0.4¶
- ca class for simple correspondance analysis
0.0.3¶
- diversity function for calculation species diversity
- rarefy function for rarefaction
0.0.2¶
- distance function for calculating distance matrices using a wide variety of coefficients and metrics
- transform function for transforming matrices
0.0.1¶
- nls class for non-linear regression
- pca class for principle components analysis