Expand description
Functional data operations: mean, center, derivatives, norms, and geometric median.
Structs§
- Deriv2D
Result - Result of 2D partial derivatives.
Enums§
- Normalization
Method - Normalization method for functional data.
Functions§
- center_
1d - Center functional data by subtracting the mean function.
- depth_
based_ median - Return the index of the deepest curve under the Fraiman-Muniz depth measure.
- deriv_
1d - deriv_
2d - Compute 2D partial derivatives for surface data.
- functional_
covariance - Compute the M×M sample covariance matrix of functional data (Bessel-corrected, ddof = n-1).
- functional_
std - Compute pointwise sample standard deviation of functional data (ddof = n-1).
- functional_
variance - Compute pointwise sample variance of functional data (Bessel-corrected, ddof = n-1).
- geometric_
median_ 1d - Compute the geometric median (L1 median) of functional data using Weiszfeld’s algorithm.
- geometric_
median_ 2d - Compute the geometric median for 2D functional data.
- mean_1d
- Compute the mean function across all samples (1D).
- mean_2d
- Compute the mean function for 2D surfaces.
- norm_
lp_ 1d - Compute Lp norm for each sample.
- normalize
- Normalize functional data using the specified method.
- normalize_
with_ argvals - Normalize functional data with an evaluation grid.
- trim_
mean - Compute the depth-trimmed mean of functional data.