Depends: R (>= 2.12.0), orthogonalsplinebasis (>= 0.1.2), methods,
Description: Paired functional data analysis via principle component
analysis using orthogonalized spline basis functions.

Title: Penalized Independent Component Analysis for Univariate
Author: Marc Vidal Badia, Ana Mª Aguilera del Pino
Maintainer: Marc Vidal Badia <marc.vidalbadia@ugent.be>
Description: Performs penalized independent component analysis for univariate functional data. Two alternative models are implemented, both based on the fourth order blind identification method.

Title: Independent Component Analysis for Univariate Functional Data
Description: Performs penalized independent component analysis for univariate functional data [<doi:10.3390/math9111243>].

Title: Persistence Flamelets: Computation and Inferential Tools
Description: Computes the Persistence Flamelets, a statistical tool for exploring the Topological Invariants of Scale-Space families introduced in Padellini and Brutti (2017) "Persistence Flamelets: multiscale Persistent Homology for kernel density exploration" <arXiv:1709.07097>.
Flamelets can be built from either sub/super level sets of arbitrary functions or
from a precomputed list of Persistence Diagrams.
In addition, this package provides functions to compute confidence bands for a Flamelet via bootstrap, assess significance of each feature,
clean a Flamelet from topological noise, perform a two sample permutation test for groups of Flamelets and it also implements a topological heuristic for bandwidth selection for kernel density estimators.
Imports: TDA, plotly, viridis, pbapply, graphics, stats, abind
Packaged: 2020-12-15 19:28:34 UTC; tulliapadellini

person("Griffin", "Barrett", email = "griffin-barrett@outlook.com",
person("Valentin", "Lucet", email = "valentin.lucet@gmail.com",
person("Josie", "Hughes", email = "josie.hughes@ec.gc.ca",
person(paste0("Her Majesty the Queen in Right of Canada, as represented by",
" the Minister of Environment and Climate Change"),
Description: Provides functions to create and apply convolution kernels to
rectangular data (matrix, raster). Implements calculations of convolutions
in parallel, emphasizing performance over convolution function diversity.
Moving window calculations are common in many fields, from image processing
to raster data manipulation. The process of ingesting values in the
neighborhood of a each cell in a grid, passing them through a function,
and returning a reduced value, is embarrassingly parallel. This package
implements this with parallel C code. In doing so, it provides a much faster
option than other similarly aimed packages and functions.
Suggests: testthat (>= 3.0.0), starsExtra, raster, terra, covr, knitr,
Minister of Environment and Climate Change [cph]