Implementations of the k-means, hierarchical agglomerative and DBSCAN clustering methods for functional data which allows for jointly aligning and clustering curves. It supports functional data defined on one-dimensional domains but possibly evaluating in multivariate codomains. It supports functional data defined in arrays but also via the fd and funData classes for functional data defined in the fda and funData packages respectively. It currently supports shift, dilation and affine warping functions for functional data defined on the real line and uses the SRVF framework to handle boundary-preserving warping for functional data defined on a specific interval. Main reference for the k-means algorithm: Sangalli L.M., Secchi P., Vantini S., Vitelli V. (2010) "k-mean alignment for curve clustering" <doi:10.1016/j.csda.2009.12.008>. Main reference for the SRVF framework: Tucker, J. D., Wu, W., & Srivastava, A. (2013) "Generative models for functional data using phase and amplitude separation" <doi:10.1016/j.csda.2012.12.001>.

Defines a collection of functions to compute average power and sample size for studies that use the false discovery rate as the final measure of statistical significance.

Shiny app for the fdapace package.

Documentation at https://melpa.org/#/fd-dired

safely create multiple ReadStream or WriteStream objects from the same file descriptor

This package provides a backend implementation for the WPE WebKit engine that uses Wayland for graphics output.

This package provides a way to create multiple readable/writable streams based on a single file descriptor, maintaining safety from concurrency issues.

This package provides a library for issuing notifications using FreeDesktop.org's Desktop Notifications protocol. This protocol is supported by services such as Ubuntu's NotifyOSD.

This package performs alignment, PCA, and modeling of multidimensional and unidimensional functions using the square-root velocity framework. This framework allows for elastic analysis of functional data through phase and amplitude separation.

The user can directly compute and display false discovery rates from inputted p-values or z-scores under a variety of assumptions. p.fdr() computes FDRs, adjusted p-values and decision reject vectors from inputted p-values or z-values. get.pi0() estimates the proportion of data that are truly null. plot.p.fdr() plots the FDRs, adjusted p-values, and the raw p-values points against their rejection threshold lines.

These functions were developed to support statistical analysis on functional covariance operators. The package contains functions to: - compute 2-Wasserstein distances between Gaussian Processes as in Masarotto, Panaretos & Zemel (2019) <doi:10.1007/s13171-018-0130-1>; - compute the Wasserstein barycenter (Frechet mean) as in Masarotto, Panaretos & Zemel (2019) <doi:10.1007/s13171-018-0130-1>; - perform analysis of variance testing procedures for functional covariances and tangent space principal component analysis of covariance operators as in Masarotto, Panaretos & Zemel (2022) <arXiv:2212.04797>. - perform a soft-clustering based on the Wasserstein distance where functional data are classified based on their covariance structure as in Masarotto & Masarotto (2023) <doi:10.1111/sjos.12692>.

Documentation at https://melpa.org/#/counsel-fd

Exposes an annotation databases generated from UCSC by exposing these as FeatureDb objects.

FdSymbol is a maths symbol font, designed as a companion to the Fedra family, but it might also fit other contemporary typefaces.

Defines a collection of functions to compute average power and sample size for studies that use the false discovery rate as the final measure of statistical significance. A three-rectangle approximation method of a p-value histogram is proposed to derive a formula to compute the statistical power for analyses that involve the FDR. The methodology paper of this package is under review.

This package is a LaTeX thesis template package for Fudan University. It can make it easy to write theses both in Chinese and English.

This package implements SelectBoost'-style variable selection workflows for functional data analysis. The package provides FDA-native design and preprocessing objects for raw curves, spline-basis expansions, Functional principal component analysis scores, and scalar covariates; grouped stability-selection routines based on repeated subject-level subsampling; multiple selector backends including lasso, group lasso, and sparse-group lasso; FDA-aware grouping functions and calibration helpers for SelectBoost'; method-comparison utilities; a formula interface; simulation, benchmarking, and validation helpers with mapped ground truth; targeted sensitivity-study utilities and shipped benchmark summaries for mean F1 comparisons between FDA-aware and plain SelectBoost workflows; small example datasets; and an optional adapter to the native stability-selection interface from the FDboost package.

It is known that current false discovery rate (FDR) procedures can be very conservative when applied to multiple testing in the discrete paradigm where p-values (and test statistics) have discrete and heterogeneous null distributions. This package implements more powerful weighted or adaptive FDR procedures for FDR control and estimation in the discrete paradigm. The package takes in the original data set rather than just the p-values in order to carry out the adjustments for discreteness and heterogeneity of p-value distributions. The package implements methods for two types of test statistics and their p-values: (a) binomial test on if two independent Poisson distributions have the same means, (b) Fisher's exact test on if the conditional distribution is the same as the marginal distribution for two binomial distributions, or on if two independent binomial distributions have the same probabilities of success.

The package provides font description files for all the many shapes available from the cbfonts collection. The files provide the means whereby the NFSS knows which fonts a LaTeX user is requesting.

Tip: installing texlive-cbfonts will automatically propagate this one.

It computes full conformal, split conformal and multi split conformal prediction regions when the response has functional nature. Moreover, the package also contain a plot function to visualize the output of the split conformal. To guarantee consistency, the package structure mimics the univariate conformalInference package of professor Ryan Tibshirani. The main references for the code are: Diquigiovanni, Fontana, and Vantini (2021) <arXiv:2102.06746>, Diquigiovanni, Fontana, and Vantini (2021) <arXiv:2106.01792>, Solari, and Djordjilovic (2021) <arXiv:2103.00627>.

makeFeatureDbFromUCSC cannot cope with this track, hence a package.

makeFeatureDbFromUCSC cannot cope with this track, hence a package.

FANTOM4 promoters, liftOver'ed from hg18 to hg19, CpGs quantified.

Compiled HumanMethylation27 and HumanMethylation450 annotations.