This package performs analysis of variance testing procedures for univariate and multivariate functional data (Cuesta-Albertos and Febrero-Bande (2010) <doi:10.1007/s11749-010-0185-3>, Gorecki and Smaga (2015) <doi:10.1007/s00180-015-0555-0>, Gorecki and Smaga (2017) <doi:10.1080/02664763.2016.1247791>, Zhang et al. (2018) <doi:10.1016/j.csda.2018.05.004>).

The fdisk utilities from the MkLinux project, adopted for Linux/m68k. mac-fdisk allows you to create and edit the partition table of a disk. It supports only the Apple partition format used on Macintosh and PowerMac, use pmac-fdisk for PC partition format disks as used on PowerPC machines. mac-fdisk is an interactive tool with a menu similar to PC fdisk, supported options are somewhat different from PC fdisk due to the differences in partition format.

This package provides tools to estimate tail area-based false discovery rates as well as local false discovery rates for a variety of null models (p-values, z-scores, correlation coefficients, t-scores). The proportion of null values and the parameters of the null distribution are adaptively estimated from the data. In addition, the package contains functions for non-parametric density estimation (Grenander estimator), for monotone regression (isotonic regression and antitonic regression with weights), for computing the greatest convex minorant (GCM) and the least concave majorant (LCM), for the half-normal and correlation distributions, and for computing empirical higher criticism (HC) scores and the corresponding decision threshold.

Implementation of the Interval Testing Procedure for functional data in different frameworks (i.e., one or two-population frameworks, functional linear models) by means of different basis expansions (i.e., B-spline, Fourier, and phase-amplitude Fourier). The current version of the package requires functional data evaluated on a uniform grid; it automatically projects each function on a chosen functional basis; it performs the entire family of multivariate tests; and, finally, it provides the matrix of the p-values of the previous tests and the vector of the corrected p-values. The functional basis, the coupled or uncoupled scenario, and the kind of test can be chosen by the user. The package provides also a plotting function creating a graphical output of the procedure: the p-value heat-map, the plot of the corrected p-values, and the plot of the functional data.

This package provides a versatile package that provides implementation of various methods of Functional Data Analysis (FDA) and Empirical Dynamics. The core of this package is Functional Principal Component Analysis (FPCA), a key technique for functional data analysis, for sparsely or densely sampled random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm. This core algorithm yields covariance and mean functions, eigenfunctions and principal component (scores), for both functional data and derivatives, for both dense (functional) and sparse (longitudinal) sampling designs. For sparse designs, it provides fitted continuous trajectories with confidence bands, even for subjects with very few longitudinal observations. PACE is a viable and flexible alternative to random effects modeling of longitudinal data. There is also a Matlab version (PACE) that contains some methods not available on fdapace and vice versa. Updates to fdapace were supported by grants from NIH Echo and NSF DMS-1712864 and DMS-2014626. Please cite our package if you use it (You may run the command citation("fdapace") to get the citation format and bibtex entry). References: Wang, J.L., Chiou, J., Müller, H.G. (2016) <doi:10.1146/annurev-statistics-041715-033624>; Chen, K., Zhang, X., Petersen, A., Müller, H.G. (2017) <doi:10.1007/s12561-015-9137-5>.

Retrieves financial data from Federal Deposit Insurance Corporation (FDIC)-insured institutions and provides access to the FDIC data taxonomy.

Likelihood based analysis of 1-dimension functional data in a mixed-effects model framework. Matrix computation are approximated by semi-explicit operator equivalents with linear computational complexity. Markussen (2013) <doi:10.3150/11-BEJ389>.

Optimal experimental designs for functional linear and functional generalised linear models, for scalar responses and profile/dynamic factors. The designs are optimised using the coordinate exchange algorithm. The methods are discussed by Michaelides (2023) <https://eprints.soton.ac.uk/474982/1/Thesis_DamianosMichaelides_Final_pdfa_1_.pdf>.

Integrated Functional Depth for Partially Observed Functional Data and applications to visualization, outlier detection and classification. It implements the methods proposed in: Elà as, A., Jiménez, R., Paganoni, A. M. and Sangalli, L. M., (2023), "Integrated Depth for Partially Observed Functional Data", Journal of Computational and Graphical Statistics, <doi:10.1080/10618600.2022.2070171>. Elà as, A., Jiménez, R., & Shang, H. L. (2023), "Depth-based reconstruction method for incomplete functional data", Computational Statistics, <doi:10.1007/s00180-022-01282-9>. Elà as, A., Nagy, S. (2024), "Statistical properties of partially observed integrated functional depths", TEST, <doi:10.1007/s11749-024-00954-6>.

Routines for model-based functional cluster analysis for functional data with optional covariates. The idea is to cluster functional subjects (often called functional objects) into homogenous groups by using spline smoothers (for functional data) together with scalar covariates. The spline coefficients and the covariates are modelled as a multivariate Gaussian mixture model, where the number of mixtures corresponds to the number of clusters. The parameters of the model are estimated by maximizing the observed mixture likelihood via an EM algorithm (Arnqvist and Sjöstedt de Luna, 2019) <doi:10.48550/arXiv.1904.10265>. The clustering method is used to analyze annual lake sediment from lake Kassjön (Northern Sweden) which cover more than 6400 years and can be seen as historical records of weather and climate.

This package provides functions that calculate appropriate sample sizes for one-sample t-tests, two-sample t-tests, and F-tests for microarray experiments based on desired power while controlling for false discovery rates. For all tests, the standard deviations (variances) among genes can be assumed fixed or random. This is also true for effect sizes among genes in one-sample and two sample experiments. Functions also output a chart of power versus sample size, a table of power at different sample sizes, and a table of critical test values at different sample sizes.

This package provides an implementation of concurrent or varying coefficient regression methods for functional data. The implementations are done for both dense and sparsely observed functional data. Pointwise confidence bands can be constructed for each case. Further, the influence of past predictor values are modeled by a smooth history index function, while the effects on the response are described by smooth varying coefficient functions, which are very useful in analyzing real data such as COVID data. References: Yao, F., Müller, H.G., Wang, J.L. (2005) <doi:10.1214/009053605000000660>. Sentürk, D., Müller, H.G. (2010) <doi:10.1198/jasa.2010.tm09228>.

Utility crate for raising file descriptors limit for OSX and Linux

This package provides a Rust library to raise file descriptors limit.

Fd-lock provides an advisory lock on a file using a file descriptor to it.

Fd-lock provides an advisory lock on a file using a file descriptor to it.

This package is an Emacs interface to F-Droid. Its purpose is to aid in the management of F-Droid packages for an Android device or an emulator inside the comfort of Emacs.

The F-Droid server tools provide various scripts and tools that are used to maintain F-Droid, the repository of free Android applications. You can use these same tools to create your own additional or alternative repository for publishing, or to assist in creating, testing and submitting metadata to the main repository.

This package provides a collection of functions for outlier detection in functional data analysis. Methods implemented include directional outlyingness by Dai and Genton (2019) <doi:10.1016/j.csda.2018.03.017>, MS-plot by Dai and Genton (2018) <doi:10.1080/10618600.2018.1473781>, total variation depth and modified shape similarity index by Huang and Sun (2019) <doi:10.1080/00401706.2019.1574241>, and sequential transformations by Dai et al. (2020) <doi:10.1016/j.csda.2020.106960 among others. Additional outlier detection tools and depths for functional data like functional boxplot, (modified) band depth etc., are also available.

An implementation of the methodology described in Petersen and Mueller (2016) <doi:10.1214/15-AOS1363> for the functional data analysis of samples of density functions. Densities are first transformed to their corresponding log quantile densities, followed by ordinary Functional Principal Components Analysis (FPCA). Transformation modes of variation yield improved interpretation of the variability in the data as compared to FPCA on the densities themselves. The standard fraction of variance explained (FVE) criterion commonly used for functional data is adapted to the transformation setting, also allowing for an alternative quantification of variability for density data through the Wasserstein metric of optimal transport.

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>.

This package provides a fast, specialized deflate implementation.

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.

Implementations of functional dynamic principle components analysis. Related graphic tools and frequency domain methods. These methods directly use multivariate dynamic principal components implementation, following the guidelines from Hormann, Kidzinski and Hallin (2016), Dynamic Functional Principal Component <doi:10.1111/rssb.12076>.