This package provides three methods to generate fully-sequential space-filling designs inside a unit hypercube. A fully-sequential space-filling design means a sequence of nested designs (as the design size varies from one point up to some maximum number of points) with the design points added one at a time and such that the design at each size has good space-filling properties. Two methods target the minimum pairwise distance criterion and generate maximin designs, among which one method is more efficient when design size is large. One method targets the maximum hole size criterion and uses a heuristic to generate what is closer to a minimax design.

This package implements fractional differencing with Autoregressive Moving Average models to analyse long-memory time series data. Traditional ARIMA models typically use integer values for differencing, which are suitable for time series with short memory or anti-persistent behaviour. In contrast, the Fractional ARIMA model allows fractional differencing, enabling it to effectively capture long memory characteristics in time series data. The âfracARMAâ package is user-friendly and allows users to manually input the fractional differencing parameter, which can be obtained using various estimators such as the GPH estimator, Sperio method, or Wavelet method and many. Additionally, the package enables users to directly feed the time series data, AR order, MA order, fractional differencing parameter, and the proportion of training data as a split ratio, all in a single command. The package is based on the reference from the paper of Irshad and others (2024, <doi:10.22271/maths.2024.v9.i6b.1906>).

This package provides a simple and efficient wrapper around the fastest Fourier transform in the west (FFTW) library <http://www.fftw.org/>.

Compute inbreeding coefficients using the method of Meuwissen and Luo (1992) <doi:10.1186/1297-9686-24-4-305>, and numerator relationship coefficients between individuals using the method of Van Vleck (2007) <https://pubmed.ncbi.nlm.nih.gov/18050089/>.

This package provides tools for describing and analysing free sorting data. Main methods are computation of consensus partition and factorial analysis of the dissimilarity matrix between stimuli (using multidimensional scaling approach).

This package contains functions to fetch data from various data sources. The user first creates a catalog of objects from a data source, then fetches data from the catalog. The package provides an easy way to access data from many different types of sources.

This package provides functions to implement the Flexible cFDR (Hutchinson et al. (2021) <doi:10.1371/journal.pgen.1009853>) and Binary cFDR (Hutchinson et al. (2021) <doi:10.1101/2021.10.21.465274>) methodologies to leverage auxiliary data from arbitrary distributions, for example functional genomic data, with GWAS p-values to generate re-weighted p-values.

This package creates participant flow diagrams directly from a dataframe. Representing the flow of participants through each stage of a study, especially in clinical trials, is essential to assess the generalisability and validity of the results. This package provides a set of functions that can be combined with a pipe operator to create all kinds of flowcharts from a data frame in an easy way.

This package provides a collection of functions for calculating Floristic Quality Assessment (FQA) metrics using regional FQA databases that have been approved or approved with reservations as ecological planning models by the U.S. Army Corps of Engineers (USACE). For information on FQA see Spyreas (2019) <doi:10.1002/ecs2.2825>. These databases are stored in a sister R package, fqadata'. Both packages were developed for the USACE by the U.S. Army Engineer Research and Development Centerâ s Environmental Laboratory.

This package provides an implementation of two-dimensional functional principal component analysis (FPCA), Marginal FPCA, and Product FPCA for repeated functional data. Marginal and Product FPCA implementations are done for both dense and sparsely observed functional data. References: Chen, K., Delicado, P., & Müller, H. G. (2017) <doi:10.1111/rssb.12160>. Chen, K., & Müller, H. G. (2012) <doi:10.1080/01621459.2012.734196>. Hall, P., Müller, H.G. and Wang, J.L. (2006) <doi:10.1214/009053606000000272>. Yao, F., Müller, H. G., & Wang, J. L. (2005) <doi:10.1198/016214504000001745>.

Computes likelihood ratios based on pigmentation traits. Also, it allows computing conditional probabilities for unidentified individuals based on missing person characteristics. A set of tailored plots are incorporated to analyze likelihood ratio distributions.

It implements the Nelson-Siegel and the Nelson-Siegel-Svensson term structures.

An efficient algorithm to fit and tune kernel quantile regression models based on the majorization-minimization (MM) method. It can also fit multiple quantile curves simultaneously without crossing.

Transform output files of some tools to the microtable object of microtable class in microeco package. The microtable class is the basic class in microeco package and is necessary for the downstream microbial community data analysis.

This package provides a set of helper functions for constructing file paths relative to the root of various types of projects, such as R packages, Git repositories, and more. File paths are specified with function arguments, or `$` to navigate into folders to specific files supported by auto-completion.

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.

This package implements the Fixed Effect Jackknife Instrumental Variables ('FEJIV') estimator of Chao, Swanson, and Woutersen (2023) <doi:10.1016/j.jeconom.2022.12.011>, allowing consistent IV estimation with many (possibly weak) instruments, cluster fixed effects, heteroskedastic errors, and many exogenous covariates. The estimator is recommended by SÅ oczyÅ ski (2024) <doi:10.48550/arXiv.2011.06695> as an alternative to two-stage least squares when estimating the interacted specification of Angrist and Imbens (1995) <doi:10.1080/01621459.1995.10476535>.

Simulates and fits semiparametric shared frailty models under a wide range of frailty distributions using a consistent and asymptotically-normal estimator. Currently supports: gamma, power variance function, log-normal, and inverse Gaussian frailty models.

Efficient computation of the Liu regression coefficient paths, Liu-related statistics and information criteria for a grid of the regularization parameter. The computations are based on the C++ library Armadillo through the R package Rcpp'.

This package contains a set of functions that can be used to apply formats to data frames or vectors. The package aims to provide functionality similar to that of SAS® formats. Formats are assigned to the format attribute on data frame columns. Then when the fdata() function is called, a new data frame is created with the column data formatted as specified. The package also contains a value() function to create a user-defined format, similar to a SAS® user-defined format.

Likelihood-free inference method for stochastic models. Uses a deterministic optimizer on simple simulations of the model that are performed with a prior drawn randomness by applying the inverse transform method. Is designed to work on its own and also by using the Julia package Jflimo available on the git page of the project: <https://metabarcoding.org/flimo>.

Given a multivariate dataset and some knowledge about the dependencies between its features, it is customary to fit a statistical model to the features to infer parameters of interest. Such a procedure implicitly assumes that the sample is exchangeable. This package provides a flexible non-parametric test of this exchangeability assumption, allowing the user to specify the feature dependencies by hand as long as features can be grouped into disjoint independent sets. This package also allows users to test a dual hypothesis, which is, given that the sample is exchangeable, does a proposed grouping of the features into disjoint sets also produce statistically independent sets of features? See Aw, Spence and Song (2023) for the accompanying paper.

This package provides a series of utility functions to help with reshaping hierarchy of data tree, and reform the structure of data tree.

Fuzzy inference systems are based on fuzzy rules, which have a good capability for managing progressive phenomenons. This package is a basic implementation of the main functions to use a Fuzzy Inference System (FIS) provided by the open source software FisPro <https://www.fispro.org>. FisPro allows to create fuzzy inference systems and to use them for reasoning purposes, especially for simulating a physical or biological system.