Developed by CDC/ATSDR (Centers for Disease Control and Prevention/ Agency for Toxic Substances and Disease Registry), Social Vulnerability Index (SVI) serves as a tool to assess the resilience of communities by taking into account socioeconomic and demographic factors. Provided with year(s), region(s) and a geographic level of interest, findSVI retrieves required variables from US census data and calculates SVI for communities in the specified area based on CDC/ATSDR SVI documentation. Reference for the calculation methods: Flanagan BE, Gregory EW, Hallisey EJ, Heitgerd JL, Lewis B (2011) <doi:10.2202/1547-7355.1792>.

Analyze and model heteroskedastic behavior in financial time series.

Approximate false positive rate control in selection frequency for random forest using the methods described by Ender Konukoglu and Melanie Ganz (2014) <arXiv:1410.2838>. Methods for calculating the selection frequency threshold at false positive rates and selection frequency false positive rate feature selection.

An implementation of the two-sample multivariate Kolmogorov-Smirnov test described by Fasano and Franceschini (1987) <doi:10.1093/mnras/225.1.155>. This test evaluates the null hypothesis that two i.i.d. random samples were drawn from the same underlying probability distribution. The data can be of any dimension, and can be of any type (continuous, discrete, or mixed).

This package implements the Fourier Bootstrap Autoregressive Distributed Lag (FBARDL) bounds testing approach for cointegration analysis. Combines the Pesaran, Shin & Smith (2001) <doi:10.1002/jae.616> ARDL bounds testing framework with Fourier terms to capture structural breaks following Yilanci, Bozoklu & Gorus (2020) <doi:10.1080/00036846.2019.1686454>, and bootstrap critical values based on McNown, Sam & Goh (2018) <doi:10.1080/00036846.2017.1366643> and Bertelli, Vacca & Zoia (2022) <doi:10.1016/j.econmod.2022.105987>. Features include automatic lag selection via AIC/BIC, optimal Fourier frequency selection by minimum SSR, long-run and short-run coefficient estimation, diagnostic tests, and dynamic multiplier analysis.

Supports fMRI (functional magnetic resonance imaging) analysis tasks including reading in CIFTI', GIFTI and NIFTI data, temporal filtering, nuisance regression, and aCompCor (anatomical Components Correction) (Muschelli et al. (2014) <doi:10.1016/j.neuroimage.2014.03.028>).

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.

An R client for the Federal Reserve Economic Data ('FRED') API <https://research.stlouisfed.org/docs/api/>. Functions to retrieve economic time series and other data from FRED'.

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>).

Nonlinear forecast reconciliation with machine learning in cross-sectional (Spiliotis et al. 2021 <doi:10.1016/j.asoc.2021.107756>), temporal, and cross-temporal (Rombouts et al. 2024 <doi:10.1016/j.ijforecast.2024.05.008>) frameworks.

Computes the power and sample size (PASS) required to test for the difference in the mean function between two groups under a repeatedly measured longitudinal or sparse functional design. See the manuscript by Koner and Luo (2023) <https://salilkoner.github.io/assets/PASS_manuscript.pdf> for details of the PASS formula and computational details. The details of the testing procedure for univariate and multivariate response are presented in Wang (2021) <doi:10.1214/21-EJS1802> and Koner and Luo (2023) <arXiv:2302.05612> respectively.

An interactive shiny'-based tool for exploration and quality assurance and quality control (QA/QC) of eddy covariance flux tower data processing. It generates data-point removal code via user-directed selection from a scatterplot, and can export a cleaned .csv with removed points set to NA plus an R script for reproducibility. Reference: Key (2025) <DOI:10.5281/zenodo.15597159>.

This package provides methods to "add" two R tables; also an alternative interpretation of named vectors as generalized R tables, so that c(a=1,b=2,c=3) + c(b=3,a=-1) will return c(b=5,c=3). Uses disordR discipline (Hankin, 2022, <doi:10.48550/arXiv.2210.03856>). Extraction and replacement methods are provided. The underlying mathematical structure is the Free Abelian group, hence the name. To cite in publications please use Hankin (2023) <doi:10.48550/arXiv.2307.13184>.

This is a method for Allele-specific DNA Copy Number Profiling using Next-Generation Sequencing. Given the allele-specific coverage at the variant loci, this program segments the genome into regions of homogeneous allele-specific copy number. It requires, as input, the read counts for each variant allele in a pair of case and control samples. For detection of somatic mutations, the case and control samples can be the tumor and normal sample from the same individual.

Work with configs with a source precedence. Either create own R6 instance or work with convenient functions at a package level.

Calculation of AHP (Analytic Hierarchy Process - <http://en.wikipedia.org/wiki/Analytic_hierarchy_process>) with classic and fuzzy weights based on Saaty's pairwise comparison method for determination of weights.

Calculates marginal effects based on logistic model objects such as glm or speedglm at the average (default) or at given values using finite differences. It also returns confidence intervals for said marginal effects and the p-values, which can easily be used as input in stargazer. The function only returns the essentials and is therefore much faster but not as detailed as other functions available to calculate marginal effects. As a result, it is highly suitable for large datasets for which other packages may require too much time or calculating power.

As in music, a fugue statistic repeats a theme in small variations. Here, the psi-function that defines an m-statistic is slightly altered to maintain the same design sensitivity in matched sets of different sizes. The main functions in the package are sen() and senCI(). For sensitivity analyses for m-statistics, see Rosenbaum (2007) Biometrics 63 456-464 <doi:10.1111/j.1541-0420.2006.00717.x>.

This package provides functions for plotting probability density functions, distribution functions, survival functions, hazard functions and computing distribution moments. The implementation is inspired by Delignette-Muller and Dutang (2015) <doi:10.18637/jss.v064.i04>.

This package provides tools for quickly processing and analyzing field observation data and air quality data. This tools contain functions that facilitate analysis in atmospheric chemistry (especially in ozone pollution). Some functions of time series are also applicable to other fields. For detail please view homepage<https://github.com/tianshu129/foqat>. Scientific Reference: 1. The Hydroxyl Radical (OH) Reactivity: Roger Atkinson and Janet Arey (2003) <doi:10.1021/cr0206420>. 2. Ozone Formation Potential (OFP): <http://ww2.arb.ca.gov/sites/default/files/barcu/regact/2009/mir2009/mir10.pdf>, Zhang et al.(2021) <doi:10.5194/acp-21-11053-2021>. 3. Aerosol Formation Potential (AFP): Wenjing Wu et al. (2016) <doi:10.1016/j.jes.2016.03.025>. 4. TUV model: <https://www2.acom.ucar.edu/modeling/tropospheric-ultraviolet-and-visible-tuv-radiation-model>.

Computes functional rarity indices as proposed by Violle et al. (2017) <doi:10.1016/j.tree.2017.02.002>. Various indices can be computed using both regional and local information. Functional Rarity combines both the functional aspect of rarity as well as the extent aspect of rarity. funrar is presented in Grenié et al. (2017) <doi:10.1111/ddi.12629>.

This package contains Rcpp and RcppEigen implementations of matrix operations useful for Gaussian process models, such as the inversion of a symmetric Toeplitz matrix, sampling from multivariate normal distributions, evaluation of the log-density of a multivariate normal vector, and Bayesian inference for latent variable Gaussian process models with elliptical slice sampling (Murray, Adams, and MacKay 2010).

Fuel economy data from the EPA, 1985-2015, conveniently packaged for consumption by R users.

This package provides efficient methods to compute local and genome wide genetic distances (corresponding to the so called Hudson Fst parameters) through moment method, perform chromosome segmentation into homogeneous Fst genomic regions, and selection sweep detection for multi-population comparison. When multiple profile segmentation is required, the procedure can be parallelized using the future package.