Generate interactive volcano plots for exploring gene expression data. Built with ggplot2', the plots are rendered interactive using ggiraph', enabling users to hover over points to display detailed information or click to trigger custom actions.

The Integro-Difference Equation model is a linear, dynamical model used to model phenomena that evolve in space and in time; see, for example, Cressie and Wikle (2011, ISBN:978-0-471-69274-4) or Dewar et al. (2009) <doi:10.1109/TSP.2008.2005091>. At the heart of the model is the kernel, which dictates how the process evolves from one time point to the next. Both process and parameter reduction are used to facilitate computation, and spatially-varying kernels are allowed. Data used to estimate the parameters are assumed to be readings of the process corrupted by Gaussian measurement error. Parameters are fitted by maximum likelihood, and estimation is carried out using an evolution algorithm.

This package performs valid statistical inference on predicted data (IPD) using recent methods, where for a subset of the data, the outcomes have been predicted by an algorithm. Provides a wrapper function with specified defaults for the type of model and method to be used for estimation and inference. Further provides methods for tidying and summarizing results. Salerno et al., (2024) <doi:10.48550/arXiv.2410.09665>.

Automates the identification and comparative evaluation of item-removal strategies in exploratory factor analysis, producing transparent summaries (explained variance, loading ranges, reliability) to support comfortable, reproducible decisions. The criteria are based on best practices and established heuristics (e.g., Costello & Osborne (2005) <doi:10.7275/jyj1-4868>, Howard (2016) <doi:10.1080/10447318.2015.1087664>).

This package provides functions to support the computations carried out in `An Introduction to Statistical Modeling of Extreme Values by Stuart Coles. The functions may be divided into the following groups; maxima/minima, order statistics, peaks over thresholds and point processes.

Chi-square tests are computed with corrections.

Calculates insulin secretion rates from C-peptide values based on the methods described in Van Cauter et al. (1992) <doi:10.2337/diab.41.3.368>. Includes functions to calculate estimated insulin secretion rates using linear or cubic spline interpolation of c-peptide values (see Eaton et al., 1980 <doi:10.1210/jcem-51-3-520> and Polonsky et al., 1986 <doi:10.1172/JCI112308>) and to calculate estimates of input coefficients (volume of distribution, short half life, long half life, and fraction attributed to short half life) as described by Van Cauter. Although the generated coefficients are specific to insulin secretion, the two-compartment secretion model used here is useful for certain applications beyond insulin.

An implementation of the Unsupervised Smooth Contour Detection algorithm for digital images as described in the paper: "Unsupervised Smooth Contour Detection" by Rafael Grompone von Gioi, and Gregory Randall (2016). The algorithm is explained at <doi:10.5201/ipol.2016.175>.

This package provides methods for quantifying temporal and spatial causality through information flow, and decomposing it into unique, redundant, and synergistic components, following the framework described in Martinez-Sanchez et al. (2024) <doi:10.1038/s41467-024-53373-4>.

This package provides S4 classes for Internet Protocol (IP) versions 4 and 6 addresses and efficient methods for IP addresses comparison, arithmetic, bit manipulation and lookup. Both IPv4 and IPv6 arbitrary ranges are also supported as well as internationalized ('IDN') domain lookup with and whois query.

An interval-valued extension of ordinary and simple kriging. Optimization of the function is based on a generalized interval distance. This creates a non-differentiable cost function that requires a differentiable approximation to the absolute value function. This differentiable approximation is optimized using a Newton-Raphson algorithm with a penalty function to impose the constraints. Analyses in the package are driven by the intsp and intgrd classes, which are interval-valued extensions of SpatialPointsDataFrame and SpatialPixelsDataFrame respectively. The package includes several wrappers to functions in the gstat and sp packages.

Electricity is not made equal and it vary in its carbon footprint (or carbon intensity) depending on its source. This package enables to access and query data provided by the Carbon Intensity API (<https://carbonintensity.org.uk/>). National Gridâ s Carbon Intensity API provides an indicative trend of regional carbon intensity of the electricity system in Great Britain.

Carries out integrative clustering analysis using multiple types of genomic dataset using integrative Non-negative Matrix factorization.

Computes individual causes of death and population cause-specific mortality fractions using the InSilicoVA algorithm from McCormick et al. (2016) <DOI:10.1080/01621459.2016.1152191>. It uses data derived from verbal autopsy (VA) interviews, in a format similar to the input of the widely used InterVA method. This package provides general model fitting and customization for InSilicoVA algorithm and basic graphical visualization of the output.

This package performs Goodness of Fit for regression models using Integrated Regression method. Works for several different fitting techniques.

The itdr() routine allows for the estimation of sufficient dimension reduction subspaces in univariate regression such as the central mean subspace or central subspace in regression. This is achieved using Fourier transformation methods proposed by Zhu and Zeng (2006) <doi:10.1198/016214506000000140>, convolution transformation methods proposed by Zeng and Zhu (2010) <doi:10.1016/j.jmva.2009.08.004>, and iterative Hessian transformation methods proposed by Cook and Li (2002) <doi:10.1214/aos/1021379861>. Additionally, mitdr() function provides optimal estimators for sufficient dimension reduction subspaces in multivariate regression by optimizing a discrepancy function using a Fourier transform approach proposed by Weng and Yin (2022) <doi:10.5705/ss.202020.0312>, and selects the sufficient variables using Fourier transform sparse inverse regression estimators proposed by Weng (2022) <doi:10.1016/j.csda.2021.107380>.

This package performs hypothesis testing using the interval estimates (e.g., confidence intervals). The non-overlapping interval estimates indicates the statistical significance. References to these procedures can be found at Noguchi and Marmolejo-Ramos (2016) <doi:10.1080/00031305.2016.1200487>, Bonett and Seier (2003) <doi:10.1198/0003130032323>, and Lemm (2006) <doi:10.1300/J082v51n02_05>.

Calculation of informative simultaneous confidence intervals for graphical described multiple test procedures and given information weights. Bretz et al. (2009) <doi:10.1002/sim.3495> and Brannath et al. (2024) <doi:10.48550/arXiv.2402.13719>. Furthermore, exploration of the behavior of the informative bounds in dependence of the information weights. Comparisons with compatible bounds are possible. Strassburger and Bretz (2008) <doi:10.1002/sim.3338>.

Programmatic connection to the OpenAltimetry API <https://openaltimetry.earthdatacloud.nasa.gov/data/openapi/swagger-ui/index.html/> to download and process ATL03 (Global Geolocated Photon Data), ATL06 (Land Ice Height), ATL07 (Sea Ice Height), ATL08 (Land and Vegetation Height), ATL10 (Sea Ice Freeboard), ATL12 (Ocean Surface Height) and ATL13 (Inland Water Surface Height) ICESat-2 Altimeter Data. The user has the option to download the data by selecting a bounding box from a 1- or 5-degree grid globally utilizing a shiny application. The ICESat-2 mission collects altimetry data of the Earth's surface. The sole instrument on ICESat-2 is the Advanced Topographic Laser Altimeter System (ATLAS) instrument that measures ice sheet elevation change and sea ice thickness, while also generating an estimate of global vegetation biomass. ICESat-2 continues the important observations of ice-sheet elevation change, sea-ice freeboard, and vegetation canopy height begun by ICESat in 2003.

This package provides a set of utilities for manipulating index numbers series including chain-linking, re-referencing, and computing growth rates.

The 14th generation International Geomagnetic Reference Field (IGRF). A standard spherical harmonic representation of the Earth's main field.

Cluster sampling is a valuable approach when constructing a comprehensive list of individual units is challenging. It provides operational and cost advantages. This package is designed to test the efficiency of cluster sampling in terms cluster variance and design effect in context to crop surveys. This package has been developed using the algorithm of Iqbal et al. (2018) <doi:10.19080/BBOAJ.2018.05.555673>.

This package provides methods for estimating causal effects in the presence of interference described in B. Saul and M. Hugdens (2017) <doi:10.18637/jss.v082.i02>. Currently it implements the inverse-probability weighted (IPW) estimators proposed by E.J. Tchetgen Tchetgen and T.J. Vanderweele (2012) <doi:10.1177/0962280210386779>.

R interface to access the web services of the ICES Stock Database <https://sd.ices.dk>.