Fit mixed-effects location scale models with spike-and-slab priors on the location random effects to identify units with unusual residual variances. The method is described in detail in Carmo, Williams and Rast (2025) <https://osf.io/sh6ne>.

Containerizes cytometry data and allows for S4 class structure to extend slots related to cell morphology, spatial coordinates, phenotype network information, and unique cellular labeling.

ISA is a metadata framework to manage an increasingly diverse set of life science, environmental and biomedical experiments. In isatabr methods for reading, modifying and writing of files in the ISA-Tab format are implemented. It also contains methods for processing assay data.

IsoSpec is a fine structure calculator used for obtaining the most probable masses of a chemical compound given the frequencies of the composing isotopes and their masses. It finds the smallest set of isotopologues with a given probability. The probability is assumed to be that of the product of multinomial distributions, each corresponding to one particular element and parametrized by the frequencies of finding these elements in nature. These numbers are supplied by IUPAC - the International Union of Pure and Applied Chemistry. See: Lacki, Valkenborg, Startek (2020) <DOI:10.1021/acs.analchem.0c00959> and Lacki, Startek, Valkenborg, Gambin (2017) <DOI:10.1021/acs.analchem.6b01459> for the description of the algorithms used.

This package provides an estimator for generalized linear models with incomplete data for discrete covariates. The estimation is based on the EM algorithm by the method of weights by Ibrahim (1990) <DOI:10.2307/2290013>.

This package provides a suite of functions for conducting and interpreting analysis of statistical interaction in regression models that was formerly part of the jtools package. Functionality includes visualization of two- and three-way interactions among continuous and/or categorical variables as well as calculation of "simple slopes" and Johnson-Neyman intervals (see e.g., Bauer & Curran, 2005 <doi:10.1207/s15327906mbr4003_5>). These capabilities are implemented for generalized linear models in addition to the standard linear regression context.

Estimates the probability of informed trading (PIN) initially introduced by Easley et. al. (1996) <doi:10.1111/j.1540-6261.1996.tb04074.x> . Contribution of the package is that it uses likelihood factorizations of Easley et. al. (2010) <doi:10.1017/S0022109010000074> (EHO factorization) and Lin and Ke (2011) <doi:10.1016/j.finmar.2011.03.001> (LK factorization). Moreover, the package uses different estimation algorithms. Specifically, the grid-search algorithm proposed by Yan and Zhang (2012) <doi:10.1016/j.jbankfin.2011.08.003> , hierarchical agglomerative clustering approach proposed by Gan et. al. (2015) <doi:10.1080/14697688.2015.1023336> and later extended by Ersan and Alici (2016) <doi:10.1016/j.intfin.2016.04.001> .

Coefficients of Interrater Reliability and Agreement for quantitative, ordinal and nominal data: ICC, Finn-Coefficient, Robinson's A, Kendall's W, Cohen's Kappa, ...

This package provides a voxel is a representation of a value on a regular, three-dimensional grid; it is the 3D equivalent of a 2D pixel. Voxel data can be visualised with this package using fixed viewpoint isometric cubes for each data point. This package also provides sample voxel data and tools for transforming the data.

Use R to make requests to the US Census Bureau's International Data Base API. Results are returned as R data frames. For more information about the IDB API, visit <https://www.census.gov/data/developers/data-sets/international-database.html>.

Query for enriched data such as country, region, city, latitude & longitude, ZIP code, time zone, Autonomous System, Internet Service Provider, domain, net speed, International direct dialing (IDD) code, area code, weather station data, mobile data, elevation, usage type, address type, advertisement category, fraud score, and proxy data with an IP address. You can also query a list of hosted domain names for the IP address too. This package uses the IP2Location.io API to query this data. To get started with a free API key, sign up here <https://www.ip2location.io/sign-up?ref=1>.

This package provides functions for classification and ranking of candidate features, reconstruction of networks from adjacency matrices and data frames, topological analysis, and calculation of centrality measures. The package includes the SIRIR model, which combines leave-one-out cross-validation with the conventional SIR model to rank vertex influence in an unsupervised manner. Additional functions support assessment of dependence and correlation between network centrality measures, as well as estimation of conditional probabilities of deviation from their corresponding means in opposite directions.

Fitting and validation of machine learning algorithms for volume prediction of trees, currently for conifer trees based on diameter at breast height and height as explanatory variables.

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.

Instrumental variable (IV) estimators for homogeneous and heterogeneous treatment effects with efficient machine learning instruments. The estimators are based on double/debiased machine learning allowing for nonlinear and potentially high-dimensional control variables. Details can be found in Scheidegger, Guo and Bühlmann (2025) "Inference for heterogeneous treatment effects with efficient instruments and machine learning" <doi:10.48550/arXiv.2503.03530>.

Expands iNEXT to include the estimation of sample completeness and evenness. The package provides simple functions to perform the following four-step biodiversity analysis: STEP 1: Assessment of sample completeness profiles. STEP 2a: Analysis of size-based rarefaction and extrapolation sampling curves to determine whether the asymptotic diversity can be accurately estimated. STEP 2b: Comparison of the observed and the estimated asymptotic diversity profiles. STEP 3: Analysis of non-asymptotic coverage-based rarefaction and extrapolation sampling curves. STEP 4: Assessment of evenness profiles. The analyses in STEPs 2a, 2b and STEP 3 are mainly based on the previous iNEXT package. Refer to the iNEXT package for details. This package is mainly focusing on the computation for STEPs 1 and 4. See Chao et al. (2020) <doi:10.1111/1440-1703.12102> for statistical background.

Facilitates access to the International Union for Conservation of Nature (IUCN) Red List of Threatened Species, a comprehensive global inventory of species at risk of extinction. This package streamlines the process of determining conservation status by matching species names with Red List data, providing tools to easily query and retrieve conservation statuses. Designed to support biodiversity research and conservation planning, this package relies on data from the iucnrdata package, available on GitHub <https://github.com/PaulESantos/iucnrdata>. To install the data package, use pak::pak('PaulESantos/iucnrdata').

Calculates the RMS intrinsic and parameter-effects curvatures of a nonlinear regression model. The curvatures are global measures of assessing whether a model/data set combination is close-to-linear or not. See Bates and Watts (1980) <doi:10.1002/9780470316757> and Ratkowsky and Reddy (2017) <doi:10.1093/aesa/saw098> for details.

Interactive plots for R.

This package provides tools for multivariate nonparametrics, as location tests based on marginal ranks, spatial median and spatial signs computation, Hotelling's T-test, estimates of shape are implemented.

Time parceling method and Bayesian variability modeling methods for modeling within individual variability indicators as predictors.For more details, see <https://github.com/xliu12/IIVpredicitor>.

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 tools to download, process, and analyze data from the International Monetary Fund's World Economic Outlook (WEO) database <https://www.imf.org/en/Publications/SPROLLs/world-economic-outlook-databases>. Functions support downloading complete WEO releases, accessing specific economic indicators for selected countries, and listing available data.

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>.