This package implements the Interval-Censored Sequence Kernel Association (ICSKAT) test for testing the association between interval-censored time-to-event outcomes and groups of single nucleotide polymorphisms (SNPs). Interval-censored time-to-event data occur when the event time is not known exactly but can be deduced to fall within a given interval. For example, some medical conditions like bone mineral density deficiency are generally only diagnosed at clinical visits. If a patient goes for clinical checkups yearly and is diagnosed at, say, age 30, then the onset of the deficiency is only known to fall between the date of their age 29 checkup and the date of the age 30 checkup. Interval-censored data include right- and left-censored data as special cases. This package also implements the interval-censored Burden test and the ICSKATO test, which is the optimal combination of the ICSKAT and Burden tests. Please see the vignette for a quickstart guide. The paper describing these methods is " Inference for Set-Based Effects in Genetic Association Studies with Interval-Censored Outcomes" by Sun R, Zhu L, Li Y, Yasui Y, & Robison L (Biometrics 2023, <doi:10.1111/biom.13636>).

Generates three inter-related genomic datasets: methylation, gene expression and protein expression having user specified cluster patterns. The simulation utilizes the realistic inter- and intra- relationships from real DNA methylation, mRNA expression and protein expression data from the TCGA ovarian cancer study, Chalise (2016) <doi:10.1016/j.cmpb.2016.02.011>.

An R interface to the InfluxDB time series database <https://www.influxdata.com>. This package allows you to fetch and write time series data from/to an InfluxDB server. Additionally, handy wrappers for the Influx Query Language (IQL) to manage and explore a remote database are provided.

This package implements a suite of sensitivity analysis tools for instrumental variable estimates as described in Cinelli and Hazlett (2025) <doi:10.1093/biomet/asaf004>.

This package provides functionality to perform a likelihood-free method for estimating the parameters of complex models that results in a simulated sample from the posterior distribution of model parameters given targets. The method begins with a accept/reject approximate bayes computation (ABC) step applied to a sample of points from the prior distribution of model parameters. Accepted points result in model predictions that are within the initially specified tolerance intervals around the target points. The sample is iteratively updated by drawing additional points from a mixture of multivariate normal distributions, accepting points within tolerance intervals. As the algorithm proceeds, the acceptance intervals are narrowed. The algorithm returns a set of points and sampling weights that account for the adaptive sampling scheme. For more details see Rutter, Ozik, DeYoreo, and Collier (2018) <arXiv:1804.02090>.

Given a response y and a one- or two-dimensional predictor, the isotonic regression estimator is calculated with the usual orderings.

This package provides methods to evaluate predictive performance of models that estimate risks under hypothetical intervention scenarios (interventional/causal/counterfactual predictions) with observational data subject to treatment-outcome confounding. Inverse probability of treatment weighting (IPTW) is used to construct a pseudopopulation in which all individuals receive a specified intervention, enabling assessment of agreement between predicted risks under the intervention and observed outcomes in the pseudo-population corresponding to that intervention. Package supports binary and time-to-event outcomes under binary interventions made at a single time point. Performance measures supported are AUC (Area Under the receiving operating characteristic Curve), Brier score, observed-expected ratio, and calibration plots. Methods implemented in this package are based on work by Keogh and Van Geloven (2024) <DOI:10.1097/EDE.0000000000001713>.

This package implements the "Node + Edge Betweenness" community detection algorithm for network analysis using igraph objects. This algorithm combines node degree and betweenness centrality measures to identify communities within networks, with a gradient evident in social partitioning. The package provides functions for implementation of the algorithm, visualization, and analysis of the resulting community structure along with the original dataset used in the publication. Methods are based on results from Smith, Pittman and Xu (2026) <doi:10.1002/cjs.70060>.

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.

When added to an existing shiny app, users may subset any developer-chosen R data.frame on the fly. That is, users are empowered to slice & dice data by applying multiple (order specific) filters using the AND (&) operator between each, and getting real-time updates on the number of rows effected/available along the way. Thus, any downstream processes that leverage this data source (like tables, plots, or statistical procedures) will re-render after new filters are applied. The shiny moduleâ s user interface has a minimalist aesthetic so that the focus can be on the data & other visuals. In addition to returning a reactive (filtered) data.frame, IDEAFilter as also returns dplyr filter statements used to actually slice the data.

Call wrappers for Istanbul Metropolitan Municipality's Open Data Portal (Turkish: İstanbul BüyükŠehir Belediyesi Açık Veri Portalı) at <https://data.ibb.gov.tr/en/>.

This package provides actuarial tools and building blocks for analysing, modelling, refining, and validating insurance rating models. Designed to support common GLM-based pricing tasks and the translation of statistical model output into practical tariff structures. The package supports the construction of insurance tariff classes using a data-driven approach, based on the methodology of Antonio and Valdez (2012) <doi:10.1007/s10182-011-0152-7>.

This package provides a set of tools for processing and analyzing in vitro toxicokinetic measurements in a standardized and reproducible pipeline. The package was developed to perform frequentist and Bayesian estimation on a variety of in vitro toxicokinetic measurements including -- but not limited to -- chemical fraction unbound in the presence of plasma (f_up), intrinsic hepatic clearance (Clint, uL/min/million hepatocytes), and membrane permeability for oral absorption (Caco2). The methods provided by the package were described in Wambaugh et al. (2019) <doi:10.1093/toxsci/kfz205>.

This package provides functions to calculate indices used to score immunoglobulin A (IgA) binding of bacteria in IgA sequencing (IgA-Seq) experiments. This includes the original Kau and Palm indices and more recent methods as described in Jackson et al. (2020) <doi:10.1101/2020.08.19.257501>. Additionally the package contains a function to simulate IgA-Seq data and an example experimental data set for method testing.

This package provides a set of tools for evaluating several measures of case influence for structural equation models.

An R package for inferring cell-type specific gene regulatory network from single-cell RNA-seq data.

This package provides a comprehensive suite of tools for managing, processing, and analyzing data from the IFCB. I R FlowCytobot ('iRfcb') supports quality control, geospatial analysis, and preparation of IFCB data for publication in databases like <https://www.gbif.org>, <https://www.obis.org>, <https://emodnet.ec.europa.eu/en>, <https://shark.smhi.se/en/>, and <https://www.ecotaxa.org>. The package integrates with the MATLAB ifcb-analysis tool, which is described in Sosik and Olson (2007) <doi:10.4319/lom.2007.5.204>, and provides features for working with raw, manually classified, and machine learningâ classified image datasets. Key functionalities include image extraction, particle size distribution analysis, taxonomic data handling, and biomass concentration calculations, essential for plankton research.

This package provides functions to conduct a model-agnostic asymptotic hypothesis test for the identification of interaction effects in black-box machine learning models. The null hypothesis assumes that a given set of covariates does not contribute to interaction effects in the prediction model. The test statistic is based on the difference of variances of partial dependence functions (Friedman (2008) <doi:10.1214/07-AOAS148> and Welchowski (2022) <doi:10.1007/s13253-021-00479-7>) with respect to the original black-box predictions and the predictions under the null hypothesis. The hypothesis test can be applied to any black-box prediction model, and the null hypothesis of the test can be flexibly specified according to the research question of interest. Furthermore, the test is computationally fast to apply as the null distribution does not require resampling or refitting black-box prediction models.

This package provides a professional R interface to download and analyze spatial development indicators from the BBSR INKAR (Indikatoren und Karten zur Raum- und Stadtentwicklung) database. Features a bilingual interactive wizard, fuzzy search, multi-indicator downloads with automatic tidy merging (long/wide), robust disk caching, and premium ggplot2 themes for regional mapping.

Fit a predictive model using iteratively reweighted boosting (IRBoost) to minimize robust loss functions within the CC-family (concave-convex). This constitutes an application of iteratively reweighted convex optimization (IRCO), where convex optimization is performed using the functional descent boosting algorithm. IRBoost assigns weights to facilitate outlier identification. Applications include robust generalized linear models and robust accelerated failure time models. Wang (2025) <doi:10.6339/24-JDS1138>.

Deriving isodar-based niche breadth indices from abundance data of two or more habitats, including several methods based on pairwise isodars, multidimensional isodars, and isodar-adjusted inequality.

Volume prediction is one of challenging task in forestry research. This package is a comprehensive toolset designed for the fitting and validation of various linear and nonlinear allometric equations (Linear, Log-Linear, Inverse, Quadratic, Cubic, Compound, Power and Exponential) used in the prediction of conifer tree volume. This package is particularly useful for forestry professionals, researchers, and resource managers engaged in assessing and estimating the volume of coniferous trees. This package has been developed using the algorithm of Sharma et al. (2017) <doi:10.13140/RG.2.2.33786.62407>.

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

Calculate false ring proportions from data frames of intra annual density fluctuations.