This package provides functions and classes to compute, handle and visualise incidence from dated events for a defined time interval. Dates can be provided in various standard formats. The class incidence is used to store computed incidence and can be easily manipulated, subsetted, and plotted. In addition, log-linear models can be fitted to incidence objects using fit'. This package is part of the RECON (<https://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.

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.

IRT-M is a semi-supervised approach based on Bayesian Item Response Theory that produces theoretically identified underlying dimensions from input data and a constraints matrix. The methodology is fully described in Morucci et al. (2024), "Measurement That Matches Theory: Theory-Driven Identification in Item Response Theory Models"'. Details are available at <https://www.cambridge.org/core/journals/american-political-science-review/article/measurement-that-matches-theory-theorydriven-identification-in-item-response-theory-models/395DA1DFE3DCD7B866DC053D7554A30B>.

This package provides the dataset and an implementation of the method illustrated in Friel, N., Rastelli, R., Wyse, J. and Raftery, A.E. (2016) <DOI:10.1073/pnas.1606295113>.

This package provides a R package to find the IP addresses which are used as VPN anonymizer, open proxies, web proxies and Tor exits. The package lookup the proxy IP address from IP2Proxy BIN Data file. You may visit <https://lite.ip2location.com> for free database download.

An implementation of the Invariance Partial Pruning (IVPP) approach described in Du, X., Johnson, S. U., Epskamp, S. (2025) The Invariance Partial Pruning Approach to The Network Comparison in Longitudinal Data. IVPP is a two-step method that first test for global network structural difference with invariance test and then inspect specific edge difference with partial pruning. The package also allows you to compute centrality measures and use radar chart to plot. Analysis of bridge centralities by community pairs is also possible (e.g., the bridge strength from depression to anxiety, and from depression to panic disorder).

This package provides functions and data sets to accompany the book Integrated Population Models: Theory and Ecological Applications with R and JAGS by Michael Schaub and Marc Kéry (ISBN: 9780128205648).

Ke, B. S., Chiang, A. J., & Chang, Y. C. I. (2018) <doi:10.1080/10543406.2017.1377728> provide two theoretical methods (influence function and local influence) based on the area under the receiver operating characteristic curve (AUC) to quantify the numerical impact of each observation to the overall AUC. Alternative graphical tools, cumulative lift charts, are proposed to reveal the existences and approximate locations of those influential observations through data visualization.

Several functions to calculate two important indexes (IBR (Integrated Biomarker Response) and IBRv2 (Integrated Biological Response version 2)), it also calculates the standardized values for enzyme activity for each index, and it has a graphing function to perform radarplots that make great data visualization for this type of data. Beliaeff, B., & Burgeot, T. (2002). <https://pubmed.ncbi.nlm.nih.gov/12069320/>. Sanchez, W., Burgeot, T., & Porcher, J.-M. (2013).<doi:10.1007/s11356-012-1359-1>. Devin, S., Burgeot, T., Giambérini, L., Minguez, L., & Pain-Devin, S. (2014). <doi:10.1007/s11356-013-2169-9>. Minato N. (2022). <https://minato.sip21c.org/msb/>.

Compute onestep and multistep time series forecasts for machine learning models.

Integrated toolbox supporting common file formats used for intracranial Electroencephalography (iEEG) and deep-brain stimulation (DBS) study.

Helper functions and example data sets to facilitate the estimation of IRTree models from data with different shape and using different software.

This R package implements methods for estimation and inference under Incomplete Block Designs and Balanced Incomplete Block Designs within a design-based finite-population framework. Based on Koo and Pashley (2026) <doi:10.1093/biomet/asag013>, it includes block-level estimators and extends to unit-level effects using Horvitz-Thompson and Hájek estimators. The package also provides asymptotic confidence intervals to support valid statistical inference.

Multiple Imputation for Informative Censoring. This package implements two methods. Gamma Imputation described in <DOI:10.1002/sim.6274> and Risk Score Imputation described in <DOI:10.1002/sim.3480>.

Compute distributional quantities for an Integrated Gamma (IG) or Integrated Gamma Limit (IGL) copula, such as a cdf and density. Compute corresponding conditional quantities such as the cdf and quantiles. Generate data from an IG or IGL copula. See the vignette for formulas, or for a derivation, see Coia, V (2017) "Forecasting of Nonlinear Extreme Quantiles Using Copula Models." PhD Dissertation, The University of British Columbia.

Select set of parametric and non-parametric statistical tests. inferr builds upon the solid set of statistical tests provided in stats package by including additional data types as inputs, expanding and restructuring the test results. The tests included are t tests, variance tests, proportion tests, chi square tests, Levene's test, McNemar Test, Cochran's Q test and Runs test.

This package provides API access to the <http://imdbapi.net> which maintains metadata about movies, games and television shows through a public API.

This package provides a component-based reactive UI framework for Shiny that binds reactive values directly to DOM attributes, enabling fine-grained updates without re-rendering or update*Input callbacks.

Uses data and researcher's beliefs on measurement error and instrumental variable (IV) endogeneity to generate the space of consistent beliefs across measurement error, instrument endogeneity, and instrumental relevance for IV regressions. Package based on DiTraglia and Garcia-Jimeno (2020) <doi:10.1080/07350015.2020.1753528>.

Estimates weights to make a continuous-valued exposure statistically independent of a vector of pre-treatment covariates using the method proposed in Huling, Greifer, and Chen (2021) <arxiv:2107.07086>.

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>).

This package provides tools to download, style, and insert scalable vector graphics (SVG) icons into R output, such as R Markdown and Quarto documents, Shiny apps, gt tables, and ggplot2 visualisations via the ggicons package. Bundles popular icon libraries, such as Font Awesome (<https://fontawesome.com>), Academicons (<https://jpswalsh.github.io/academicons/>), and Material Design Icons (<https://fonts.google.com/icons>), which are downloaded on demand to keep the package small, or build a custom icon set from any folder of local SVG files. Icon appearance, such as size, colour, and position, can be customised.

We consider the non-parametric maximum likelihood estimation of the underlying distribution function, assuming log-concavity, based on mixed-case interval-censored data. The algorithm implemented is base on Chi Wing Chu, Hok Kan Ling and Chaoyu Yuan (2024, <doi:10.48550/arXiv.2411.19878>).

Neural network has potential in forestry modelling. This package is designed to create and assess Artificial Intelligence based Neural Networks with varying architectures for prediction of volume of forest trees using two input features: height and diameter at breast height, as they are the key factors in predicting volume, therefore development and validation of efficient volume prediction neural network model is necessary. This package has been developed using the algorithm of Tabassum et al. (2022) <doi:10.18805/ag.D-5555>.