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.

Generates Personality Insights sunburst diagrams based on IBM Watson Personality Insights service output.

Note that imageData has been superseded by growthPheno'. The package growthPheno incorporates all the functionality of imageData and has functionality not available in imageData', but some imageData functions have been renamed. The imageData package is no longer maintained, but is retained for legacy purposes.

Used for analyzing immune responses and predicting vaccine efficacy using machine learning and advanced data processing techniques. Immunaut integrates both unsupervised and supervised learning methods, managing outliers and capturing immune response variability. It performs multiple rounds of predictive model testing to identify robust immunogenicity signatures that can predict vaccine responsiveness. The platform is designed to handle high-dimensional immune data, enabling researchers to uncover immune predictors and refine personalized vaccination strategies across diverse populations.

Manage a GitHub problem using R: wrangle issues, labels and milestones. It includes functions for storing, prioritizing (sorting), displaying, adding, deleting, and selecting (filtering) issues based on qualitative and quantitative information. Issues (labels and milestones) are written in lists and categorized into the S3 class to be easily manipulated as datasets in R.

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>).

The Row-column designs are widely recommended for experimental situations when there are two well-identified factors that are cross-classified representing known sources of variability. These designs are expected to result a gain in accuracy of estimating treatment comparisons in an experiment as they eliminate the effects of the row and column factors. However, these designs are not readily available when the number of treatments is more than the levels of row and column blocking factors. This package named iRoCoDe generates row-column designs with incomplete rows and columns, by amalgamating two incomplete block designs (D1 and D2). The selection of D1 and D2 (the input designs) can be done from the available incomplete block designs, viz., balanced incomplete block designs/ partially balanced incomplete block designs/ t-designs. (Mcsorley, J.P., Phillips, N.C., Wallis, W.D. and Yucas, J.L. (2005).<doi:10.1007/s10623-003-6149-9>).

Some basic functions to implement belief functions including: transformation between belief functions using the method introduced by Philippe Smets <arXiv:1304.1122>, evidence combination, evidence discounting, decision-making, and constructing masses. Currently, thirteen combination rules and six decision rules are supported. It can also be used to generate different types of random masses when working on belief combination and conflict management.

Perform common calculations based on published stable isotope theory, such as calculating carbon isotope discrimination and intrinsic water use efficiency from wood or leaf carbon isotope composition. See Mathias and Hudiburg (2022) in Global Change Biology <doi:10.1111/gcb.16407>.

This package implements the procedures suggested in Esarey and Sumner (2017) <http://justinesarey.com/interaction-overconfidence.pdf> for controlling the false discovery rate when constructing marginal effects plots for models with interaction terms.

This package provides tidy, harmonized access to demographic data from the Spanish National Statistics Institute (INE) fenomenos demograficos domain, including population, births, and deaths, retrieved live via the official ineapir API wrapper, with optional spatial integration at municipality and province level via mapSpain'. Mortality indicators follow the Human Mortality Database Methods Protocol, including the average age at death in infancy method of Andreev and Kingkade (2015) <doi:10.4054/DemRes.2015.33.13>.

This package provides direct access to the macroeconomic, financial, and regional database maintained by the Institute for Applied Economic Research (Ipea) via the Ipeadata API. For more information, see <https://www.ipeadata.gov.br/>.

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.

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>.

This minimalist package is designed to quickly score raw data outputted from an Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) <doi:10.1037/0022-3514.74.6.1464>. IAT scores are calculated as specified by Greenwald, Nosek, and Banaji (2003) <doi:10.1037/0022-3514.85.2.197>. Outputted values can be interpreted as effect sizes. The input function consists of three arguments. First, indicate the name of the dataset to be analyzed. This is the only required input. Second, indicate the number of trials in your entire IAT (the default is set to 219, which is typical for most IATs). Last, indicate whether congruent trials (e.g., flowers and pleasant) or incongruent trials (e.g., guns and pleasant) were presented first for this participant (the default is set to congruent). The script will tell you how long it took to run the code, the effect size for the participant, and whether that participant should be excluded based on the criteria outlined by Greenwald et al. (2003). Data files should consist of six columns organized in order as follows: Block (0-6), trial (0-19 for training blocks, 0-39 for test blocks), category (dependent on your IAT), the type of item within that category (dependent on your IAT), a dummy variable indicating whether the participant was correct or incorrect on that trial (0=correct, 1=incorrect), and the participantâ s reaction time (in milliseconds). Three sample datasets are included in this package (labeled IAT', TooFastIAT', and BriefIAT') to practice with.

This package provides a framework for matrix completion and regression on response matrices with missing values. The model estimates missing entries using any combination of intercepts, row and column covariates, and a low-rank matrix approximation. It applies Lasso penalties on the covariates and a nuclear norm penalty on the low-rank component. It also adjusts for correlation within the rows and columns of the target matrix using similarity matrices. The framework is described in Fouda, Labbe and Oualkacha (2026) <doi:10.48550/arXiv.2606.26325>.

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 provides a collection of wrapper functions for common variable and dataset manipulation workflows primarily used by iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions. Additionally, many of the functions return the tidyverse code used to obtain the result in an effort to bridge the gap between GUI and coding.

Estimate test-retest reliability for complex sampling strategies and extract variances using IntraClass Effect Decomposition. Developed by Brandmaier et al. (2018) "Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED)" <doi:10.7554/eLife.35718> Also includes functions to simulate data based on sampling strategy. Unofficial version release name: "Good work squirrels".

Calibration and risk-set calibration methods for fitting Cox proportional hazard model when a binary covariate is measured intermittently. Methods include functions to fit calibration models from interval-censored data and modified partial likelihood for the proportional hazard model, Nevo et al. (2018+) <arXiv:1801.01529>.

Sports Injury Data analysis aims to identify and describe the magnitude of the injury problem, and to gain more insights (e.g. determine potential risk factors) by statistical modelling approaches. The injurytools package provides standardized routines and utilities that simplify such analyses. It offers functions for data preparation, informative visualizations and descriptive and model-based analyses.

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 a small collection of various network data sets, to use with the igraph package: the Enron email network, various food webs, interactions in the immunoglobulin protein, the karate club network, Koenigsberg's bridges, visuotactile brain areas of the macaque monkey, UK faculty friendship network, domestic US flights network, etc.

Simulation of chromosomal segments shared identical-by-descent (IBD) among pedigree members. Using sex-specific recombination rates along the human genome (Halldorsson et al. (2019) <doi:10.1126/science.aau1043>), phased chromosomes are simulated for all pedigree members. Applications include calculation of realised relatedness coefficients and IBD segment distributions. ibdsim2 is part of the pedsuite collection of packages for pedigree analysis. A detailed presentation of the pedsuite', including a separate chapter on ibdsim2', is available in the book Pedigree analysis in R (Vigeland, 2021, ISBN:9780128244302). A Shiny app for visualising and comparing IBD distributions is available at <https://magnusdv.shinyapps.io/ibdsim2-shiny/>.