This package provides classes and functions for working with IP (Internet Protocol) addresses and networks, inspired by the Python ipaddress module. Offers full support for both IPv4 and IPv6 (Internet Protocol versions 4 and 6) address spaces. It is specifically designed to work well with the tidyverse'.

Reverse engineer a regular expression pattern for the characters contained in an R object. Individual characters can be categorised into digits, letters, punctuation or spaces and encoded into run-lengths. This can be used to summarise the structure of a dataset or identify non-standard entries. Many non-character inputs such as numeric vectors and data frames are supported.

Computation of test statistics of independence between (continuous) innovations of time series. They can be used with stochastic volatility models and Hidden Markov Models (HMM). This improves the results in Duchesne, Ghoudi & Remillard (2012) <doi:10.1002/cjs.11141>.

Supplements for a book, "iTOS" = "Introduction to the Theory of Observational Studies." Data sets are aHDL from Rosenbaum (2023a) <doi:10.1111/biom.13558> and bingeM from Rosenbaum (2023b) <doi:10.1111/biom.13921>. The function makematch() uses two-criteria matching from Zhang et al. (2023) <doi:10.1080/01621459.2021.1981337> to create the matched data bingeM from binge'. The makematch() function also implements optimal matching (Rosenbaum (1989) <doi:10.2307/2290079>) and matching with fine or near-fine balance (Rosenbaum et al. (2007) <doi:10.1198/016214506000001059> and Yang et al (2012) <doi:10.1111/j.1541-0420.2011.01691.x>). The book makes use of two other R packages, weightedRank and tightenBlock'.

This package provides tools for measuring income and wealth inequality. Computes the Gini coefficient with bootstrap or asymptotic confidence intervals following Davidson (2009) <doi:10.1016/j.jeconom.2008.09.011>, the extended S-Gini family, Theil T and L indices (generalised entropy family), the Atkinson index, the Kolm absolute inequality index, Palma ratio, Hoover index, percentile ratios, and Lorenz curves. Supports between-within group decomposition following Bourguignon (1979) <doi:10.2307/1914138>, income share tabulation, concentration indices for health inequality with Erreygers (2009) correction, Kakwani tax progressivity and Reynolds-Smolensky redistribution indices, Foster-Greer-Thorbecke poverty measures including the Sen index, growth incidence curves following Ravallion and Chen (2003) <doi:10.1016/S0165-1765(02)00205-7>, and Wolfson polarisation. All functions accept optional survey weights and work with data from any source.

Classical Ising Model is a land mark system in statistical physics.The model explains the physics of spin glasses and magnetic materials, and cooperative phenomenon in general, for example phase transitions and neural networks.This package provides utilities to simulate one dimensional Ising Model with Metropolis and Glauber Monte Carlo with single flip dynamics in periodic boundary conditions. Utility functions for exact solutions are provided. Such as transfer matrix for 1D. Utility functions for exact solutions are provided. Example use cases are as follows: Measuring effective ergodicity and power-laws in so called functional-diffusion. Example usage contains parallel runs, fitting power-laws, finite size scaling, computing autocorrelation, uncertainty analysis and plotting utilities.

We construct the explicit form of clamped cubic interpolating spline (both uniform - knots are equidistant and non-uniform - knots are arbitrary). Using this form, we propose a linear regression model suitable for real data smoothing.

Generates efficient designs for discrete choice experiments based on the multinomial logit model, and individually adapted designs for the mixed multinomial logit model. The generated designs can be presented on screen and choice data can be gathered using a shiny application. Traets F, Sanchez G, and Vandebroek M (2020) <doi:10.18637/jss.v096.i03>.

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>. The output of this function is a data frame that consists of four rows containing the following information: (1) the overall IAT effect size for the participant's dataset, (2) the effect size calculated for odd trials only, (3) the effect size calculated for even trials only, and (4) the proportion of trials with reaction times under 300ms (which is important for exclusion purposes). Items (2) and (3) allow for a measure of the internal consistency of the IAT. Specifically, you can use the subsetted IAT effect sizes for odd and even trials to calculate Cronbach's alpha across participants in the sample. 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 220, 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). 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). A sample dataset (titled sampledata') is included in this package to practice with.

These are data and functions to support quantitative peace science research. The data are important state-year information on democracy and wealth, which require periodic updates and regular maintenance. The functions permit some exploratory and diagnostic assessment of the kinds of data in demand by the community, but do not impose many dependencies on the user.

Estimate the relative abundance of tissue-infiltrating immune subpopulations abundances using gene expression data.

This package provides functions to fetch market data, search historical prices, execute trades, and get account details from the IG Trading REST API <https://labs.ig.com>. Returns tidy tibbles for easy analysis. Trading contracts for difference (CFDs), options and spread bets carries a high risk of losing money. This package is not financial or trading advice.

The Interactive Tree Of Life <https://itol.embl.de/> online server can edit and annotate trees interactively. The itol.toolkit package can support all types of annotation templates.

This package provides functions to access real-time infectious disease data from the disease.sh API', including COVID-19 global, US states, continent, and country statistics, vaccination coverage, influenza-like illness data from the Centers for Disease Control and Prevention (CDC), and more. Also includes curated datasets on a variety of infectious diseases such as influenza, measles, dengue, Ebola, tuberculosis, meningitis, AIDS, and others. The package supports epidemiological research and data analysis by combining API access with high-quality historical and survey datasets on infectious diseases. For more details on the disease.sh API', see <https://disease.sh/>.

Assists in generating categorical clustered outcome data, estimating the Intracluster Correlation Coefficient (ICC) for nominal or ordinal data with 2+ categories under the resampling and method of moments (MoM) methods, with confidence intervals.

This package provides access to granular socioeconomic indicators from the Spanish Statistical Office (INE) Household Income Distribution Atlas. The package downloads and processes data from a companion GitHub repository (<https://github.com/pablogguz/ineAtlas.data/>) which contains processed versions of the official INE Atlas data. Functions are provided to fetch data at multiple geographic levels (municipalities, districts, and census tracts), including income indicators, demographic characteristics, and inequality metrics. The data repository is updated every year when new releases are published by INE.

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>.

This package implements Interpretable Boosted Linear Models (IBLMs). These combine a conventional generalized linear model (GLM) with a machine learning component, such as XGBoost. The package also provides tools within for explaining and analyzing these models. For more details see Gawlowski and Wang (2025) <https://ifoa-adswp.github.io/IBLM/reference/figures/iblm_paper.pdf>.

When you want to install R package or download file from GitHub, but you can't access GitHub, this package helps you install R packages or download file from GitHub via the proxy website <https://gh-proxy.com/> or <https://ghfast.top/>, which is in real-time sync with GitHub.

This package contains some important regression methods for interval-valued variables. For each method, it is available the fitted values, residuals and some goodness-of-fit measures.

Imbalanced domain learning has almost exclusively focused on solving classification tasks, where the objective is to predict cases labelled with a rare class accurately. Such a well-defined approach for regression tasks lacked due to two main factors. First, standard regression tasks assume that each value is equally important to the user. Second, standard evaluation metrics focus on assessing the performance of the model on the most common cases. This package contains methods to tackle imbalanced domain learning problems in regression tasks, where the objective is to predict extreme (rare) values. The methods contained in this package are: 1) an automatic and non-parametric method to obtain such relevance functions; 2) visualisation tools; 3) suite of evaluation measures for optimisation/validation processes; 4) the squared-error relevance area measure, an evaluation metric tailored for imbalanced regression tasks. More information can be found in Ribeiro and Moniz (2020) <doi:10.1007/s10994-020-05900-9>.

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., (2025) <doi:10.1093/bioinformatics/btaf055>.

Imputation of missing values using the last observation carried forward technique on Indonesia food prices data that is time series data. Also, this technique applies imputation to data whose dates do not appear directly. So that the series assumptions in the time series data are met.

Get image statistics based on processing fluency theory. The functions provide scores for several basic aesthetic principles that facilitate fluent cognitive processing of images: contrast, complexity / simplicity, self-similarity, symmetry, and typicality. See Mayer & Landwehr (2018) <doi:10.1037/aca0000187> and Mayer & Landwehr (2018) <doi:10.31219/osf.io/gtbhw> for the theoretical background of the methods.