This package provides functions to perform robust nonparametric survival analysis with right censored data using a prior near-ignorant Dirichlet Process. Mangili, F., Benavoli, A., de Campos, C.P., Zaffalon, M. (2015) <doi:10.1002/bimj.201500062>.

Kappa statistics is one of the most used methods to evaluate the effectiveness of inpsections based on attribute assessments in industry. However, its estimation by available methods does not provide its "real" or "intrinstic" value. This package provides functions for the computation of the intrinsic kappa value as it is described in: Rafael Sanchez-Marquez, Frank Gerhorst and David Schindler (2023) "Effectiveness of quality inspections of attributive characteristics â A novel and practical method for estimating the â intrinsicâ value of kappa based on alpha and beta statistics." <doi:10.1016/j.cie.2023.109006>.

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>.

This package provides tools for assessment and quantification of individual identity information in animal signals. This package accompanies a research article by Linhart et al. (2019) <doi:10.1101/546143>: "Measuring individual identity information in animal signals: Overview and performance of available identity metrics".

Utilities to work with data from the Internal Displacement Monitoring Centre (IDMC) (<https://www.internal-displacement.org/>), with convenient functions for loading events data from the IDMC API and transforming events data to daily displacement estimates.

Computes bootstrapped Monte Carlo estimate of p value of Kolmogorov-Smirnov (KS) test and likelihood ratio test for zero-inflated count data, based on the work of Aldirawi et al. (2019) <doi:10.1109/BHI.2019.8834661>. With the package, user can also find tools to simulate random deviates from zero inflated or hurdle models and obtain maximum likelihood estimate of unknown parameters in these models.

Convert between bookmaker odds and probabilities. Eight different algorithms are available, including basic normalization, Shin's method (Hyun Song Shin, (1992) <doi:10.2307/2234526>), and others.

Develops a General Equilibrium (GE) Model, which estimates key variables such as wages, the number of residents and workers, the prices of the floor space, and its distribution between commercial and residential use, as in Ahlfeldt et al., (2015) <doi:10.3982/ECTA10876>. By doing so, the model allows understanding the economic influence of different urban policies.

Implementation of the methodology proposed in Data-driven design of targeted gene panels for estimating immunotherapy biomarkers', Bradley and Cannings (2021) <arXiv:2102.04296>. This package allows the user to fit generative models of mutation from an annotated mutation dataset, and then further to produce tunable linear estimators of exome-wide biomarkers. It also contains functions to simulate mutation annotated format (MAF) data, as well as to analyse the output and performance of models.

Analysis of the initialization for numerical optimization of real-valued functions, particularly likelihood functions of statistical models. See <https://loelschlaeger.de/ino/> for more details.

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.

Send emails using the mailgun api. To use this package you will need an account from <https://www.mailgun.com> .

This package provides a set of functions for performing null hypothesis testing on samples of persistence diagrams using the theory of permutations. Currently, only two-sample testing is implemented. Inputs can be either samples of persistence diagrams themselves or vectorizations. In the former case, they are embedded in a metric space using either the Bottleneck or Wasserstein distance. In the former case, persistence data becomes functional data and inference is performed using tools available in the fdatest package. Main reference for the interval-wise testing method: Pini A., Vantini S. (2017) "Interval-wise testing for functional data" <doi:10.1080/10485252.2017.1306627>. Main reference for inference on populations of networks: Lovato, I., Pini, A., Stamm, A., & Vantini, S. (2020) "Model-free two-sample test for network-valued data" <doi:10.1016/j.csda.2019.106896>.

Estimates regression coefficients in proportional hazards models for interval-censored multistate data. Individuals may be observed at irregular times and their states may be partially observed. Allowable transitions and transition-specific covariate effects can be specified. The numerical estimation is implemented in C++ using RcppArmadillo'. The method implemented in this package is described in You, Liu, and Krischer (2024) <doi:10.1002/sim.10079>.

Non-parametric tests of independence (mutual or serial) between some quantitative random vectors, as described in Bilodeau M. and Lafaye de Micheaux P. (2009) <doi:10.1016/j.jspi.2008.11.006>, in Beran R., Bilodeau M. and Lafaye de Micheaux P. (2007) <doi:10.1016/j.jmva.2007.01.009> and in Fan Y., Lafaye de Micheaux P., Penev S. and Salopek D. (2017) <doi:10.1016/j.jmva.2016.09.014>.

Prepare objects to implement models over spatial and spacetime domains with the INLA package (<https://www.r-inla.org>). These objects contain data to for the cgeneric interface in INLA', enabling fast parallel computations. We implemented the spatial barrier model, see Bakka et. al. (2019) <doi:10.1016/j.spasta.2019.01.002>, and some of the spatio-temporal models proposed in Lindgren et. al. (2024) <https://raco.cat/index.php/SORT/article/view/428665>. Details are provided in the available vignettes and from the URL bellow.

Implementation of the classifier described in the paper Ali HR et al (2014) <doi:10.1186/s13059-014-0431-1>. It uses copy number and/or expression form breast cancer data, trains a Tibshirani's pamr classifier with the features available and predicts the iC10 group.

This package implements approximate Bayesian inference for Structural Equation Models (SEM) using a custom adaptation of the Integrated Nested Laplace Approximation (Rue et al., 2009) <doi:10.1111/j.1467-9868.2008.00700.x> as described in Jamil and Rue (2026a) <doi:10.48550/arXiv.2603.25690>. Provides a computationally efficient alternative to Markov Chain Monte Carlo (MCMC) for Bayesian estimation, allowing users to fit latent variable models using the lavaan syntax. See also the companion paper on implementation and workflows, Jamil and Rue (2026b) <doi:10.48550/arXiv.2604.00671>.

Implementation of some Individual Based Models (IBMs, sensu Grimm and Railsback 2005) and methods to create new ones, particularly for population dynamics models (reproduction, mortality and movement). The basic operations for the simulations are implemented in Rcpp for speed.

Advanced fuzzy logic based techniques are implemented to compute the similarity among different objects or items. Typically, application areas consist of transforming raw data into the corresponding advanced fuzzy logic representation and determining the similarity between two objects using advanced fuzzy similarity techniques in various fields of research, such as text classification, pattern recognition, software projects, decision-making, medical diagnosis, and market prediction. Functions are designed to compute the membership, non-membership, hesitant-membership, indeterminacy-membership, and refusal-membership for the input matrices. Furthermore, it also includes a large number of advanced fuzzy logic based similarity measure functions to compute the Intuitionistic fuzzy similarity (IFS), Pythagorean fuzzy similarity (PFS), and Spherical fuzzy similarity (SFS) between two objects or items based on their fuzzy relationships. It also includes working examples for each function with sample data sets.

This package provides a native client for Apache Iceberg', the open table format used by Snowflake', Databricks', BigQuery', AWS and Dremio'. Rather than reaching Iceberg through a query engine such as DuckDB', this package talks to it directly: it connects to REST and AWS Glue catalogs, lists namespaces and tables, reads the schema and partition specification of a table, scans data with predicates and projections pushed down, travels back through snapshot history, and appends new data. Apache Arrow is the interchange layer throughout, so scan results arrive in R without a serialisation round trip. Built on iceberg-rust', the Apache-governed Rust implementation, via extendr'. Supports table spec versions 1 and 2; see the README for the full matrix of supported and unsupported features. This is a community package, not affiliated with or endorsed by The Apache Software Foundation; Apache', Apache Iceberg and Iceberg are trademarks of The Apache Software Foundation.

Generates a Graphviz graph of the most significant 3-way interaction gains (i.e. conditional information gains) based on a provided discrete data frame. Various output formats are supported ('Graphviz', SVG, PNG, PDF, PS). For references, see the webpage of Aleks Jakulin <http://stat.columbia.edu/~jakulin/Int/>.

This package provides facilities of general to specific model selection for exogenous regressors in 2SLS models. Furthermore, indicator saturation methods can be used to detect outliers and structural breaks in the sample.

This package provides a tool to calculate and plot estimates from models in which an interaction between the main predictor and a continuous covariate has been specified. Methods used in the package refer to Harrell Jr FE (2015, ISBN:9783319330396); Durrleman S, Simon R. (1989) <doi:10.1002/sim.4780080504>; Greenland S. (1995) <doi:10.1097/00001648-199507000-00005>.