Design and simulate fuzzy logic systems using Type-1 and Interval Type-2 Fuzzy Logic. This toolkit includes with graphical user interface (GUI) and an adaptive neuro- fuzzy inference system (ANFIS). This toolkit is a continuation from the previous package ('FuzzyToolkitUoN'). Produced by the Intelligent Modelling & Analysis Group (IMA) and Lab for UnCertainty In Data and decision making (LUCID), University of Nottingham. A big thank you to the many people who have contributed to the development/evaluation of the toolbox. Please cite the toolbox and the corresponding paper <doi:10.1109/FUZZ48607.2020.9177780> when using it. More related papers can be found in the NEWS.

This package provides convenient wrapper functions around the glue library for common string interpolation tasks. The package simplifies the process of combining glue string templating with common R functions like message(), warning(), stop(), print(), cat(), and file writing operations. Instead of manually calling glue() and then passing the result to these functions, glueDo provides direct wrapper functions that handle both steps in a single call. This is particularly useful for logging, error handling, and formatted output in R scripts and packages. The main reference for the underlying glue package is Hester and Bryan (2022) <https://CRAN.R-project.org/package=glue>.

In Latent Space Item Response Models, subjects and items are embedded in a multidimensional Euclidean latent space. As such, interactions among persons, items, and person-item combinations can be revealed that are unmodelled in more conventional item response theory models. This package implements the methods from Molenaar & Jeon (in press) and can be used to fit Latent Space Item Response Models to data using joint maximum likelihood estimation. The package can handle binary data, ordinal data, and data with mixed scales. The package incorporates facilities for data simulation, rotation of the latent space, and K-fold cross-validation to select the number of dimensions of the latent space.

This package implements the Bi-objective Lexicographical Classification method and Performance Assessment Ratio at 10% metric for algorithm classification. Constructs matrices representing algorithm performance under multiple criteria, facilitating decision-making in algorithm selection and evaluation. Analyzes and compares algorithm performance based on various metrics to identify the most suitable algorithms for specific tasks. This package includes methods for algorithm classification and evaluation, with examples provided in the documentation. Carvalho (2019) presents a statistical evaluation of algorithmic computational experimentation with infeasible solutions <doi:10.48550/arXiv.1902.00101>. Moreira and Carvalho (2023) analyze power in preprocessing methodologies for datasets with missing values <doi:10.1080/03610918.2023.2234683>.

Tabular data from statistical institutes and agencies are mostly confidential and must be protected prior to publications. The cell-key method is a post-tabular Statistical Disclosure Control perturbation technique that adds random noise to tabular data. The statistical properties of the perturbations are defined by some noise probability distributions - also referred to as perturbation tables. This tool can be used to create the perturbation tables based on a maximum entropy approach as described for example in Giessing (2016) <doi:10.1007/978-3-319-45381-1_18>. The perturbation tables created can finally be used to apply a cell-key method to frequency count or magnitude tables.

This package provides tools for both single and batch image manipulation and analysis (Olivoto, 2022 <doi:10.1111/2041-210X.13803>) and phytopathometry (Olivoto et al., 2022 <doi:10.1007/S40858-021-00487-5>). The tools can be used for the quantification of leaf area, object counting, extraction of image indexes, shape measurement, object landmark identification, and Elliptical Fourier Analysis of object outlines (Claude (2008) <doi:10.1007/978-0-387-77789-4>). The package also provides a comprehensive pipeline for generating shapefiles with complex layouts and supports high-throughput phenotyping of RGB, multispectral, and hyperspectral orthomosaics. This functionality facilitates field phenotyping using UAV- or satellite-based imagery.

This package provides functions implementing Single Source of Error state space models for purposes of time series analysis and forecasting. The package includes ADAM (Svetunkov, 2023, <https://openforecast.org/adam/>), Exponential Smoothing (Hyndman et al., 2008, <doi: 10.1007/978-3-540-71918-2>), SARIMA (Svetunkov & Boylan, 2019 <doi: 10.1080/00207543.2019.1600764>), Complex Exponential Smoothing (Svetunkov & Kourentzes, 2018, <doi: 10.13140/RG.2.2.24986.29123>), Simple Moving Average (Svetunkov & Petropoulos, 2018 <doi: 10.1080/00207543.2017.1380326>) and several simulation functions. It also allows dealing with intermittent demand based on the iETS framework (Svetunkov & Boylan, 2019, <doi: 10.13140/RG.2.2.35897.06242>).

Interacts with a suite of web application programming interfaces (API) for taxonomic tasks, such as getting database specific taxonomic identifiers, verifying species names, getting taxonomic hierarchies, fetching downstream and upstream taxonomic names, getting taxonomic synonyms, converting scientific to common names and vice versa, and more. Some of the services supported include NCBI E-utilities (<https://www.ncbi.nlm.nih.gov/books/NBK25501/>), Encyclopedia of Life (<https://eol.org/docs/what-is-eol/data-services>), Global Biodiversity Information Facility (<https://techdocs.gbif.org/en/openapi/>), and many more. Links to the API documentation for other supported services are available in the documentation for their respective functions in this package.

This package provides a two-stage regression method that can be used when various input data types are correlated, for example gene expression and methylation in drug response prediction. In the first stage it uses the upstream features (such as methylation) to predict the response variable (such as drug response), and in the second stage it uses the downstream features (such as gene expression) to predict the residuals of the first stage. In our manuscript (Aben et al., 2016, <doi:10.1093/bioinformatics/btw449>), we show that using TANDEM prevents the model from being dominated by gene expression and that the features selected by TANDEM are more interpretable.

Distributed Online Covariance Matrix Tests Docovt is a powerful tool designed to efficiently process and analyze distributed datasets. It enables users to perform covariance matrix tests in an online, distributed manner, making it highly suitable for large-scale data analysis. By leveraging advanced computational techniques, Docovt ensures robust and scalable solutions for statistical analysis, particularly in scenarios where data is dispersed across multiple nodes or sources. This package is ideal for researchers and practitioners working with high-dimensional data, providing a flexible and efficient framework for covariance matrix estimation and hypothesis testing. The philosophy of Docovt is described in Guo G.(2025) <doi:10.1016/j.physa.2024.130308>.

Models for analyzing site occupancy and count data models with detection error, including single-visit based models (Lele et al. 2012 <doi:10.1093/jpe/rtr042>, Moreno et al. 2010 <doi:10.1890/09-1073.1>, Solymos et al. 2012 <doi:10.1002/env.1149>, Denes et al. 2016 <doi:10.1111/1365-2664.12818>), conditional distance sampling and time-removal models (QPAD) (Solymos et al. 2013 <doi:10.1111/2041-210X.12106>, Solymos et al. 2018 <doi:10.1650/CONDOR-18-32.1>), and single bin QPAD (SQPAD) models (Lele & Solymos 2025). Package development was supported by the Alberta Biodiversity Monitoring Institute and the Boreal Avian Modelling Project.

Providing classes, methods, and functions to deal with financial networks. Users can easily store information about both physical and legal persons by using pre-made classes that are studied for integration with scraping packages such as rvest and RSelenium'. Moreover, the package assists in creating various types of financial networks depending on the type of relation between its units depending on the relation under scrutiny (ownership, board interlocks, etc.), the desired tie type (valued or binary), and renders them in the most common formats (adjacency matrix, incidence matrix, edge list, igraph', network'). There are also ad-hoc functions for the Fiedler value, global network efficiency, and cascade-failure analysis.

Functionality required to efficiently use R with IBM(R) Db2(R) Warehouse offerings (formerly IBM dashDB(R)) and IBM Db2 for z/OS(R) in conjunction with IBM Db2 Analytics Accelerator for z/OS. Many basic and complex R operations are pushed down into the database, which removes the main memory boundary of R and allows to make full use of parallel processing in the underlying database. For executing R-functions in a multi-node environment in parallel the idaTApply() function requires the SparkR package (<https://spark.apache.org/docs/latest/sparkr.html>). The optional ggplot2 package is needed for the plot.idaLm() function only.

This package provides functions for analysing eye tracking data, including event detection, visualizations and area of interest (AOI) based analyses. The package includes implementations of the IV-T, I-DT, adaptive velocity threshold, and Identification by two means clustering (I2MC) algorithms. See separate documentation for each function. The principles underlying I-VT and I-DT algorithms are described in Salvucci & Goldberg (2000,\doi10.1145/355017.355028). Two-means clustering is described in Hessels et al. (2017, \doi10.3758/s13428-016-0822-1). The adaptive velocity threshold algorithm is described in Nyström & Holmqvist (2010,\doi10.3758/BRM.42.1.188). See a demonstration in the URL.

We have designed this package to address experimental scenarios involving multiple covariates. It focuses on construction of Optimal Covariate Designs (OCDs), checking space filling property of the developed design. The primary objective of the package is to generate OCDs using four methods viz., M array method, Juxtapose method, Orthogonal Integer Array and Hadamard method. The package also evaluates space filling properties of both the base design and OCDs using the MaxPro criterion, providing a meaningful basis for comparison. In addition, it includes tool to visualize the spread offered by the design points in the form of scatterplot, which help users to assess distribution and coverage of design points.

This package provides functions for efficiently fitting linear models with spatially correlated errors by robust (Kuensch et al. (2011) <doi:10.3929/ethz-a-009900710>) and Gaussian (Harville (1977) <doi:10.1080/01621459.1977.10480998>) (Restricted) Maximum Likelihood and for computing robust and customary point and block external-drift Kriging predictions (Cressie (1993) <doi:10.1002/9781119115151>), along with utility functions for variogram modelling in ad hoc geostatistical analyses, model building, model evaluation by cross-validation, (conditional) simulation of Gaussian processes (Davies and Bryant (2013) <doi:10.18637/jss.v055.i09>), unbiased back-transformation of Kriging predictions of log-transformed data (Cressie (2006) <doi:10.1007/s11004-005-9022-8>).

Test for overall association between microbiome composition data and phenotypes via phylogenetic kernels. The phenotype can be univariate continuous or binary (Zhao et al. (2015) <doi:10.1016/j.ajhg.2015.04.003>), survival outcomes (Plantinga et al. (2017) <doi:10.1186/s40168-017-0239-9>), multivariate (Zhan et al. (2017) <doi:10.1002/gepi.22030>) and structured phenotypes (Zhan et al. (2017) <doi:10.1111/biom.12684>). The package can also use robust regression (unpublished work) and integrated quantile regression (Wang et al. (2021) <doi:10.1093/bioinformatics/btab668>). In each case, the microbiome community effect is modeled nonparametrically through a kernel function, which can incorporate phylogenetic tree information.

This package provides tools for extracting and processing structured annotations from R and Python source files to facilitate workflow visualization. The package scans source files for special PUT annotations that define nodes, connections, and metadata within a data processing workflow. These annotations can then be used to generate visual representations of data flows and processing steps across polyglot software environments. Builds on concepts from literate programming Knuth (1984) <doi:10.1093/comjnl/27.2.97> and utilizes directed acyclic graph (DAG) theory for workflow representation Foraita, Spallek, and Zeeb (2014) <doi:10.1007/978-0-387-09834-0_65>. Diagram generation powered by Mermaid Sveidqvist (2014) <https://mermaid.js.org/>.

The Statistical Package for REliability Data Analysis (SPREDA) implements recently-developed statistical methods for the analysis of reliability data. Modern technological developments, such as sensors and smart chips, allow us to dynamically track product/system usage as well as other environmental variables, such as temperature and humidity. We refer to these variables as dynamic covariates. The package contains functions for the analysis of time-to-event data with dynamic covariates and degradation data with dynamic covariates. The package also contains functions that can be used for analyzing time-to-event data with right censoring, and with left truncation and right censoring. Financial support from NSF and DuPont are acknowledged.

This package provides a graphical and automated pipeline for the analysis of short time-series in R ('santaR'). This approach is designed to accommodate asynchronous time sampling (i.e. different time points for different individuals), inter-individual variability, noisy measurements and large numbers of variables. Based on a smoothing splines functional model, santaR is able to detect variables highlighting significantly different temporal trajectories between study groups. Designed initially for metabolic phenotyping, santaR is also suited for other Systems Biology disciplines. Command line and graphical analysis (via a shiny application) enable fast and parallel automated analysis and reporting, intuitive visualisation and comprehensive plotting options for non-specialist users.

The CCREPE (Compositionality Corrected by REnormalizaion and PErmutation) package is designed to assess the significance of general similarity measures in compositional datasets. In microbial abundance data, for example, the total abundances of all microbes sum to one; CCREPE is designed to take this constraint into account when assigning p-values to similarity measures between the microbes. The package has two functions: ccrepe: Calculates similarity measures, p-values and q-values for relative abundances of bugs in one or two body sites using bootstrap and permutation matrices of the data. nc.score: Calculates species-level co-variation and co-exclusion patterns based on an extension of the checkerboard score to ordinal data.

Can be used to read and write a fwf with an accompanying Blaise datamodel. Blaise is the software suite built by Statistics Netherlands (CBS). It is essentially a way to write and collect surveys and perform statistical analysis on the data. It stores its data in fixed width format with an accompanying metadata file, this is the Blaise format. The package automatically interprets this metadata and reads the file into an R dataframe. When supplying a datamodel for writing, the dataframe will be automatically converted to that format and checked for compatibility. Supports dataframes, tibbles and LaF objects. For more information about Blaise', see <https://blaise.com/products/general-information>.

Fully Bayesian Classification with a subset of high-dimensional features, such as expression levels of genes. The data are modeled with a hierarchical Bayesian models using heavy-tailed t distributions as priors. When a large number of features are available, one may like to select only a subset of features to use, typically those features strongly correlated with the response in training cases. Such a feature selection procedure is however invalid since the relationship between the response and the features has be exaggerated by feature selection. This package provides a way to avoid this bias and yield better-calibrated predictions for future cases when one uses F-statistic to select features.

An R implementation of the python program Metabolomics Peak Analysis Computational Tool ('MPACT') (Robert M. Samples, Sara P. Puckett, and Marcy J. Balunas (2023) <doi:10.1021/acs.analchem.2c04632>). Filters in the package serve to address common errors in tandem mass spectrometry preprocessing, including: (1) isotopic patterns that are incorrectly split during preprocessing, (2) features present in solvent blanks due to carryover between samples, (3) features whose abundance is greater than user-defined abundance threshold in a specific group of samples, for example media blanks, (4) ions that are inconsistent between technical replicates, and (5) in-source fragment ions created during ionization before fragmentation in the tandem mass spectrometry workflow.