N>=3 methods are used to measure each of n items. The data are used to estimate simultaneously systematic error (bias) and random error (imprecision). Observed measurements for each method or device are assumed to be linear functions of the unknown true values and the errors are assumed normally distributed. Pairwise calibration curves and plots can be easily generated. Unlike the ncb.od function, the omx function builds a one-factor measurement error model using OpenMx and allows missing values, uses full information maximum likelihood to estimate parameters, and provides both likelihood-based and bootstrapped confidence intervals for all parameters, in addition to Wald-type intervals.

Compute bending energies, principal warps, partial warp scores, and the non-affine component of shape variation for 2D landmark configurations, as well as Mardia-Dryden distributions and self-similar distributions of landmarks, as described in Mitteroecker et al. (2020) <doi:10.1093/sysbio/syaa007>. Working examples to decompose shape variation into small-scale and large-scale components, and to decompose the total shape variation into outline and residual shape components are provided. Two landmark datasets are provided, that quantify skull morphology in humans and papionin primates, respectively from Mitteroecker et al. (2020) <doi:10.5061/dryad.j6q573n8s> and Grunstra et al. (2020) <doi:10.5061/dryad.zkh189373>.

This package provides functions to estimate the size-controlled phenotypic integration index, a novel method by Torices & Méndez (2014) <doi:10.1086/676622> to solve problems due to individual size when estimating integration (namely, larger individuals have larger components, which will drive a correlation between components only due to resource availability that might obscure the observed measures of integration). In addition, the package also provides the classical estimation by Wagner (1984) <doi:10.1007/BF00275224>, bootstrapping and jackknife methods to calculate confidence intervals and a significance test for both integration indices. Further details can be found in Torices & Muñoz-Pajares <doi:10.3732/apps.1400104>.

In clinical trials, endpoints are sometimes evaluated with uncertainty. Adjudication is commonly adopted to ensure the study integrity. We propose to use multiple imputation (MI) introduced by Robin (1987) <doi:10.1002/9780470316696> to incorporate these uncertainties if reasonable event probabilities were provided. The method has been applied to Cox Proportional Hazard (PH) model, Kaplan-Meier (KM) estimation and Log-rank test in this package. Moreover, weighted estimations discussed in Cook (2004) <doi:10.1016/S0197-2456(00)00053-2> were also implemented with weights calculated from event probabilities. In conclusion, this package can handle time-to-event analysis if events presented with uncertainty by different methods.

This package enables the detection of driver chromosomal imbalances including loss of heterozygosity (LOH) from array comparative genomic hybridization (aCGH) data. VegaMC performs a joint segmentation of a dataset and uses a statistical framework to distinguish between driver and passenger mutation. VegaMC has been implemented so that it can be immediately integrated with the output produced by PennCNV tool. In addition, VegaMC produces in output two web pages that allows a rapid navigation between both the detected regions and the altered genes. In the web page that summarizes the altered genes, the link to the respective Ensembl gene web page is reported.

The purpose of this package is to provide a lightweight and unified Future API for sequential and parallel processing of R expression via futures. This package implements sequential, multicore, multisession, and cluster futures. With these, R expressions can be evaluated on the local machine, in parallel a set of local machines, or distributed on a mix of local and remote machines. Extensions to this package implement additional backends for processing futures via compute cluster schedulers etc. Because of its unified API, there is no need to modify any code in order to switch from sequential on the local machine to, say, distributed processing on a remote compute cluster.

Computerized tomography (CT) can be used to assess certain wood properties when wood disks or logs are scanned. Wood density profiles (i.e. variations of wood density from pith to bark) can yield important information used for studies in forest resource assessment, wood quality and dendrochronology studies. The first step consists in transforming grey values from the scan images to density values. The packages then proposes a unique method to automatically locate the pith by combining an adapted Hough Transform method and a one-dimensional edge detector. Tree ring profiles (average ring density, earlywood and latewood density, ring width and percent latewood for each ring) are then obtained.

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>.

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 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 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.

cosmiq is a tool for the preprocessing of liquid- or gas - chromatography mass spectrometry (LCMS/GCMS) data with a focus on metabolomics or lipidomics applications. To improve the detection of low abundant signals, cosmiq generates master maps of the mZ/RT space from all acquired runs before a peak detection algorithm is applied. The result is a more robust identification and quantification of low-intensity MS signals compared to conventional approaches where peak picking is performed in each LCMS/GCMS file separately. The cosmiq package builds on the xcmsSet object structure and can be therefore integrated well with the package xcms as an alternative preprocessing step.

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.

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>.

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.

This package implements macroevolutionary analyses on phylogenetic trees. See Morlon et al. (2010) <DOI:10.1371/journal.pbio.1000493>, Morlon et al. (2011) <DOI:10.1073/pnas.1102543108>, Condamine et al. (2013) <DOI:10.1111/ele.12062>, Morlon et al. (2014) <DOI:10.1111/ele.12251>, Manceau et al. (2015) <DOI:10.1111/ele.12415>, Lewitus & Morlon (2016) <DOI:10.1093/sysbio/syv116>, Drury et al. (2016) <DOI:10.1093/sysbio/syw020>, Manceau et al. (2016) <DOI:10.1093/sysbio/syw115>, Morlon et al. (2016) <DOI:10.1111/2041-210X.12526>, Clavel & Morlon (2017) <DOI:10.1073/pnas.1606868114>, Drury et al. (2017) <DOI:10.1093/sysbio/syx079>, Lewitus & Morlon (2017) <DOI:10.1093/sysbio/syx095>, Drury et al. (2018) <DOI:10.1371/journal.pbio.2003563>, Clavel et al. (2019) <DOI:10.1093/sysbio/syy045>, Maliet et al. (2019) <DOI:10.1038/s41559-019-0908-0>, Billaud et al. (2019) <DOI:10.1093/sysbio/syz057>, Lewitus et al. (2019) <DOI:10.1093/sysbio/syz061>, Aristide & Morlon (2019) <DOI:10.1111/ele.13385>, Maliet et al. (2020) <DOI:10.1111/ele.13592>, Drury et al. (2021) <DOI:10.1371/journal.pbio.3001270>, Perez-Lamarque & Morlon (2022) <DOI:10.1111/mec.16478>, Perez-Lamarque et al. (2022) <DOI:10.1101/2021.08.30.458192>, Mazet et al. (2023) <DOI:10.1111/2041-210X.14195>, Drury et al. (2024) <DOI:10.1016/j.cub.2023.12.055>.

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.