Identification and network inference of genetic loci associated with correlation changes in quantitative traits (called correlated trait loci, CTLs). Arends et al. (2016) <doi:10.21105/joss.00087>.

Perform the functional modeling methods of Huang and Wang (2018) <doi:10.1111/biom.12741> to accommodate dependent error in covariates of the proportional hazards model. The adopted measurement error model has minimal assumptions on the dependence structure, and an instrumental variable is supposed to be available.

Explore calcium (Ca) and phosphate (Pi) homeostasis with two novel Shiny apps, building upon on a previously published mathematical model written in C, to ensure efficient computations. The underlying model is accessible here <https://pubmed.ncbi.nlm.nih.gov/28747359/)>. The first application explores the fundamentals of Ca-Pi homeostasis, while the second provides interactive case studies for in-depth exploration of the topic, thereby seeking to foster student engagement and an integrative understanding of Ca-Pi regulation.

This package provides a generic, easy-to-use and expandable implementation of a pharmacokinetic (PK) / pharmacodynamic (PD) model based on the S4 class system. This package allows the user to read and write pharmacometric models from and to files, including a JSON-based interface to import Campsis models defined using a formal JSON schema distributed with the package. Models can be adapted further on the fly in the R environment using an intuitive API to add, modify or delete equations, ordinary differential equations (ODEs), model parameters or compartment properties (such as infusion duration or rate, bioavailability and initial values). The package also provides export facilities for use with the simulation packages rxode2 and mrgsolve'. The package itself is licensed under the GPL (>= 3); the JSON schema file shipped in inst/extdata is licensed separately under the Creative Commons Attribution 4.0 International (CC BY 4.0). This package is designed and intended to be used with the package campsis', a PK/PD simulation platform built on top of rxode2 and mrgsolve'.

Routines doing cone projection and quadratic programming, as well as doing estimation and inference for constrained parametric regression and shape-restricted regression problems. See Mary C. Meyer (2013)<doi:10.1080/03610918.2012.659820> for more details.

This package implements lasso and ridge regression for dichotomised outcomes (<doi:10.1080/02664763.2023.2233057>), i.e., numerical outcomes that were transformed to binary outcomes. Such artificial binary outcomes indicate whether an underlying measurement is greater than a threshold.

The Codemeta Project defines a JSON-LD format for describing software metadata, as detailed at <https://codemeta.github.io>. This package provides utilities to generate, parse, and modify codemeta.json files automatically for R packages, as well as tools and examples for working with codemeta.json JSON-LD more generally.

This package provides a collection of functions to generate a large variety of structures in high dimensions. These data structures are useful for testing, validating, and improving algorithms used in dimensionality reduction, clustering, machine learning, and visualization.

Package contains functions for analyzing check-all-that-apply (CATA) data from consumer and sensory tests. Cochran's Q test, McNemar's test, and Penalty-Lift analysis are provided; for details, see Meyners, Castura & Carr (2013) <doi:10.1016/j.foodqual.2013.06.010>. Cluster analysis can be performed using b-cluster analysis, then evaluated using various measures; for details, see Castura, Meyners, Varela & Næs (2022) <doi:10.1016/j.foodqual.2022.104564>. Consumers can also be clustered on their product-related hedonic responses; see Castura, Meyners, Pohjanheimo, Varela & Næs (2023) <doi:10.1111/joss.12860>. Permutation tests based on the L1-norm methods are provided; for details, see Chaya, Castura & Greenacre (2025) <doi:10.1016/j.foodqual.2025.105639>.

Wraps cytoscape.js as a shiny widget. cytoscape.js <https://js.cytoscape.org/> is a Javascript-based graph theory (network) library for visualization and analysis. This package supports the visualization of networks with custom visual styles and several available layouts. Demo Shiny applications are provided in the package code.

To re-calculate the coefficients and the standard deviation when changing the reference group.

This package provides a comprehensive high-level package, for composite indicator construction and analysis. It is a "development environment" for composite indicators and scoreboards, which includes utilities for construction (indicator selection, denomination, imputation, data treatment, normalisation, weighting and aggregation) and analysis (multivariate analysis, correlation plotting, short cuts for principal component analysis, global sensitivity analysis, and more). A composite indicator is completely encapsulated inside a single hierarchical list called a "coin". This allows a fast and efficient work flow, as well as making quick copies, testing methodological variations and making comparisons. It also includes many plotting options, both statistical (scatter plots, distribution plots) as well as for presenting results.

Create CUSUM (cumulative sum) statistics from a vector or dataframe. Also create single or faceted CUSUM control charts, with or without control limits. Accepts vector, dataframe, tibble or data.table inputs.

Generates tree plots with precise branch lengths, gene annotations, and cellular prevalence. The package handles complex tree structures (angles, lengths, etc.) and can be further refined as needed by the user.

Draws causal hypergraph plots from models output by configurational comparative methods such as Coincidence Analysis (CNA) or Qualitative Comparative Analysis (QCA).

Uses data from the EPSG Registry to look up suitable coordinate reference system transformations for spatial datasets in R. Returns a data frame with CRS codes that can be used for CRS transformation and mapping projects. Please see the EPSG Dataset Terms of Use at <https://epsg.org/terms-of-use.html> for more information.

Calculation of consensus values for atomic weights, isotope amount ratios, and isotopic abundances with the associated uncertainties using multivariate meta-regression approach for consensus building.

This package creates ggplot2 Cumulative Residual (CURE) plots to check the goodness-of-fit of a count model; or the tables to create a customized version. A dataset of crashes in Washington state is available for illustrative purposes.

This package provides a comprehensive and automated workflow for managing multicollinearity in data frames with numeric and/or categorical variables. The package integrates five robust methods into a single function: (1) target encoding of categorical variables based on response values (Micci-Barreca, 2001 (Micci-Barreca, D. 2001 <doi:10.1145/507533.507538>); (2) automated feature prioritization to preserve key predictors during filtering; (3 and 4) pairwise correlation and VIF filtering across all variable types (numericâ numeric, numericâ categorical, and categoricalâ categorical); (5) adaptive correlation and VIF thresholds. Together, these methods enable a reliable multicollinearity management in most use cases while maintaining model integrity. The package also supports parallel processing and progress tracking via the packages future and progressr', and provides seamless integration with the tidymodels ecosystem through a dedicated recipe step.

Allows inferring gene regulatory networks with direct physical interactions from microarray expression data using C3NET.

Create correlation (or partial correlation) matrices. Correlation matrices are formatted with significance stars based on user preferences. Matrices of coefficients, p-values, and number of pairwise observations are returned. Send resultant formatted matrices to the clipboard to be pasted into excel and other programs. A plot method allows users to visualize correlation matrices created with corx'.

Filtering, also known as gating, of flow cytometry samples using the curvHDR method, which is described in Naumann, U., Luta, G. and Wand, M.P. (2010) <DOI:10.1186/1471-2105-11-44>.

This package provides a new robust principal component analysis algorithm is implemented that relies upon the Cauchy Distribution. The algorithm is suitable for high dimensional data even if the sample size is less than the number of variables. The methodology is described in this paper: Fayomi A., Pantazis Y., Tsagris M. and Wood A.T.A. (2024). "Cauchy robust principal component analysis with applications to high-dimensional data sets". Statistics and Computing, 34: 26. <doi:10.1007/s11222-023-10328-x>.

According to the codes and names of county-level and above administrative divisions released in 2022 by the Ministry of Civil Affairs of the People's Republic of China, the online vector map files were retrieved from the website (available at: <http://datav.aliyun.com/portal/school/atlas/area_selector>). This study was supported by the National Natural Science Foundation of China (NSFC, Grant No. 42205177).