Run Paris Agreement Capital Transition Assessment ('PACTA') analyses on multiple loan books in a structured way. Provides access to standard PACTA metrics and additional PACTA'-related metrics for multiple loan books. Results take the form of csv files and plots and are exported to user-specified project paths.

Drop-in replacements for standard base graphics functions. The replacements are prettier versions of the originals.

Identifies the entries with patterned responses for psychometric scales. The patterns included in the package are identical (a, a, a), ascending (a, b, c), descending (c, b, a), alternative (a, b, a, b / a, b, c, a, b, c).

Extends the Heckman selection framework to panel data with individual random effects. The first stage models participation via a panel Probit specification, while the second stage can take a panel linear, Probit, Poisson, or Poisson log-normal form. Model details are provided in Bailey and Peng (2025) <doi:10.2139/ssrn.5475626> and Peng and Van den Bulte (2024) <doi:10.1287/mnsc.2019.01897>.

This package provides methods for assessing the performance of a prediction model with respect to identifying patient-level treatment benefit. All methods are applicable for continuous and binary outcomes, and for any type of statistical or machine-learning prediction model as long as it uses baseline covariates to predict outcomes under treatment and control.

Enable users to measure and record the execution time of pipe operations (using |>) with optional logging to dataframes and output to the console.

This package provides a comprehensive library for colour vectors and colour palettes using a new family of colour classes (palettes_colour and palettes_palette) that always print as hex codes with colour previews. Capabilities include: formatting, casting and coercion, extraction and updating of components, plotting, colour mixing arithmetic, and colour interpolation.

This package performs sensitivity analysis for publication bias in meta-analyses (per Mathur & VanderWeele, 2020 [<doi:10.31219/osf.io/s9dp6>]). These analyses enable statements such as: "For publication bias to shift the observed point estimate to the null, significant results would need to be at least 30-fold more likely to be published than negative or nonsignificant results." Comparable statements can be made regarding shifting to a chosen non-null value or shifting the confidence interval. Provides a worst-case meta-analytic point estimate under maximal publication bias obtained simply by conducting a standard meta-analysis of only the negative and "nonsignificant" studies.

This package provides functions and data-sets that are helpful for teaching statistics and data analysis. It was originally designed for use when teaching students in the Psychology Department at Nottingham Trent University.

Utilize the Bayesian prior and posterior predictive checking approach to provide a statistical assessment of replication success and failure. The package is based on the methods proposed in Zhao,Y., Wen X.(2021) <arXiv:2105.03993>.

Designed for prediction error estimation through resampling techniques, possibly accelerated by parallel execution on a compute cluster. Newly developed model fitting routines can be easily incorporated. Methods used in the package are detailed in Porzelius Ch., Binder H. and Schumacher M. (2009) <doi:10.1093/bioinformatics/btp062> and were used, for instance, in Porzelius Ch., Schumacher M. and Binder H. (2011) <doi:10.1007/s00180-011-0236-6>.

Implementation of class "polyMatrix" for storing a matrix of polynomials and implements basic matrix operations; including a determinant and characteristic polynomial. It is based on the package polynom and uses a lot of its methods to implement matrix operations. This package includes 3 methods of triangularization of polynomial matrices: Extended Euclidean algorithm which is most classical but numerically unstable; Sylvester algorithm based on LQ decomposition; Interpolation algorithm is based on LQ decomposition and Newton interpolation. Both methods are described in D. Henrion & M. Sebek, Reliable numerical methods for polynomial matrix triangularization, IEEE Transactions on Automatic Control (Volume 44, Issue 3, Mar 1999, Pages 497-508) <doi:10.1109/9.751344> and in Salah Labhalla, Henri Lombardi & Roger Marlin, Algorithmes de calcule de la reduction de Hermite d'une matrice a coefficients polynomeaux, Theoretical Computer Science (Volume 161, Issue 1-2, July 1996, Pages 69-92) <doi:10.1016/0304-3975(95)00090-9>.

This is a data only package, that provides distances from a paper plane experiment.

The pwrss R package provides flexible and comprehensive functions for statistical power and minimum required sample size calculations across a wide range of commonly used hypothesis tests in psychological, biomedical, and social sciences.

This package provides a framework for creating interactive figures for data exploration. All plots are automatically linked and support several kinds of interactive features, including selection, zooming, panning, and parameter manipulation. The figures can be interacted with either manually, using a mouse and a keyboard, or by running code from inside an active R session.

Bundles a number of established statistical methods to facilitate the visual interpretation of large datasets in sedimentary geology. Includes functionality for adaptive kernel density estimation, principal component analysis, correspondence analysis, multidimensional scaling, generalised procrustes analysis and individual differences scaling using a variety of dissimilarity measures. Univariate provenance proxies, such as single-grain ages or (isotopic) compositions are compared with the Kolmogorov-Smirnov, Kuiper, Wasserstein-2 or Sircombe-Hazelton L2 distances. Categorical provenance proxies such as chemical compositions are compared with the Aitchison and Bray-Curtis distances,and count data with the chi-square distance. Varietal data can either be converted to one or more distributional datasets, or directly compared using the multivariate Wasserstein distance. Also included are tools to plot compositional and count data on ternary diagrams and point-counting data on radial plots, to calculate the sample size required for specified levels of statistical precision, and to assess the effects of hydraulic sorting on detrital compositions. Includes an intuitive query-based user interface for users who are not proficient in R.

This package provides profile likelihoods for a parameter of interest in commonly used statistical models. The models include linear models, generalized linear models, proportional odds models, linear mixed-effects models, and linear models for longitudinal responses fitted by generalized least squares. The package also provides plots for normalized profile likelihoods as well as the maximum profile likelihood estimates and the kth likelihood support intervals.

Latent class analysis and latent class regression models for polytomous outcome variables. Also known as latent structure analysis.

This package provides tools from the domain of graph theory can be used to quantify the complexity and vulnerability to failure of a software package. That is the guiding philosophy of this package. pkgnet provides tools to analyze the dependencies between functions in an R package and between its imported packages. See the pkgnet website for vignettes and other supplementary information.

Three-dimensional systematic conservation planning, conducting nested prioritization analyses across multiple depth levels and ensuring efficient resource allocation throughout the water column. It provides a structured workflow designed to address biodiversity conservation and management challenges in the 3 dimensions, while facilitating usersâ choices and parameterization (Doxa et al. 2025 <doi:10.1016/j.ecolmodel.2024.110919>).

Statistical power analysis for designs including t-tests, correlations, multiple regression, ANOVA, mediation, and logistic regression. Functions accompany Aberson (2019) <doi:10.4324/9781315171500>.

Support functions, data sets, and vignettes for the psych package. Contains several of the biggest data sets for the psych package as well as four vignettes. A few helper functions for file manipulation are included as well. For more information, see the <https://personality-project.org/r/> web page.

Screens and sorts phylogenetic trees in both traditional and extended Newick format. Allows for the fast and flexible screening (within a tree) of Exclusive clades that comprise only the target taxa and/or Non- Exclusive clades that includes a defined portion of non-target taxa.

This package provides a collection of software provides R support for ADMB (Automatic Differentiation Model Builder) and a GUI interface facilitates the conversion of ADMB template code to C code followed by compilation to a binary executable. Stand-alone functions can also be run by users not interested in clicking a GUI'.