Creation and selection of (Advanced) Coupled Matrix and Tensor Factorization (ACMTF) and ACMTF-Regression (ACMTF-R) models. Selection of the optimal number of components can be done using ACMTF_modelSelection() and ACMTFR_modelSelection()'. The CMTF and ACMTF methods were originally described by Acar et al., 2011 <doi:10.48550/arXiv.1105.3422> and Acar et al., 2014 <doi:10.1186/1471-2105-15-239>, respectively.

This package provides tools for creating and visualizing statistical process control charts. Control charts are used for monitoring measurement processes, such as those occurring in manufacturing. The objective is to monitor the history of such processes and flag outlying measurements: out-of-control signals. Montgomery, D. (2009, ISBN:978-0-470-16992-6) contains an extensive discussion of the methodology.

Discover causality for bivariate categorical data. This package aims to enable users to discover causality for bivariate observational categorical data. See Ni, Y. (2022) <arXiv:2209.08579> "Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation. Advances in Neural Information Processing Systems 35 (in press)".

Cristin to Zotero ('c2z') aims at obtaining total dominion over Cristin ('Current Research Information SysTem in Norway') and Zotero'. The package enables manipulating Zotero libraries using R'. Import references from Cristin', Regjeringen', CRAN', ISBN ('Alma', LoC'), and DOI ('CrossRef', DataCite') to a Zotero library. Add, edit, copy, or delete items, including attachments and collections, and export references to BibLaTeX (and other formats).

Several authors have proposed methods for constructing simultaneous confidence intervals for multinomial proportions. The package implements seven classical approachesâ Wilson, Quesenberry and Hurst, Goodman, Wald (with and without continuity correction), Fitzpatrick and Scott, and Sison and Glazâ along with Bayesian methods based on Dirichlet models. Both equal and unequal Dirichlet priors are supported, providing a broad framework for inference, data analysis, and sensitivity evaluation.

This package contains the basic functions to apply the unified framework for partitioning the drivers of stability of ecological communities. Segrestin et al. (2024) <doi:10.1111/geb.13828>.

Fast and memory-efficient (or cheap') tools to facilitate efficient programming, saving time and memory. It aims to provide cheaper alternatives to common base R functions, as well as some additional functions.

Package to analyze the clinical utility of a biomarker. It provides the clinical utility curve, clinical utility table, efficacy of a biomarker, clinical efficacy curve and tests to compare efficacy between markers.

Detection of outliers in circular-circular regression models, modifying its and estimating of models parameters.

Provide the CrossClustering algorithm (Tellaroli et al. (2016) <doi:10.1371/journal.pone.0152333>), which is a partial clustering algorithm that combines the Ward's minimum variance and Complete Linkage algorithms, providing automatic estimation of a suitable number of clusters and identification of outlier elements.

Various utilities for the complex multivariate Gaussian distribution and complex Gaussian processes.

This package creates compact letter displays (CLDs) for pairwise comparisons from statistical post-hoc tests. Groups sharing the same letter are not significantly different from each other. Supports multiple input formats including results from stats pairwise tests, DescTools', PMCMRplus', rstatix', symmetric matrices of p-values, and data frames. Provides a consistent interface for visualizing statistical groupings across different testing frameworks.

While data from randomized experiments remain the gold standard for causal inference, estimation of causal estimands from observational data is possible through various confounding adjustment methods. However, the challenge of unmeasured confounding remains a concern in causal inference, where failure to account for unmeasured confounders can lead to biased estimates of causal estimands. Sensitivity analysis within the framework of causal inference can help adjust for possible unmeasured confounding. In `causens`, three main methods are implemented: adjustment via sensitivity functions (Brumback, Hernán, Haneuse, and Robins (2004) <doi:10.1002/sim.1657> and Li, Shen, Wu, and Li (2011) <doi:10.1093/aje/kwr096>), Bayesian parametric modelling and Monte Carlo approaches (McCandless, Lawrence C and Gustafson, Paul (2017) <doi:10.1002/sim.7298>).

Detection of change-points for variance of heteroscedastic Gaussian variables with piecewise constant variance function. Adelfio, G. (2012), Change-point detection for variance piecewise constant models, Communications in Statistics, Simulation and Computation, 41:4, 437-448, <doi:10.1080/03610918.2011.592248>.

Download, cache, and manage social contact survey data from the social contact data community on Zenodo (<https://zenodo.org/communities/social_contact_data>) for use in infectious disease modelling. Provides functions to list available surveys, download survey files with automatic caching, and retrieve citations. Contact survey data describe who contacts whom in a population and are used to parameterise age-structured transmission models, for example via the socialmixr package. The surveys available include those from the POLYMOD study (Mossong et al. (2008) <doi:10.1371/journal.pmed.0050074>) and other social contact data shared on Zenodo.

Non-parametric test for equality of multivariate distributions. Trains a classifier to classify (multivariate) observations as coming from one of several distributions. If the classifier is able to classify the observations better than would be expected by chance (using permutation inference), then the null hypothesis that the distributions are equal is rejected.

This package provides a collection of data sets for teaching cluster analysis.

This package provides functions to analyze the spatial distribution of biodiversity, in particular categorical analysis of neo- and paleo-endemism (CANAPE) as described in Mishler et al (2014) <doi:10.1038/ncomms5473>. canaper conducts statistical tests to determine the types of endemism that occur in a study area while accounting for the evolutionary relationships of species.

This package implements algorithms for analyzing Cayley graphs of permutation groups, with a focus on the TopSpin puzzle and similar permutation-based combinatorial puzzles. Provides methods for cycle detection, state space exploration, bidirectional BFS pathfinding, and finding optimal operation sequences in permutation groups generated by shift and reverse operations. Includes C++ implementations of core operations via Rcpp for performance. Optional GPU acceleration via ggmlR Vulkan backend for batch distance calculations and parallel state transformations.

Computes confidence intervals for the positive predictive value (PPV) and negative predictive value (NPV) based on varied scenarios. In situations where the proportion of diseased subjects does not correspond to the disease prevalence (e.g. case-control studies), this package provides two types of solutions: 1) five methods for estimating confidence intervals for PPV and NPV via ratio of two binomial proportions including Gart & Nam (1988), Walter (1975), MOVER-J (Laud, 2017), Fieller (1954), and Bootstrap (Efron, 1979); 2) three direct methods that compute the confidence intervals including Pepe (2003), Zhou (2007), and Delta. In prospective studies where the proportion of diseased subjects is an unbiased estimate of the disease prevalence, this package provides several methods for calculating the confidence intervals for PPV and NPV including Clopper-Pearson, Wald, Wilson, Agresti-Coull, and Beta. See the Details and References sections in the corresponding functions.

Enables: (1) plotting two-dimensional confidence regions, (2) coverage analysis of confidence region simulations, (3) calculating confidence intervals and the associated actual coverage for binomial proportions, (4) calculating the support values and the probability mass function of the Kaplan-Meier product-limit estimator, and (5) plotting the actual coverage function associated with a confidence interval for the survivor function from a randomly right-censored data set. Each is given in greater detail next. (1) Plots the two-dimensional confidence region for probability distribution parameters (supported distribution suffixes: cauchy, gamma, invgauss, logis, llogis, lnorm, norm, unif, weibull) corresponding to a user-given complete or right-censored dataset and level of significance. The crplot() algorithm plots more points in areas of greater curvature to ensure a smooth appearance throughout the confidence region boundary. An alternative heuristic plots a specified number of points at roughly uniform intervals along its boundary. Both heuristics build upon the radial profile log-likelihood ratio technique for plotting confidence regions given by Jaeger (2016) <doi:10.1080/00031305.2016.1182946>, and are detailed in a publication by Weld et al. (2019) <doi:10.1080/00031305.2018.1564696>. (2) Performs confidence region coverage simulations for a random sample drawn from a user- specified parametric population distribution, or for a user-specified dataset and point of interest with coversim(). (3) Calculates confidence interval bounds for a binomial proportion with binomTest(), calculates the actual coverage with binomTestCoverage(), and plots the actual coverage with binomTestCoveragePlot(). Calculates confidence interval bounds for the binomial proportion using an ensemble of constituent confidence intervals with binomTestEnsemble(). Calculates confidence interval bounds for the binomial proportion using a complete enumeration of all possible transitions from one actual coverage acceptance curve to another which minimizes the root mean square error for n <= 15 and follows the transitions for well-known confidence intervals for n > 15 using binomTestMSE(). (4) The km.support() function calculates the support values of the Kaplan-Meier product-limit estimator for a given sample size n using an induction algorithm described in Qin et al. (2023) <doi:10.1080/00031305.2022.2070279>. The km.outcomes() function generates a matrix containing all possible outcomes (all possible sequences of failure times and right-censoring times) of the value of the Kaplan-Meier product-limit estimator for a particular sample size n. The km.pmf() function generates the probability mass function for the support values of the Kaplan-Meier product-limit estimator for a particular sample size n, probability of observing a failure h at the time of interest expressed as the cumulative probability percentile associated with X = min(T, C), where T is the failure time and C is the censoring time under a random-censoring scheme. The km.surv() function generates multiple probability mass functions of the Kaplan-Meier product-limit estimator for the same arguments as those given for km.pmf(). (5) The km.coverage() function plots the actual coverage function associated with a confidence interval for the survivor function from a randomly right-censored data set for one or more of the following confidence intervals: Greenwood, log-minus-log, Peto, arcsine, and exponential Greenwood. The actual coverage function is plotted for a small number of items on test, stated coverage, failure rate, and censoring rate. The km.coverage() function can print an optional table containing all possible failure/censoring orderings, along with their contribution to the actual coverage function.

Simulate species occurrence and abundances (counts) along gradients.

Wrangle country data more effectively and quickly. This package contains functions to easily identify and convert country names, download country information, merge country data from different sources, and make quick world maps.

Partitions data points (variables) into communities/clusters, similar to clustering algorithms such as k-means and hierarchical clustering. This package implements a clustering algorithm based on a new metric CORD, defined for high-dimensional parametric or semiparametric distributions. For more details see Bunea et al. (2020), Annals of Statistics <doi:10.1214/18-AOS1794>.