Implement an interval censor method to break ties when using data with ties to fitting a bivariate copula.

Clique percolation community detection for weighted and unweighted networks as well as threshold and plotting functions. For more information see Farkas et al. (2007) <doi:10.1088/1367-2630/9/6/180> and Palla et al. (2005) <doi:10.1038/nature03607>.

Convolute probabilistic distributions using the random generator function of each distribution. A new random number generator function is created that perform the mathematical operation on the individual random samples from the random generator function of each distribution. See the documentation for examples.

Analysis of network community objects with applications to neuroimaging data. There are two main components to this package. The first is the hierarchical multimodal spinglass (HMS) algorithm, which is a novel community detection algorithm specifically tailored to the unique issues within brain connectivity. The other is a suite of semiparametric kernel machine methods that allow for statistical inference to be performed to test for potential associations between these community structures and an outcome of interest (binary or continuous).

Create interactive charts with the C3.js <http://c3js.org/> charting library. All plot types in C3.js are available and include line, bar, scatter, and mixed geometry plots. Plot annotations, labels and axis are highly adjustable. Interactive web based charts can be embedded in R Markdown documents or Shiny web applications.

It uses the first-order sensitivity index to measure whether the weights assigned by the creator of the composite indicator match the actual importance of the variables. Moreover, the variance inflation factor is used to reduce the set of correlated variables. In the case of a discrepancy between the importance and the assigned weight, the script determines weights that allow adjustment of the weights to the intended impact of variables. If the optimised weights are unable to reflect the desired importance, the highly correlated variables are reduced, taking into account variance inflation factor. The final outcome of the script is the calculated value of the composite indicator based on optimal weights and a reduced set of variables, and the linear ordering of the analysed objects.

The currentSurvival package contains functions for the estimation of the current cumulative incidence (CCI) and the current leukaemia-free survival (CLFS). The CCI is the probability that a patient is alive and in any disease remission (e.g. complete cytogenetic remission in chronic myeloid leukaemia) after initiating his or her therapy (e.g. tyrosine kinase therapy for chronic myeloid leukaemia). The CLFS is the probability that a patient is alive and in any disease remission after achieving the first disease remission.

Categorize links and nodes from multiple networks in 3 categories: Common links (alpha) specific links (gamma), and different links (beta). Also categorizes the links into sub-categories and groups. The package includes a visualization tool for the networks. More information about the methodology can be found at: Gysi et. al., 2018 <arXiv:1802.00828>.

This package contains tools for working with data during statistical analysis, promoting flexible, intuitive, and reproducible workflows. There are functions designated for specific statistical tasks such building a custom univariate descriptive table, computing pairwise association statistics, etc. These are built on a collection of data manipulation tools designed for general use that are motivated by the functional programming concept.

Classification of climate according to Koeppen - Geiger, of aridity indices, of continentality indices, of water balance after Thornthwaite, of viticultural bioclimatic indices. Drawing climographs: Thornthwaite, Peguy, Bagnouls-Gaussen.

Includes binning categorical variables into lesser number of categories based on t-test, converting categorical variables into continuous features using the mean of the response variable for the respective categories, understanding the relationship between the response variable and predictor variables using data transformations.

Implementations of threshold regression approaches for linear regression models with a covariate subject to random censoring, including deletion threshold regression and completion threshold regression. Reverse survival regression, which flip the role of response variable and the covariate, is also considered.

Recalibrate risk scores (predicting binary outcomes) to improve clinical utility of risk score using weighted logistic or constrained logistic recalibration methods. Additionally, produces plots to assess the potential for recalibration to improve the clinical utility of a risk model. Methods are described in detail in Mishra, A. (2019) "Methods for Risk Markers that Incorporate Clinical Utility" <http://hdl.handle.net/1773/44068>.

Implementation of transductive conformal prediction (see Vovk, 2013, <doi:10.1007/978-3-642-41142-7_36>) and inductive conformal prediction (see Balasubramanian et al., 2014, ISBN:9780124017153) for classification problems.

This package implements the framework introduced in Di Francesco and Mellace (2025) <doi:10.48550/arXiv.2502.11691>, shifting the focus to well-defined and interpretable estimands that quantify how treatment affects the probability distribution over outcome categories. It supports selection-on-observables, instrumental variables, regression discontinuity, and difference-in-differences designs.

This package provides a programmatic interface to the Chromosome Counts Database (<https://ccdb.tau.ac.il/>), Rice et al. (2014) <doi:10.1111/nph.13191>. This package is part of the ROpenSci suite (<https://ropensci.org>).

Calculate the colocalization index, NSInC, in two different ways as described in the paper (Liu et al., 2019. Manuscript submitted for publication.) for multiple-species spatial data which contain the precise locations and membership of each spatial point. The two main functions are nsinc.d() and nsinc.z(). They provide the Pearsonâ s correlation coefficients of signal proportions in different memberships within a concerned proximity of every signal (or every base signal if single direction colocalization is considered) across all (base) signals using two different ways of normalization. The proximity sizes could be an individual value or a range of values, where the default ranges of values are different for the two functions.

This package provides functions for evaluating and visualizing predictive model performance (specifically: binary classifiers) in the field of customer scoring. These metrics include lift, lift index, gain percentage, top-decile lift, F1-score, expected misclassification cost and absolute misclassification cost. See Berry & Linoff (2004, ISBN:0-471-47064-3), Witten and Frank (2005, 0-12-088407-0) and Blattberg, Kim & Neslin (2008, ISBN:978â 0â 387â 72578â 9) for details. Visualization functions are included for lift charts and gain percentage charts. All metrics that require class predictions offer the possibility to dynamically determine cutoff values for transforming real-valued probability predictions into class predictions.

Plot confidence interval from the objects of statistical tests such as t.test(), var.test(), cor.test(), prop.test() and fisher.test() ('htest class), Tukey test [TukeyHSD()], Dunnett test [glht() in multcomp package], logistic regression [glm()], and Tukey or Games-Howell test [posthocTGH() in userfriendlyscience package]. Users are able to set the styles of lines and points. This package contains the function to calculate odds ratios and their confidence intervals from the result of logistic regression.

Read and manipulate Camera Trap Data Packages ('Camtrap DP'). Camtrap DP (<https://camtrap-dp.tdwg.org>) is a data exchange format for camera trap data. With camtrapdp you can read, filter and transform data (including to Darwin Core) before further analysis in e.g. camtraptor or camtrapR'.

Estimation, testing and regression modeling of subdistribution functions in competing risks using quantile regressions, as described in Peng and Fine (2009) <DOI:10.1198/jasa.2009.tm08228>.

This package provides tools for penalized estimation of flexible hidden Markov models for time series of counts w/o the need to specify a (parametric) family of distributions. These include functions for model fitting, model checking, and state decoding. For details, see Adam, T., Langrock, R., and Weià , C.H. (2019): Penalized Estimation of Flexible Hidden Markov Models for Time Series of Counts. <arXiv:1901.03275>.

This package provides tools for estimation and clustering of spherical data, seamlessly integrated with the flexmix package. Includes the necessary M-step implementations for both Poisson Kernel-Based Distribution (PKBD) and spherical Cauchy distribution. Additionally, the package provides random number generators for PKBD and spherical Cauchy distribution. Methods are based on Golzy M., Markatou M. (2020) <doi:10.1080/10618600.2020.1740713>, Kato S., McCullagh P. (2020) <doi:10.3150/20-bej1222> and Sablica L., Hornik K., Leydold J. (2023) <doi:10.1214/23-ejs2149>.

Evaluation of the Carlson elliptic integrals and the incomplete elliptic integrals with complex arguments. The implementations use Carlson's algorithms <doi:10.1007/BF02198293>. Applications of elliptic integrals include probability distributions, geometry, physics, mechanics, electrodynamics, statistical mechanics, astronomy, geodesy, geodesics on conics, and magnetic field calculations.