It names the R Markdown chunks of files based on the filename.

Calculate the precision in mean differences (raw or Cohen's D) and correlation coefficients for different sample sizes. Uses permutations of the collected functional magnetic resonance imaging (fMRI) region of interest data. Method described in Klapwijk, Jongerling, Hoijtink and Crone (2024) <doi:10.31234/osf.io/cz32t>.

This package provides utility functions and objects for Extreme Value Analysis. These include probability functions with their exact derivatives w.r.t. the parameters that can be used for estimation and inference, even with censored observations. The transformations exchanging the two parameterizations of Peaks Over Threshold (POT) models: Poisson-GP and Point-Process are also provided with their derivatives.

This R package provides a calculation of between-cases AUC estimate, corresponding covariance, and variance estimate in the nested data problem. Also, the package has the function to simulate the nested data. The calculated between-cases AUC estimate is used to evaluate the reader's diagnostic performance in clinical tasks with nested data. For more details on the above methods, please refer to the paper by H Du, S Wen, Y Guo, F Jin, BD Gallas (2022) <doi:10.1177/09622802221111539>.

This package provides a navigation menu to enable pipe-friendly data processing for hierarchical data structures. By activating the menu items, you can perform operations on each item while maintaining the overall structure in attributes.

This package performs nonparametric estimation in mixture cure models when the cure status is partially known. For details, see Safari et al (2021) <doi:10.1002/bimj.202100156>, Safari et al (2022) <doi:10.1177/09622802221115880> and Safari et al (2023) <doi:10.1007/s10985-023-09591-x>.

Computes the probability density function, cumulative distribution function, quantile function, random numbers and measures of inference for the following general families of distributions (each family defined in terms of an arbitrary cdf G): Marshall Olkin G distributions, exponentiated G distributions, beta G distributions, gamma G distributions, Kumaraswamy G distributions, generalized beta G distributions, beta extended G distributions, gamma G distributions, gamma uniform G distributions, beta exponential G distributions, Weibull G distributions, log gamma G I distributions, log gamma G II distributions, exponentiated generalized G distributions, exponentiated Kumaraswamy G distributions, geometric exponential Poisson G distributions, truncated-exponential skew-symmetric G distributions, modified beta G distributions, and exponentiated exponential Poisson G distributions.

This package implements network analysis and graph theory measures used in neuroscience, cognitive science, and psychology. Methods include various filtering methods and approaches such as threshold, dependency (Kenett, Tumminello, Madi, Gur-Gershgoren, Mantegna, & Ben-Jacob, 2010 <doi:10.1371/journal.pone.0015032>), Information Filtering Networks (Barfuss, Massara, Di Matteo, & Aste, 2016 <doi:10.1103/PhysRevE.94.062306>), and Efficiency-Cost Optimization (Fallani, Latora, & Chavez, 2017 <doi:10.1371/journal.pcbi.1005305>). Brain methods include the recently developed Connectome Predictive Modeling (see references in package). Also implements several network measures including local network characteristics (e.g., centrality), community-level network characteristics (e.g., community centrality), global network characteristics (e.g., clustering coefficient), and various other measures associated with the reliability and reproducibility of network analysis.

This package provides a set of techniques that can be used to develop, validate, and implement automated classifiers. A powerful tool for transforming raw data into meaningful information, ncodeR (Shaffer, D. W. (2017) Quantitative Ethnography. ISBN: 0578191687) is designed specifically for working with big data: large document collections, logfiles, and other text data.

This package provides functions for manipulating nested data frames in a list-column using dplyr <https://dplyr.tidyverse.org/> syntax. Rather than unnesting, then manipulating a data frame, nplyr allows users to manipulate each nested data frame directly. nplyr is a wrapper for dplyr functions that provide tools for common data manipulation steps: filtering rows, selecting columns, summarising grouped data, among others.

With this package, it is possible to compute nonparametric simultaneous confidence intervals for relative contrast effects in the unbalanced one way layout. Moreover, it computes simultaneous p-values. The simultaneous confidence intervals can be computed using multivariate normal distribution, multivariate t-distribution with a Satterthwaite Approximation of the degree of freedom or using multivariate range preserving transformations with Logit or Probit as transformation function. 2 sample comparisons can be performed with the same methods described above. There is no assumption on the underlying distribution function, only that the data have to be at least ordinal numbers. See Konietschke et al. (2015) <doi:10.18637/jss.v064.i09> for details.

In many binary classification applications, such as disease diagnosis and spam detection, practitioners commonly face the need to limit type I error (i.e., the conditional probability of misclassifying a class 0 observation as class 1) so that it remains below a desired threshold. To address this need, the Neyman-Pearson (NP) classification paradigm is a natural choice; it minimizes type II error (i.e., the conditional probability of misclassifying a class 1 observation as class 0) while enforcing an upper bound, alpha, on the type I error. Although the NP paradigm has a century-long history in hypothesis testing, it has not been well recognized and implemented in classification schemes. Common practices that directly limit the empirical type I error to no more than alpha do not satisfy the type I error control objective because the resulting classifiers are still likely to have type I errors much larger than alpha. As a result, the NP paradigm has not been properly implemented for many classification scenarios in practice. In this work, we develop the first umbrella algorithm that implements the NP paradigm for all scoring-type classification methods, including popular methods such as logistic regression, support vector machines and random forests. Powered by this umbrella algorithm, we propose a novel graphical tool for NP classification methods: NP receiver operating characteristic (NP-ROC) bands, motivated by the popular receiver operating characteristic (ROC) curves. NP-ROC bands will help choose in a data adaptive way and compare different NP classifiers.

Package for a Network assisted algorithm for Epigenetic studies using mean and variance Combined signals: NEpiC. NEpiC combines both signals in mean and variance differences in methylation level between case and control groups searching for differentially methylated sub-networks (modules) using the protein-protein interaction network.

Calculates the normalized mutual information (NMI) of two community structures in network analysis.

This package provides automated methods for generating initial parameter estimates in population pharmacokinetic modeling. The pipeline integrates adaptive single-point methods, naive pooled graphic approaches, noncompartmental analysis methods, and parameter sweeping across pharmacokinetic models. It estimates residual unexplained variability using either data-driven or fixed-fraction approaches and assigns pragmatic initial values for inter-individual variability. These strategies are designed to improve model robustness and convergence in nlmixr2 workflows. For more details see Huang Z, Fidler M, Lan M, Cheng IL, Kloprogge F, Standing JF (2025) <doi:10.1007/s10928-025-10000-z>.

Package including an interactive Shiny application for testing normality visually.

We developed a comprehensive tool that helps with visualization and analysis of networks with the same variables across multiple factor levels. The netShiny contains most of the popular network features such as centrality measures, modularity, and other summary statistics (e.g. clustering coefficient). It also contains known tools to look at the (dis)similarities between two networks, such as pairwise distance measures between networks, set operations on the nodes of the networks, distribution of the weights of the edges and a network representing the difference between two correlation matrices. The package netShiny also contains tools to perform bootstrapping and find clusters in networks. See the netShiny manual for more information, documentation and examples.

Nonparametric methods for analysis of covariance (ANCOVA) are distribution-free and provide a flexible statistical framework for situations where the assumptions of parametric ANCOVA are violated or when the response variable is ordinal. This package implements several well-known nonparametric ANCOVA procedures, including Quade, Puri and Sen, McSweeney and Porter, Burnett and Barr, Hettmansperger and McKean, Shirley, and Puri-Sen-Harwell-Serlin. The package provides user-friendly functions to apply these methods in practice. These methods are described in Olejnik et al. (1985) <doi:10.1177/0193841X8500900104> and Harwell et al. (1988) <doi:10.1037/0033-2909.104.2.268>.

Conducts Bayesian Hypothesis tests of a point null hypothesis against a two-sided alternative using Non-local Alternative Prior (NAP) for one- and two-sample z- and t-tests (Pramanik and Johnson, 2022). Under the alternative, the NAP is assumed on the standardized effects size in one-sample tests and on their differences in two-sample tests. The package considers two types of NAP densities: (1) the normal moment prior, and (2) the composite alternative. In fixed design tests, the functions calculate the Bayes factors and the expected weight of evidence for varied effect size and sample size. The package also provides a sequential testing framework using the Sequential Bayes Factor (SBF) design. The functions calculate the operating characteristics (OC) and the average sample number (ASN), and also conducts sequential tests for a sequentially observed data.

The purpose of this library is to to call different optimization solvers (such as Gonzalez Rodriguez et al. (2022) <doi:10.1007/s10898-022-01229-w>, Tawarmalani and Sahinidis (2005) <doi:10.1007/s10107-005-0581-8>, and Byrd et al. (2006) <doi:10.1007/0-387-30065-1_4>) to solve problems given by a standard nl file.

This package provides interface to the online basketball data resources such as Basketball reference API <https://www.basketball-reference.com/> and helps R users analyze basketball data.

Support the book: Wu CO and Tian X (2018). Nonparametric Models for Longitudinal Data. Chapman & Hall/CRC (to appear); and provide fit for using global and local smoothing methods for the conditional-mean and conditional-distribution based models with longitudinal Data.

This package provides a method for obtaining nonparametric estimates of regression models with or without factor-by-curve interactions using local polynomial kernel smoothers or splines. Additionally, a parametric model (allometric model) can be estimated.

Access and manipulation of data using the Neotoma Paleoecology Database. <https://api.neotomadb.org/api-docs/>. Examples in functions that require API access are not executed during CRAN checks. Vignettes do not execute as to avoid API calls during CRAN checks.