This package implements node subsampling methods for multivariate inference on network moments (rescaled motif counts), including: uniform node subsampling to approximate the joint distribution of multiple network moments (Algorithm 1); externally sparsified moments for density-matched comparisons (Algorithm 2); and a two-sample test for unmatchable networks with unequal edge densities via a split-and-sparsify subsampling procedure (Algorithm 3). Built-in support for V-shape (2-star), triangle, and 3-star motifs, with a user-extensible interface for arbitrary additional motifs. Parallel execution is supported via doParallel and foreach'. Based on Qi, Hua, Li and Zhou (2024) <doi:10.48550/arXiv.2409.01599>.

In semi-structured interviews that use the framework method, it is not always clear how refinements to interview questions affect the decision of when to stop interviews. The trend of novel and duplicate interview codes (novel codes are information that other interviewees have not previously mentioned) provides insight into the richness of qualitative information. This package provides tools to visualise when refinements occur and how that affects the trends of novel and duplicate codes. These visualisations, when used progressively as new interviews are finished, can help the researcher to decide on a stopping point for their interviews. For context, see Wong et al., (2023) <doi:10.1177/16094069231220773>.

This package provides utility functions to facilitate the use of R within nf-core modules. The package helps parse Nextflow inputs and perform validation checks to ensure correct parameter handling and reproducible execution. For more details see Ewels (2020) <doi:10.1038/s41587-020-0439-x>.

This package provides functions for nominal data mining based on bipartite graphs, which build a pipeline for analysis and missing values imputation. Methods are mainly from the paper: Jafari, Mohieddin, et al. (2021) <doi:10.1101/2021.03.18.436040>, some new ones are also included.

Fits sphere-sphere regression models by estimating locally weighted rotations. Simulation of sphere-sphere data according to non-rigid rotation models. Provides methods for bias reduction applying iterative procedures within a Newton-Raphson learning scheme. Cross-validation is exploited to select smoothing parameters. See Marco Di Marzio, Agnese Panzera & Charles C. Taylor (2018) <doi:10.1080/01621459.2017.1421542>.

This package provides a model library for nlmixr2'. The models include (and plan to include) pharmacokinetic, pharmacodynamic, and disease models used in pharmacometrics. Where applicable, references for each model are included in the meta-data for each individual model. The package also includes model composition and modification functions to make model updates easier.

This package provides tools for estimating Receiver Operating Characteristic (ROC) curves, building confidence bands, comparing several curves both for dependent and independent data, estimating the cumulative-dynamic ROC curve in presence of censored data, and performing meta-analysis studies, among others.

Network changepoint analysis for undirected network data. The package implements a hidden Markov network change point model (Park and Sohn (2020)). Functions for break number detection using the approximate marginal likelihood and WAIC are also provided. Version 1.1.0 includes high-performance C++ implementations via Rcpp'/'RcppArmadillo for 5-15x faster MCMC sampling, along with modern ggplot2'-based visualizations with colorblind-friendly palettes.

Vector AutoRegressive (VAR) type models with tailored regularisation structures are provided to uncover network type structures in the data, such as influential time series (influencers). Currently the package implements the LISAR model from Zhang and Trimborn (2023) <doi:10.2139/ssrn.4619531>. The package automatically derives the required regularisation sequences and refines it during the estimation to provide the optimal model. The package allows for model optimisation under various loss functions such as Mean Squared Forecasting Error (MSFE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). It provides a dedicated class, allowing for summary prints of the optimal model and a plotting function to conveniently analyse the optimal model via heatmaps.

This package provides a Bayesian approach to estimate the number of occurred-but-not-yet-reported cases from incomplete, time-stamped reporting data for disease outbreaks. NobBS learns the reporting delay distribution and the time evolution of the epidemic curve to produce smoothed nowcasts in both stable and time-varying case reporting settings, as described in McGough et al. (2020) <doi:10.1371/journal.pcbi.1007735>.

The aim of nosoi (pronounced no.si) is to provide a flexible agent-based stochastic transmission chain/epidemic simulator (Lequime et al. Methods in Ecology and Evolution 11:1002-1007). It is named after the daimones of plague, sickness and disease that escaped Pandora's jar in the Greek mythology. nosoi is able to take into account the influence of multiple variable on the transmission process (e.g. dual-host systems (such as arboviruses), within-host viral dynamics, transportation, population structure), alone or taken together, to create complex but relatively intuitive epidemiological simulations.

This package provides functions to flash your hue lights, or text yourself, from R. Designed to be used with long running scripts.

Non-negative Matrix Factorization.

Measure the dependence structure between two random variables with a new correlation coefficient and extend it to hypothesis test, feature screening and false discovery rate control.

This package provides a collection of network analytic (convenience) functions which are missing in other standard packages. This includes triad census with attributes <doi:10.1016/j.socnet.2019.04.003>, core-periphery models <doi:10.1016/S0378-8733(99)00019-2>, and several graph generators. Most functions are build upon igraph'.

Due to Rstudio's status as open source software, we believe it will be utilized frequently for future data analysis by users whom lack formal training or experience with R'. The NMVANOVA (Novice Model Variation ANOVA) a streamlined variation of experimental design functions that allows novice Rstudio users to perform different model variations one-way analysis of variance without downloading multiple libraries or packages. Users can easily manipulate the data block, and needed inputs so that users only have to plugin the four designed variables/values.

An extension of the Beta Kernel Process model designed to handle negative binomial responses for count data modeling, building upon Zhao, Qing and Xu (2025) <doi:10.48550/arXiv.2508.10447>.

Calculates a cumulative summation nonparametric extended median test based on the work of Brown & Schaffer (2020) <DOI:10.1080/03610926.2020.1738492>. It then generates a control chart to assess processes and determine if any streams are out of control.

Automatically runs 18 individual models and 14 ensembles on numeric data, for a total of 32 models. The package automatically returns complete results on all 32 models, 25 charts and six tables. The user simply provides the tidy data, and answers a few questions (for example, how many times would you like to resample the data). From there the package randomly splits the data into train, test and validation sets as the user requests (for example, train = 0.60, test = 0.20, validation = 0.20), fits each of models on the training data, makes predictions on the test and validation sets, measures root mean squared error (RMSE), removes features above a user-set level of Variance Inflation Factor, and has several optional features including scaling all numeric data, four different ways to handle strings in the data. Perhaps the most significant feature is the package's ability to make predictions using the 32 pre trained models on totally new (untrained) data if the user selects that feature. This feature alone represents a very effective solution to the issue of reproducibility of models in data science. The package can also randomly resample the data as many times as the user sets, thus giving more accurate results than a single run. The graphs provide many results that are not typically found. For example, the package automatically calculates the Kolmogorov-Smirnov test for each of the 32 models and plots a bar chart of the results, a bias bar chart of each of the 32 models, as well as several plots for exploratory data analysis (automatic histograms of the numeric data, automatic histograms of the numeric data). The package also automatically creates a summary report that can be both sorted and searched for each of the 32 models, including RMSE, bias, train RMSE, test RMSE, validation RMSE, overfitting and duration. The best results on the holdout data typically beat the best results in data science competitions and published results for the same data set.

This package provides tools for visual inference. Generate null data sets and null plots using permutation and simulation. Calculate distance metrics for a lineup, and examine the distributions of metrics.

Modelling the vegetation, carbon, nitrogen and water dynamics of undisturbed open bog ecosystems in a temperate to sub-boreal climate. The executable of the model can downloaded from <https://github.com/jeroenpullens/NUCOMBog>.

This package provides a finite-population significance test of the sharp causal null hypothesis that treatment exposure X has no effect on final outcome Y, within the principal stratum of Compliers. A generalized likelihood ratio test statistic is used, and the resulting p-value is exact. Currently, it is assumed that there are only Compliers and Never Takers in the population.

Utilities for unambiguous, neat and legible representation of data (date, time stamp, numbers, percentages and strings) for presentation of analysis , aiming for elegance and consistency. The purpose of this package is to format data, that is better for presentation and any automation jobs that reports numbers.

This package provides a bootstrap method for Respondent-Driven Sampling (RDS) that relies on the underlying structure of the RDS network to estimate uncertainty.