Designed to create interactive and visually compelling network maps using R Shiny. It allows users to quickly analyze CSV files and visualize complex relationships, structures, and connections within data by leveraging powerful network analysis libraries and dynamic web interfaces.

Updating the now 10-year-old nycflights13 data package. It contains information about all flights that departed from the three main New York City airports in 2023 and metadata on airlines, airports, weather, and planes.

This package provides several novel exact hypothesis tests with minimal assumptions on the errors. The tests are exact, meaning that their p-values are correct for the given sample sizes (the p-values are not derived from asymptotic analysis). The test for stochastic inequality is for ordinal comparisons based on two independent samples and requires no assumptions on the errors. The other tests include tests for the mean and variance of a single sample and comparing means in independent samples. All these tests only require that the data has known bounds (such as percentages that lie in [0,100]. These bounds are part of the input.

This package provides tools to create time series and geometry NetCDF files.

This package provides a set of functions to scrape and analyze rugby data. Supports competitions including the National Rugby League, New South Wales Cup, Queensland Cup, Super League, and various representative and women's competitions. Includes functions to fetch player statistics, match results, ladders, venues, and coaching data. Designed to assist analysts, fans, and researchers in exploring historical and current rugby league data. See Woods et al. (2017) <doi:10.1123/ijspp.2016-0187> for an example of rugby league performance analysis methodology.

Implementation of discriminant analysis with network structures in predictors accommodated to do classification and prediction.

This package provides tools for importing, harmonising, cleaning, analysing, benchmarking and visualising National Health Service (NHS) England Cancer Waiting Times data. The package supports national performance monitoring, provider-level benchmarking and cancer pathway comparisons through key performance indicator summaries, provider filtering, clustering analyses, mixed-effects regression models, variance decomposition, intraclass correlation coefficient estimation, adjusted provider performance estimation and sensitivity analyses. Functions are included for exploratory analysis, publication-ready visualisations and spreadsheet exports, supporting reproducible health services research, cancer services evaluation, quality improvement and assessment of waiting-time performance across healthcare organisations. Mixed-effects modelling functionality is based on Bates et al. (2015) <doi:10.18637/jss.v067.i01>. Multilevel modelling concepts and variance decomposition follow Gelman and Hill (2007, ISBN:9780521686891). Cancer Waiting Times definitions and reporting standards follow NHS England <https://www.england.nhs.uk/statistics/statistical-work-areas/cancer-waiting-times/>.

This package provides functions to produce advanced ascii graphics, directly to the terminal window. This package utilizes the txtplot() function from the txtplot package, to produce text-based histograms, empirical cumulative distribution function plots, scatterplots with fitted and regression lines, quantile plots, density plots, image plots, and contour plots.

Variational Expectation-Maximization algorithm to fit the noisy stochastic block model to an observed dense graph and to perform a node clustering. Moreover, a graph inference procedure to recover the underlying binary graph. This procedure comes with a control of the false discovery rate. The method is described in the article "Powerful graph inference with false discovery rate control" by T. Rebafka, E. Roquain, F. Villers (2020) <arXiv:1907.10176>.

Fit univariate non-linear scale mixture of skew-normal(NL-SMSN) regression, details in Garay, Lachos and Abanto-Valle (2011) <doi:10.1016/j.jkss.2010.08.003> and Lachos, Bandyopadhyay and Garay (2011) <doi:10.1016/j.spl.2011.03.019>.

This package provides routines to compute normalised prediction distribution errors, a metric designed to evaluate non-linear mixed effect models such as those used in pharmacokinetics and pharmacodynamics.

The ntfy (pronounce: notify) service is a simple HTTP-based pub-sub notification service. It allows you to send notifications to your phone or desktop via scripts from any computer, entirely without signup, cost or setup. It's also open source if you want to run your own. Visit <https://ntfy.sh> for more details.

Converts number spellings into their equivalent numbers. Supports numbers written in English, French, or Spanish.

Neighbour-balanced designs ensure that no treatment is disadvantaged unfairly by its surroundings. The treatment allocation in these designs is such that every treatment appears equally often as a neighbour with every other treatment. Neighbour Balanced Designs are employed when there is a possibility of neighbour effects from treatments used in adjacent experimental units. In the literature, a vast number of such designs have been developed. This package generates some efficient neighbour balanced block designs which are balanced and partially variance balanced for estimating the contrast pertaining to direct and neighbour effects, as well as provides a function for analysing the data obtained from such trials (Azais, J.M., Bailey, R.A. and Monod, H. (1993). "A catalogue of efficient neighbour designs with border plots". Biometrics, 49, 1252-1261 ; Tomar, J. S., Jaggi, Seema and Varghese, Cini (2005)<DOI: 10.1080/0266476042000305177>. "On totally balanced block designs for competition effects"). This package contains functions named nbbd1(),nbbd2(),nbbd3(),pnbbd1() and pnbbd2() which generates neighbour balanced block designs within a specified range of number of treatment (v). It contains another function named anlys()for performing the analysis of data generated from such trials.

This package performs nonparametric estimation in mixture cure models, and significance tests for the cure probability. For details, see López-Cheda et al. (2017a) <doi:10.1016/j.csda.2016.08.002> and López-Cheda et al. (2017b) <doi:10.1007/s11749-016-0515-1>.

Omics data come in different forms: gene expression, methylation, copy number, protein measurements and more. NCutYX allows clustering of variables, of samples, and both variables and samples (biclustering), while incorporating the dependencies across multiple types of Omics data. (SJ Teran Hidalgo et al (2017), <doi:10.1186/s12864-017-3990-1>).

This package provides functions for normalizing psychometric test scores. The normalization aims at correcting the metrological properties of the psychometric tests such as the ceiling and floor effects and the curvilinearity (unequal interval scaling). Functions to compute and plot predictions in the natural scale of the psychometric test from the estimates of a linear mixed model estimated on the normalized scores are also provided. See Philipps et al (2014) <doi:10.1159/000365637> for details.

Includes five particle filtering algorithms for use with state space models in the nimble system: Auxiliary', Bootstrap', Ensemble Kalman filter', Iterated Filtering 2', and Liu-West', as described in Michaud et al. (2021), <doi:10.18637/jss.v100.i03>. A full User Manual is available at <https://r-nimble.org>.

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.

An estimation procedure for the analysis of nonparametric proportional hazards model (e.g. h(t) = h0(t)exp(b(t)'Z)), providing estimation of b(t) and its pointwise standard errors, and semiparametric proportional hazards model (e.g. h(t) = h0(t)exp(b(t)'Z1 + c*Z2)), providing estimation of b(t), c and their standard errors. More details can be found in Lu Tian et al. (2005) <doi:10.1198/016214504000000845>.

Next-Generation Clustered Heat Maps (NG-CHMs) allow for dynamic exploration of heat map data in a web browser. NGCHM allows users to create both stand-alone HTML files containing a Next-Generation Clustered Heat Map, and .ngchm files to view in the NG-CHM viewer. See Ryan MC, Stucky M, et al (2020) <doi:10.12688/f1000research.20590.2> for more details.

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

Uses a modified lifting algorithm on which it builds the nondecimated lifting transform. It has applications in wavelet shrinkage.

Extracts team records/schedules and player statistics for the 2020-2025 National Collegiate Athletic Association (NCAA) women's and men's divisions I, II, and III volleyball teams from <https://stats.ncaa.org>. Functions can aggregate statistics for teams, conferences, divisions, or custom groups of teams.