We design algorithms with linear time complexity with respect to the dimension for three commonly studied correlation structures, including exchangeable, decaying-product and K-dependent correlation structures, and extend the algorithms to generate binary data of general non-negative correlation matrices with quadratic time complexity. Jiang, W., Song, S., Hou, L. and Zhao, H. "A set of efficient methods to generate high-dimensional binary data with specified correlation structures." The American Statistician. See <doi:10.1080/00031305.2020.1816213> for a detailed presentation of the method.

This data package contains monthly climate data in Germany, it can be used for heating and cooling calculations (external temperature, heating / cooling days, solar radiation).

This package provides a systematic biology tool was developed to identify cell infiltration via Individualized Cell-Cell interaction network. CITMIC first constructed a weighted cell interaction network through integrating Cell-target interaction information, molecular function data from Gene Ontology (GO) database and gene transcriptomic data in specific sample, and then, it used a network propagation algorithm on the network to identify cell infiltration for the sample. Ultimately, cell infiltration in the patient dataset was obtained by normalizing the centrality scores of the cells.

Modeling the correlation transitions under specified distributional assumptions within the realm of discretization in the context of the latency and threshold concepts. The details of the method are explained in Demirtas, H. and Vardar-Acar, C. (2017) <DOI:10.1007/978-981-10-3307-0_4>.

Various statistical methods for survival analysis in comparing survival curves between two groups, including overall hypothesis tests described in Li et al. (2015) <doi:10.1371/journal.pone.0116774> and Huang et al. (2020) <doi:10.1080/03610918.2020.1753075>, fixed-point tests in Klein et al. (2007) <doi:10.1002/sim.2864>, short-term tests, and long-term tests in Logan et al. (2008) <doi:10.1111/j.1541-0420.2007.00975.x>. Some commonly used descriptive statistics and plots are also included.

This package performs Bayesian non-parametric calibration of multiple related radiocarbon determinations, and summarises the calendar age information to plot their joint calendar age density (see Heaton (2022) <doi:10.1111/rssc.12599>). Also models the occurrence of radiocarbon samples as a variable-rate (inhomogeneous) Poisson process, plotting the posterior estimate for the occurrence rate of the samples over calendar time, and providing information about potential change points.

Accelerate the process from clinical data to medical publication, including clinical data cleaning, significant result screening, and the generation of publish-ready tables and figures.

Find the numbers of test tubes that can be balanced in centrifuge rotors and show various ways to load them. Refer to Pham (2020) <doi:10.31224/osf.io/4xs38> for more information on package functionality.

Providing a set of functions to easily generate and iterate complex networks. The functions can be used to generate realistic networks with a wide range of different clustering, density, and average path length. For more information consult research articles by Amiyaal Ilany and Erol Akcay (2016) <doi:10.1093/icb/icw068> and Ilany and Erol Akcay (2016) <doi:10.1101/026120>, which have inspired many methods in this package.

Offers tools to estimate the climate representativeness of reference polygons and quantifies its transformation under future climate change scenarios. Approaches described in Mingarro and Lobo (2018) <doi:10.32800/abc.2018.41.0333> and Mingarro and Lobo (2022) <doi:10.1017/S037689292100014X>.

This package creates project specific directory and file templates that are written to a .Rprofile file. Upon starting a new R session, these templates can be used to streamline the creation of new directories that are standardized to the user's preferences and can include the initiation of a git repository, an RStudio R project, and project-local dependency management with the renv package.

This package implements the combined cluster and discriminant analysis method for finding homogeneous groups of data with known origin as described in Kovacs et. al (2014): Classification into homogeneous groups using combined cluster and discriminant analysis (CCDA). Environmental Modelling & Software. <doi:10.1016/j.envsoft.2014.01.010>.

It is designed to streamline the process of calculating complete annual growth rates with user-friendly functions and robust algorithms. It enables researchers and analysts to effortlessly generate precise growth rate estimates for their data. For method details see, Sharma, M.K.(2013) <https://www.indianjournals.com/ijor.aspx?target=ijor:jfl&volume=26&issue=1and2&article=018>. It offers a comprehensive suite of functions and customisable parameters. Equipped to handle varying complexities in data structures. It empowers users to uncover insightful growth dynamics and make informed decisions.

Data analysis often requires coding, especially when data are collected through interviews, observations, or questionnaires. As a result, code counting and data preparation are essential steps in the analysis process. Analysts may need to count the codes in a text (Tokenization, counting of pre-established codes, computing the co-occurrence matrix by line) and prepare the data (e.g., min-max normalization, Z-score, robust scaling, Box-Cox transformation, and non-parametric bootstrap). For the Box-Cox transformation (Box & Cox, 1964, <https://www.jstor.org/stable/2984418>), the optimal Lambda is determined using the log-likelihood method. Non-parametric bootstrap involves randomly sampling data with replacement. Two random number generators are also integrated: a Lehmer congruential generator for uniform distribution and a Box-Muller generator for normal distribution. Package for educational purposes.

Allows users to input their data, segmentation and function used for the segmentation (and additional arguments) and the package calculates the influence of the data on the changepoint locations, see Wilms et al. (2022) <doi:10.1080/10618600.2021.2000873>. Currently this can only be used with the changepoint package functions to identify changes, but we plan to extend this. There are options for different types of graphics to assess the influence.

This package provides a comprehensive API for colour conversion between popular colour spaces ('RGB', HSL', OKLab', OKLch', hex', and named colours) along with clean, modern CSS Color Level 4 syntax output. Integrates seamlessly into Shiny and Quarto workflows. Includes nearest colour name lookup powered by a curated database of over 30,000 colour names. OKLab'/'OKLCh colour spaces are described in Ottosson (2020) <https://bottosson.github.io/posts/oklab/>. CSS Color Level 4 syntax follows the W3C specification <https://www.w3.org/TR/css-color-4/>.

Modeling associations between covariates and power spectra of replicated time series using a cepstral-based semiparametric framework. Implements a fast two-stage estimation procedure via Whittle likelihood and multivariate regression.The methodology is based on Li and Dong (2025) <doi:10.1080/10618600.2025.2473936>.

This package provides functions for visualizing, animating, solving and analyzing the Rubik's cube. Includes data structures for solvable and unsolvable cubes, random moves and random state scrambles and cubes, 3D displays and animations using OpenGL', patterned cube generation, and lightweight solvers. See Rokicki, T. (2008) <arXiv:0803.3435> for the Kociemba solver.

An implementation of the statistical methods commonly used for advanced composite materials in aerospace applications. This package focuses on calculating basis values (lower tolerance bounds) for material strength properties, as well as performing the associated diagnostic tests. This package provides functions for calculating basis values assuming several different distributions, as well as providing functions for non-parametric methods of computing basis values. Functions are also provided for testing the hypothesis that there is no difference between strength and modulus data from an alternate sample and that from a "qualification" or "baseline" sample. For a discussion of these statistical methods and their use, see the Composite Materials Handbook, Volume 1 (2012, ISBN: 978-0-7680-7811-4). Additional details about this package are available in the paper by Kloppenborg (2020, <doi:10.21105/joss.02265>).

Provide step by step guided tours of Shiny applications.

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>.

Calculates pointwise confidence intervals for the cumulative distribution function of the event time for current status data, data where each individual is assessed at one time to see if they had the event or not by the assessment time.

This package provides a system containing easy-to-use tools to compute the bioequivalence assessment in the univariate framework using the methods proposed in Boulaguiem et al. (2023) <doi:10.1101/2023.03.11.532179>.

This model fitting tool incorporates cyclic coordinate descent and majorization-minimization approaches to fit a variety of regression models found in large-scale observational healthcare data. Implementations focus on computational optimization and fine-scale parallelization to yield efficient inference in massive datasets. Please see: Suchard, Simpson, Zorych, Ryan and Madigan (2013) <doi:10.1145/2414416.2414791>.