Writing an R value as JSON that a human can read, and reading it back unchanged. The jsonlite package offers either a readable but lossy pair of functions or a faithful but verbose one; this package emits ordinary JSON for ordinary values and annotates only what JSON cannot express, namely the distinction between integer and double, typed missing values, non-finite numbers, attributes, and objects from the S3, S4 and S7 systems.

In repeated measures studies with extreme large or small values it is common that the subjects measurements on average are closer to the mean of the basic population. Interpreting possible changes in the mean in such situations can lead to biased results since the values were not randomly selected, they come from truncated sampling. This method allows to estimate the range of means where treatment effects are likely to occur when regression toward the mean is present. Ostermann, T., Willich, Stefan N. & Luedtke, Rainer. (2008). Regression toward the mean - a detection method for unknown population mean based on Mee and Chua's algorithm. BMC Medical Research Methodology.<doi:10.1186/1471-2288-8-52>. Acknowledgments: We would like to acknowledge "Lena Roth" and "Nico Steckhan" for the package's initial updates (Q3 2024) and continued supervision and guidance. Both have contributed to discussing and integrating these methods into the package, ensuring they are up-to-date and contextually relevant.

GladiaTOX R package is an open-source, flexible solution to high-content screening data processing and reporting in biomedical research. GladiaTOX takes advantage of the tcpl core functionalities and provides a number of extensions: it provides a web-service solution to fetch raw data; it computes severity scores and exports ToxPi formatted files; furthermore it contains a suite of functionalities to generate pdf reports for quality control and data processing.

An implementation, which takes input data and makes it available for proper batch effect removal by ComBat or Limma. The implementation appropriately handles missing values by dissecting the input matrix into smaller matrices with sufficient data to feed the ComBat or limma algorithm. The adjusted data is returned to the user as a rebuild matrix. The implementation is meant to make as much data available as possible with minimal data loss.

timeOmics is a generic data-driven framework to integrate multi-Omics longitudinal data measured on the same biological samples and select key temporal features with strong associations within the same sample group. The main steps of timeOmics are: 1. Plaform and time-specific normalization and filtering steps; 2. Modelling each biological into one time expression profile; 3. Clustering features with the same expression profile over time; 4. Post-hoc validation step.

This package provides a collection of functions allowing to derive the posterior distribution of the model parameters in random-effects meta-analysis or meta-regression, and providing functionality to evaluate joint and marginal posterior probability distributions, predictive distributions, shrinkage effects, posterior predictive p-values, etc.; For more details, see also Roever C (2020) <doi:10.18637/jss.v093.i06>, or Roever C and Friede T (2022) <doi:10.1016/j.cmpb.2022.107303>.

An algorithm developed to efficiently and accurately process complex and variable cardiac data with three key features: 1. employing autocorrelation to identify recurrent heartbeats and use their periods to compute heart rates; 2. incorporating a genetic algorithm framework to minimize data loss due to noise interference and accommodate within-sequence variations; and 3. introducing a tracking index as a moving reference to reduce errors. Lau, Wong, & Gu (2026) <https://ssrn.com/abstract=5153081>.

This package provides a method for determining groups in multiple curves with an automatic selection of their number based on k-means or k-medians algorithms. The selection of the optimal number is provided by bootstrap methods or other approaches with lower computational cost. The methodology can be applied both in regression and survival framework. Implemented methods are: Grouping multiple survival curves described by Villanueva et al. (2018) <doi:10.1002/sim.8016>.

We provide a list of functions for replicating the results of the Monte Carlo simulations and empirical application of Jiang et al. (2022). In particular, we provide corresponding functions for generating the three types of random data described in this paper, as well as all the estimation strategies. Detailed information about the data generation process and estimation strategy can be found in Jiang et al. (2022) <doi:10.48550/arXiv.2201.13004>.

This package provides functions to quantify animal dominance hierarchies. The major focus is on Elo rating and its ability to deal with temporal dynamics in dominance interaction sequences. For static data, David's score and de Vries I&SI are also implemented. In addition, the package provides functions to assess transitivity, linearity and stability of dominance networks. See Neumann et al (2011) <doi:10.1016/j.anbehav.2011.07.016> for an introduction.

This package provides methods for distance-based fuzzy unsupervised and semi-supervised clustering, including fuzzy and possibilistic models based on alternating optimization (AO) algorithm. The package introduces a vectorized estimation framework for prototype-based fuzzy clustering algorithms, enabling modular algorithm design and extensibility. It also supports storage and retrieval of intermediate AO optimization results for downstream analysis and processing. For more details see Kmita et al. (2024) <doi:10.1109/TFUZZ.2024.3370768>.

Interface for accessing the Fortnite Ecosystem API, allowing users to retrieve island metadata and engagement metrics. The package provides functions to search for Fortnite Creative islands, retrieve detailed metadata about specific islands including titles, descriptions, and tags, and access engagement metrics such as daily active users and play duration. It supports pagination for large result sets and time-series analysis of island performance. The API endpoint is <https://api.fortnite.com/ecosystem/v1>.

The basic idea of this package is provides some tools to help the researcher to work with geostatistics. Initially, we present a collection of functions that allow the researchers to deal with spatial data using bootstrap procedure. There are five methods available and two ways to display them: bootstrap confidence interval - provides a two-sided bootstrap confidence interval; bootstrap plot - a graphic with the original variogram and each of the B bootstrap variograms.

This package provides functions to conduct robust inference in difference-in-differences and event study designs by implementing the methods developed in Rambachan & Roth (2023) <doi:10.1093/restud/rdad018>, "A More Credible Approach to Parallel Trends" [Previously titled "An Honest Approach..."]. Inference is conducted under a weaker version of the parallel trends assumption. Uniformly valid confidence sets are constructed based upon conditional confidence sets, fixed-length confidence sets and hybridized confidence sets.

This package provides functions and classes to compute, handle and visualise incidence from dated events for a defined time interval. Dates can be provided in various standard formats. The class incidence is used to store computed incidence and can be easily manipulated, subsetted, and plotted. In addition, log-linear models can be fitted to incidence objects using fit'. This package is part of the RECON (<https://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.

Utility functions for mutational signature analysis as described in Alexandrov, L. B. (2020) <doi:10.1038/s41586-020-1943-3>. This package provides two groups of functions. One is for dealing with mutational signature "exposures" (i.e. the counts of mutations in a sample that are due to each mutational signature). The other group of functions is for matching or comparing sets of mutational signatures. mSigTools stands for mutational Signature analysis Tools.

Enhanced RTF wrapper written in R for use with existing R tables packages such as Huxtable or GT'. This package fills a gap where tables in certain packages can be written out to RTF, but cannot add certain metadata or features to the document that are required/expected in a report for a regulatory submission, such as multiple levels of titles and footnotes, making the document landscape, and controlling properties such as margins.

Psychometric mixture models based on flexmix infrastructure. At the moment Rasch mixture models with different parameterizations of the score distribution (saturated vs. mean/variance specification), Bradley-Terry mixture models, and MPT mixture models are implemented. These mixture models can be estimated with or without concomitant variables. See Frick et al. (2012) <doi:10.18637/jss.v048.i07> and Frick et al. (2015) <doi:10.1177/0013164414536183> for details on the Rasch mixture models.

The steepness package computes steepness as a property of dominance hierarchies. Steepness is defined as the absolute slope of the straight line fitted to the normalized David's scores. The normalized David's scores can be obtained on the basis of dyadic dominance indices corrected for chance or by means of proportions of wins. Given an observed sociomatrix, it computes hierarchy's steepness and estimates statistical significance by means of a randomization test.

Implementation of the BLEU-Score in C++ to evaluate the quality of generated text. The BLEU-Score, introduced by Papineni et al. (2002) <doi:10.3115/1073083.1073135>, is a metric for evaluating the quality of generated text. It is based on the n-gram overlap between the generated text and reference texts. Additionally, the package provides some smoothing methods as described in Chen and Cherry (2014) <doi:10.3115/v1/W14-3346>.

Generate synthetic time series from commonly used statistical models, including linear, nonlinear and chaotic systems. Applications to testing methods can be found in Jiang, Z., Sharma, A., & Johnson, F. (2019) <doi:10.1016/j.advwatres.2019.103430> and Jiang, Z., Sharma, A., & Johnson, F. (2020) <doi:10.1029/2019WR026962> associated with an open-source tool by Jiang, Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020) <doi:10.1016/j.envsoft.2020.104907>.

Obtain parameters of Svensson's Method, including percentage agreement, systematic change and individual change. Also, the contingency table can be generated. Svensson's Method is a rank-invariant nonparametric method for the analysis of ordered scales which measures the level of change both from systematic and individual aspects. For the details, please refer to Svensson E. Analysis of systematic and random differences between paired ordinal categorical data [dissertation]. Stockholm: Almqvist & Wiksell International; 1993.

This package provides functions for preparing and analyzing animal tracking data, with the intention of identifying areas which are potentially important at the population level and therefore of conservation interest. Areas identified using this package may be checked against global or regionally-defined criteria, such as those set by the Key Biodiversity Area program. The method published herein is described in full in Beal et al. 2021 <doi:10.1111/2041-210X.13713>.

ATAC-seq, an assay for Transposase-Accessible Chromatin using sequencing, is a rapid and sensitive method for chromatin accessibility analysis. It was developed as an alternative method to MNase-seq, FAIRE-seq and DNAse-seq. The ATACseqQC package was developed to help users to quickly assess whether their ATAC-seq experiment is successful. It includes diagnostic plots of fragment size distribution, proportion of mitochondria reads, nucleosome positioning pattern, and CTCF or other Transcript Factor footprints.