Permutation Distribution Clustering is a clustering method for time series. Dissimilarity of time series is formalized as the divergence between their permutation distributions. The permutation distribution was proposed as measure of the complexity of a time series.

Calculates, via simulation, power and appropriate stopping alpha boundaries (and/or futility bounds) for sequential analyses (i.e., group sequential design) as well as for multiple hypotheses (multiple tests included in an analysis), given any specified global error rate. This enables the sequential use of practically any significance test, as long as the underlying data can be simulated in advance to a reasonable approximation. Lukács (2022) <doi:10.21105/joss.04643>.

Support for parallel computation with progress bar, and option to stop or proceed on errors. Also provides logging to console and disk, and the logging persists in the parallel threads. Additional functions support function call automation with delayed execution (e.g. for executing functions in parallel).

Easy function for text-mining the PubMed repository based on defined sets of terms. The relationship between fix-terms (related to your research topic) and pub-terms (terms which pivot around your research focus) is calculated using the pointwise mutual information algorithm ('PMI'). Church, Kenneth Ward and Hanks, Patrick (1990) <https://www.aclweb.org/anthology/J90-1003/> A text file is generated with the PMI'-scores for each fix-term. Then for each collocation pairs (a fix-term + a pub-term), a text file is generated with related article titles and publishing years. Additional Author section will follow in the next version updates.

This R package allows the determination of some distributions of the voters power when passing laws in weighted voting situations.

This package provides a framework for analysing state sequences with probabilistic suffix trees (PST), the construction that stores variable length Markov chains (VLMC). Besides functions for learning and optimizing VLMC models, the PST library includes many additional tools to analyse sequence data with these models: visualization tools, functions for sequence prediction and artificial sequences generation, as well as for context and pattern mining. The package is specifically adapted to the field of social sciences by allowing to learn VLMC models from sets of individual sequences possibly containing missing values, and by accounting for case weights. The library also allows to compute probabilistic divergence between two models, and to fit segmented VLMC, where sub-models fitted to distinct strata of the learning sample are stored in a single PST. This software results from research work executed within the framework of the Swiss National Centre of Competence in Research LIVES, which is financed by the Swiss National Science Foundation. The authors are grateful to the Swiss National Science Foundation for its financial support.

Simulate pedigree, genetic merits and phenotypes with random/non-random matings followed by random/non-random selection with different intensities and patterns in males and females. Genotypes can be simulated for a given pedigree, or an appended pedigree to an existing pedigree with genotypes. Mrode, R. A. (2005) <ISBN:9780851989969, 0851989969>; Nilforooshan, M.A. (2022) <doi:10.37496/rbz5120210131>.

Create hexagonal shape sticker image. polaroid can be used in user's web browser. polaroid can be used in shinyapps.io'. In both way, user can download created hexSticker as PNG image. polaroid is built based on argonDash', colourpicker and hexSticker R package.

Miscellaneous printing of numeric or statistical results in R Markdown or Quarto documents according to guidelines of the "Publication Manual" of the American Psychological Association (2020, ISBN: 978-1-4338-3215-4). These guidelines are usually referred to as APA style (<https://apastyle.apa.org/>) and include specific rules on the formatting of numbers and statistical test results. APA style has to be implemented when submitting scientific reports in a wide range of research fields, especially in the social sciences. The default output of numbers in the R console or R Markdown and Quarto documents does not meet the APA style requirements, and reformatting results manually can be cumbersome and error-prone. This package covers the automatic conversion of R objects to textual representations that meet the APA style requirements, which can be included in R Markdown or Quarto documents. It covers some basic statistical tests (t-test, ANOVA, correlation, chi-squared test, Wilcoxon test) as well as some basic number printing manipulations (formatting p-values, removing leading zeros for numbers that cannot be greater than one, and others). Other packages exist for formatting numbers and tests according to the APA style guidelines, such as papaja (<https://cran.r-project.org/package=papaja>) and apa (<https://cran.r-project.org/package=apa>), but they do not offer all convenience functionality included in prmisc'. The vignette has an overview of most of the functions included in the package.

This package provides a reliable and flexible toolbox to score patient-reported outcome (PRO), Quality of Life (QOL), and other psychometric measures. The guiding philosophy is that scoring errors can be eliminated by using a limited number of well-tested, well-behaved functions to score PRO-like measures. The workhorse of the package is the scoreScale function, which can be used to score most single-scale measures. It can reverse code items that need to be reversed before scoring and pro-rate scores for missing item data. Currently, three different types of scores can be output: summed item scores, mean item scores, and scores scaled to range from 0 to 100. The PROscorerTools functions can be used to write new functions that score more complex measures. In fact, PROscorerTools functions are the building blocks of the scoring functions in the PROscorer package (which is a repository of functions that score specific commonly-used instruments). Users are encouraged to use PROscorerTools to write scoring functions for their favorite PRO-like instruments, and to submit these functions for inclusion in PROscorer (a tutorial vignette will be added soon). The long-term vision for the PROscorerTools and PROscorer packages is to provide an easy-to-use system to facilitate the incorporation of PRO measures into research studies in a scientifically rigorous and reproducible manner. These packages and their vignettes are intended to help establish and promote "best practices" for scoring and describing PRO-like measures in research.

This function fits a reversible jump Bayesian piecewise exponential model that also includes the intensity of each event considered along with the rate of events.

An implementation of the Partition Of variation (POV) method as developed by Dr. Thomas A Little <https://thomasalittleconsulting.com> in 1993 for the analysis of semiconductor data for hard drive manufacturing. POV is based on sequential sum of squares and is an exact method that explains all observed variation. It quantitates both the between and within factor variation effects and can quantitate the influence of both continuous and categorical factors.

Fast, flexible framework for implementing Bayesian optimization of model hyperparameters according to the methods described in Snoek et al. <arXiv:1206.2944>. The package allows the user to run scoring function in parallel, save intermediary results, and tweak other aspects of the process to fully utilize the computing resources available to the user.

This package provides a novel pseudo-value regression approach for the differential co-expression network analysis in expression data, which can incorporate additional clinical variables in the model. This is a direct regression modeling for the differential network analysis, and it is therefore computationally amenable for the most users. The full methodological details can be found in Ahn S et al (2023) <doi:10.1186/s12859-022-05123-w>.

Conduct power analyses and inference of marginal effects. Uses plug-in estimation and influence functions to perform robust inference, optionally leveraging historical data to increase precision with prognostic covariate adjustment. The methods are described in Højbjerre-Frandsen et al. (2025) <doi:10.48550/arXiv.2503.22284>.

Hybridization probes for target sequences can be made based on melting temperature value calculated by R package TmCalculator <https://CRAN.R-project.org/package=TmCalculator> and methods extended from Beliveau, B. J.,(2018) <doi:10.1073/pnas.1714530115>, and those hybridization probes can be used to capture specific target regions in fluorescence in situ hybridization and next generation sequence experiments.

Includes JavaScript files that allow plotly maps to render without an internet connection.

These are useful tools and data sets for the study of quantitative peace science. The goal for this package is to include tools and data sets for doing original research that mimics well what a user would have to previously get from a software package that may not be well-sourced or well-supported. Those software bundles were useful the extent to which they encourage replications of long-standing analyses by starting the data-generating process from scratch. However, a lot of the functionality can be done relatively quickly and more transparently in the R programming language.

Provide summary table of daily physical activity and per-person/grouped heat map for accelerometer data from SenseWear Armband. See <https://templehealthcare.wordpress.com/the-sensewear-armband/> for more information about SenseWear Armband.

This package provides several measures ((dis)similarity, distance/metric, correlation, entropy) for comparing two partitions of the same set of objects. The different measures can be assigned to three different classes: Pair comparison (containing the famous Jaccard and Rand indices), set based, and information theory based. Many of the implemented measures can be found in Albatineh AN, Niewiadomska-Bugaj M and Mihalko D (2006) <doi:10.1007/s00357-006-0017-z> and Meila M (2007) <doi:10.1016/j.jmva.2006.11.013>. Partitions are represented by vectors of class labels which allow a straightforward integration with existing clustering algorithms (e.g. kmeans()). The package is mostly based on the S4 object system.

The Proton Game is a console-based data-crunching game for younger and older data scientists. Act as a data-hacker and find Slawomir Pietraszko's credentials to the Proton server. You have to solve four data-based puzzles to find the login and password. There are many ways to solve these puzzles. You may use loops, data filtering, ordering, aggregation or other tools. Only basics knowledge of R is required to play the game, yet the more functions you know, the more approaches you can try. The knowledge of dplyr is not required but may be very helpful. This game is linked with the ,,Pietraszko's Cave story available at http://biecek.pl/BetaBit/Warsaw. It's a part of Beta and Bit series. You will find more about the Beta and Bit series at http://biecek.pl/BetaBit.

Accurate classification of breast cancer tumors based on gene expression data is not a trivial task, and it lacks standard practices.The PAM50 classifier, which uses 50 gene centroid correlation distances to classify tumors, faces challenges with balancing estrogen receptor (ER) status and gene centering. The PCAPAM50 package leverages principal component analysis and iterative PAM50 calls to create a gene expression-based ER-balanced subset for gene centering, avoiding the use of protein expression-based ER data resulting into an enhanced Breast Cancer subtyping.

This package provides a direct and flexible method for estimating an ICA model. This approach estimates the densities for each component directly via a tilted Gaussian. The tilt functions are estimated via a GAM Poisson model. Details can be found in "Elements of Statistical Learning (2nd Edition)" in Section 14.7.4.

This package provides a novel tool for generating a piecewise constant estimation list of increasingly complex predictors based on an intensive and comprehensive search over the entire covariate space.