Data Analysis using Bootstrap-Coupled ESTimation. Estimation statistics is a simple framework that avoids the pitfalls of significance testing. It uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one's experiment/intervention, as opposed to a false dichotomy engendered by P values. An estimation plot has two key features: 1. It presents all datapoints as a swarmplot, which orders each point to display the underlying distribution. 2. It presents the effect size as a bootstrap 95% confidence interval on a separate but aligned axes. Estimation plots are introduced in Ho et al., Nature Methods 2019, 1548-7105. <doi:10.1038/s41592-019-0470-3>. The free-to-view PDF is located at <https://www.nature.com/articles/s41592-019-0470-3.epdf?author_access_token=Euy6APITxsYA3huBKOFBvNRgN0jAjWel9jnR3ZoTv0Pr6zJiJ3AA5aH4989gOJS_dajtNr1Wt17D0fh-t4GFcvqwMYN03qb8C33na_UrCUcGrt-Z0J9aPL6TPSbOxIC-pbHWKUDo2XsUOr3hQmlRew%3D%3D>.

Estimate and return the needed parameters for visualizations designed for OpenBudgets.eu <http://openbudgets.eu/> datasets. Calculate descriptive statistical measures in budget data of municipalities across Europe, according to the OpenBudgets.eu data model. There are functions for measuring central tendency and dispersion of amount variables along with their distributions and correlations and the frequencies of categorical variables for a given dataset. Also, can be used generally to other datasets, to extract visualization parameters, convert them to JSON format and use them as input in a different graphical interface.

Efficient procedures for fitting the DD-PCA (Ke et al., 2019, <arXiv:1906.00051>) by decomposing a large covariance matrix into a low-rank matrix plus a diagonally dominant matrix. The implementation of DD-PCA includes the convex approach using the Alternating Direction Method of Multipliers (ADMM) and the non-convex approach using the iterative projection algorithm. Applications of DD-PCA to large covariance matrix estimation and global multiple testing are also included in this package.

This package provides a data augmentation based sampler for conducting privacy-aware Bayesian inference. The dapper_sample() function takes an existing sampler as input and automatically constructs a privacy-aware sampler. The process of constructing a sampler is simplified through the specification of four independent modules, allowing for easy comparison between different privacy mechanisms by only swapping out the relevant modules. Probability mass functions for the discrete Gaussian and discrete Laplacian are provided to facilitate analyses dealing with privatized count data. The output of dapper_sample() can be analyzed using many of the same tools from the rstan ecosystem. For methodological details on the sampler see Ju et al. (2022) <doi:10.48550/arXiv.2206.00710>, and for details on the discrete Gaussian and discrete Laplacian distributions see Canonne et al. (2020) <doi:10.48550/arXiv.2004.00010>.

This package implements common measures of diversity and spatial segregation. This package has tools to compute the majority of measures are reviewed in Massey and Denton (1988) <doi:10.2307/2579183>. Multiple common measures of within-geography diversity are implemented as well. All functions operate on data frames with a tidyselect based workflow.

Populate data from an R environment into .doc and .docx templates. Create a template document in a program such as Word', and add strings encased in guillemet characters to create flags («example»). Use getDictionary() to create a dictionary of flags and replacement values, then call docket() to generate a populated document.

Statistical tests and test statistics to identify events in a dataset that are dragon kings (DKs). The statistical methods in this package were reviewed in Wheatley & Sornette (2015) <doi:10.2139/ssrn.2645709>.

Generate descriptive statistics such as measures of location, dispersion, frequency tables, cross tables, group summaries and multiple one/two way tables.

Produce publication quality graphics from output of GGobi describe display plugin.

DMC model simulation detailed in Ulrich, R., Schroeter, H., Leuthold, H., & Birngruber, T. (2015). Automatic and controlled stimulus processing in conflict tasks: Superimposed diffusion processes and delta functions. Cognitive Psychology, 78, 148-174. Ulrich et al. (2015) <doi:10.1016/j.cogpsych.2015.02.005>. Decision processes within choice reaction-time (CRT) tasks are often modelled using evidence accumulation models (EAMs), a variation of which is the Diffusion Decision Model (DDM, for a review, see Ratcliff & McKoon, 2008). Ulrich et al. (2015) introduced a Diffusion Model for Conflict tasks (DMC). The DMC model combines common features from within standard diffusion models with the addition of superimposed controlled and automatic activation. The DMC model is used to explain distributional reaction time (and error rate) patterns in common behavioural conflict-like tasks (e.g., Flanker task, Simon task). This R-package implements the DMC model and provides functionality to fit the model to observed data. Further details are provided in the following paper: Mackenzie, I.G., & Dudschig, C. (2021). DMCfun: An R package for fitting Diffusion Model of Conflict (DMC) to reaction time and error rate data. Methods in Psychology, 100074. <doi:10.1016/j.metip.2021.100074>.

Data are essential in statistical analysis. This data package consists of four datasets for descriptive statistics, two datasets for statistical hypothesis testing, and two datasets for regression analysis. All of the datasets are based on Rattanalertnusorn, A. (2024) <https://www.researchgate.net/publication/371944275_porkaermxarlaeakarprayuktchingan_R_and_its_applications>.

Analysis of historical non-decimal currencies and value systems that use tripartite or tetrapartite systems such as pounds, shillings, and pence. It introduces new vector classes to represent non-decimal currencies, making them compatible with numeric classes, and provides functions to work with these classes in data frames in the context of double-entry bookkeeping.

Plan optimal sample size allocation and go/no-go decision rules for phase II/III drug development programs with time-to-event, binary or normally distributed endpoints when assuming fixed treatment effects or a prior distribution for the treatment effect, using methods from Kirchner et al. (2016) <doi:10.1002/sim.6624> and Preussler (2020). Optimal is in the sense of maximal expected utility, where the utility is a function taking into account the expected cost and benefit of the program. It is possible to extend to more complex settings with bias correction (Preussler S et al. (2020) <doi:10.1186/s12874-020-01093-w>), multiple phase III trials (Preussler et al. (2019) <doi:10.1002/bimj.201700241>), multi-arm trials (Preussler et al. (2019) <doi:10.1080/19466315.2019.1702092>), and multiple endpoints (Kieser et al. (2018) <doi:10.1002/pst.1861>).

Easy access to species distribution data for 6 regions in the world, for a total of 226 anonymised species. These data are described and made available by Elith et al (2020) <doi:10.17161/bi.v15i2.13384> to compare species distribution modelling methods.

It contains functions to apply blockmodeling of signed (positive and negative weights are assigned to the links), one-mode and valued one-mode and two-mode (two sets of nodes are considered, e.g. employees and organizations) networks (Brusco et al. (2019) <doi:10.1111/bmsp.12192>).

Example datasets from the book "An Introduction to Generalised Linear Models" (Year: 2018, ISBN:9781138741515) by Dobson and Barnett.

View 2D/3D sections, contour plots, mesh of excursion sets for computer experiments designs, surrogates or test functions.

Implementation of DetMCD, a new algorithm for robust and deterministic estimation of location and scatter. The benefits of robust and deterministic estimation are explained in Hubert, Rousseeuw and Verdonck (2012) <doi:10.1080/10618600.2012.672100>.

This package contains one main function deduped() which speeds up slow, vectorized functions by only performing computations on the unique values of the input and expanding the results at the end.

This package provides a deep neural network model with a monotonic increasing single index function tailored for periodontal disease studies. The residuals are assumed to follow a skewed T distribution, a skewed normal distribution, or a normal distribution. More details can be found at Liu, Huang, and Bai (2024) <doi:10.1016/j.csda.2024.108012>.

This package performs an exploratory data analysis through a shiny interface. It includes basic methods such as the mean, median, mode, normality test, among others. It also includes clustering techniques such as Principal Components Analysis, Hierarchical Clustering and the K-Means Method.

This package provides several datasets used throughout the book "Sampling and Data Analysis Using R: Theory and Practice" by Islam (2025, ISBN:978-984-35-8644-5). The datasets support teaching and learning of statistical concepts such as sampling methods, descriptive analysis, estimation and basic data handling. These curated data objects allow instructors, students and researchers to reproduce examples, practice data manipulation and perform hands-on analysis using R.

This package performs detection of Differential Item Functioning using the method DIFboost as proposed by Schauberger and Tutz (2016) <doi:10.1111/bmsp.12060>.

Compare functional enrichment between two experimentally-derived groups of genes or proteins (Peterson, DR., et al.(2018)) <doi: 10.1371/journal.pone.0198139>. Given a list of gene symbols, diffEnrich will perform differential enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) REST API. This package provides a number of functions that are intended to be used in a pipeline. Briefly, the user provides a KEGG formatted species id for either human, mouse or rat, and the package will download and clean species specific ENTREZ gene IDs and map them to their respective KEGG pathways by accessing KEGG's REST API. KEGG's API is used to guarantee the most up-to-date pathway data from KEGG. Next, the user will identify significantly enriched pathways from two gene sets, and finally, the user will identify pathways that are differentially enriched between the two gene sets. In addition to the analysis pipeline, this package also provides a plotting function.