The distributed expectation maximization algorithms are used to solve parameters of multivariate Gaussian mixture models. The philosophy of the package is described in Guo, G. (2022) <doi:10.1080/02664763.2022.2053949>.

Estimation of the total population size from capture-recapture data efficiently and with low bias implementing the methods from Das M, Kennedy EH, and Jewell NP (2021) <arXiv:2104.14091>. The estimator is doubly robust against errors in the estimation of the intermediate nuisance parameters. Users can choose from the flexible estimation models provided in the package, or use any other preferred model.

Implement the methods proposed by Ahmad & Dey (2007) <doi:10.1016/j.datak.2007.03.016> in calculating the dissimilarity matrix at the presence of mixed attributes. This Package includes functions to discretize quantitative variables, calculate conditional probability for each pair of attribute values, distance between every pair of attribute values, significance of attributes, calculate dissimilarity between each pair of objects.

Several tests for differential methylation in methylation array data, including one-sided differential mean and variance test. Methods used in the package refer to Dai, J, Wang, X, Chen, H and others (2021) "Incorporating increased variability in discovering cancer methylation markers", Biostatistics, submitted.

This package contains the discrete nonparametric survivor function estimation algorithm of De Gruttola and Lagakos for doubly interval-censored failure time data and the discrete nonparametric survivor function estimation algorithm of Sun for doubly interval-censored left-truncated failure time data [Victor De Gruttola & Stephen W. Lagakos (1989) <doi:10.2307/2532030>] [Jianguo Sun (1995) <doi:10.2307/2533008>].

Draws stylized choropleth maps -- hexagonal maps and triangular multiclass hex maps -- for New Zealand District Health Boards and Regional Council areas. These allow faceted, coloured displays of quantitative information for comparison across District Health Boards or Regional Councils. The preprint Lumley (2019) <arXiv:1912.04435> is based on the methods in this package.

This package provides functions to facilitate access to the DKAN API (<https://dkan.readthedocs.io/en/latest/apis/index.html>), including the DKAN REST API (metadata), and the DKAN datastore API (data). Includes functions to list, create, retrieve, update, and delete datasets and resources nodes. It also includes functions to search and retrieve data from the DKAN datastore.

This package provides a collection of data-limited management procedures that can be evaluated with management strategy evaluation with the MSEtool package, or applied to fishery data to provide management recommendations.

This package provides an interactive viewer for data.frame and tibble objects using shiny <https://shiny.posit.co/> and DT <https://rstudio.github.io/DT/>. It supports complex filtering, column selection, and automatic generation of reproducible dplyr <https://dplyr.tidyverse.org/> code for data manipulation. The package is designed for ease of use in data exploration and reporting workflows.

Geologic pattern data from <https://ngmdb.usgs.gov/fgdc_gds/geolsymstd.php>. Access functions are provided in the accompanying package deeptime'.

Modifies dot plots to have different sizes of dots mimicking violin plots and identifies modes or peaks for them based on frequency and kernel density estimates (Rosenblatt, 1956) <doi:10.1214/aoms/1177728190> (Parzen, 1962) <doi:10.1214/aoms/1177704472>.

Shiny modules to import data into an application or addin from various sources, and to manipulate them after that.

Analyze and visualize the rhythmic behavior of animals using the degree of functional coupling (See Scheibe (1999) <doi:10.1076/brhm.30.2.216.1420>), compute and visualize harmonic power, actograms, average activity and diurnality index.

Deconvolving cell types from high-throughput gene profiling data. For more information on dtangle see Hunt et al. (2019) <doi:10.1093/bioinformatics/bty926>.

Interactive R tutorials and datasets for the textbook Field (2026), "Discovering Statistics Using R and RStudio", <https://www.discovr.rocks/>. Interactive tutorials cover general workflow in R and RStudio', summarizing data, visualizing data, fitting models and bias, correlation, the general linear model (GLM), moderation, mediation, missing values, comparing means using the GLM (analysis of variance), comparing adjusted means (analysis of covariance), factorial designs, repeated measures designs, exploratory factor analysis (EFA). There are no functions, only datasets and interactive tutorials.

Read, construct and write CDISC (Clinical Data Interchange Standards Consortium) Dataset JSON (JavaScript Object Notation) files, while validating per the Dataset JSON schema file, as described in CDISC (2023) <https://www.cdisc.org/standards/data-exchange/dataset-json>.

This package provides a variety of methods to identify data quality issues in process-oriented data, which are useful to verify data quality in a process mining context. Builds on the class for activity logs implemented in the package bupaR'. Methods to identify data quality issues either consider each activity log entry independently (e.g. missing values, activity duration outliers,...), or focus on the relation amongst several activity log entries (e.g. batch registrations, violations of the expected activity order,...).

The DALY Calculator is a free, open-source Graphical User Interface (GUI) for stochastic disability-adjusted life year (DALY) calculation.

Decomposition of (income) inequality by population sub groups. For a decomposition on a single variable the mean log deviation can be used (see Mookherjee Shorrocks (1982) <DOI:10.2307/2232673>). For a decomposition on multiple variables a regression based technique can be used (see Fields (2003) <DOI:10.1016/s0147-9121(03)22001-x>). Recentered influence function regression for marginal effects of the (income or wealth) distribution (see Firpo et al. (2009) <DOI:10.3982/ECTA6822>). Some extensions to inequality functions to handle weights and/or missings.

Researchers can characterize and learn about the properties of research designs before implementation using `DeclareDesign`. Ex ante declaration and diagnosis of designs can help researchers clarify the strengths and limitations of their designs and to improve their properties, and can help readers evaluate a research strategy prior to implementation and without access to results. It can also make it easier for designs to be shared, replicated, and critiqued.

Post Global Financial Crisis derivatives reforms have lifted the veil off over-the-counter (OTC) derivative markets. Swap Execution Facilities (SEFs) and Swap Data Repositories (SDRs) now publish data on swaps that are traded on or reported to those facilities (respectively). This package provides you the ability to get this data from supported sources.

This package provides a metapackage that brings together a curated collection of R packages containing domain-specific datasets. It includes time series data, educational metrics, crime records, medical datasets, and oncology research data. Designed to provide researchers, analysts, educators, and data scientists with centralized access to structured and well-documented datasets, this metapackage facilitates reproducible research, data exploration, and teaching applications across a wide range of domains. Included packages: - timeSeriesDataSets': Time series data from economics, finance, energy, and healthcare. - educationR': Datasets related to education, learning outcomes, and school metrics. - crimedatasets': Datasets on global and local crime and criminal behavior. - MedDataSets': Datasets related to medicine, public health, treatments, and clinical trials. - OncoDataSets': Datasets focused on cancer research, survival, genetics, and biomarkers.

Lightweight utility functions used for the R package development infrastructure inside the data integration centers ('DIZ') to standardize and facilitate repetitive tasks such as setting up a database connection or issuing notification messages and to avoid redundancy.

Calculates the T-Statistic for the drift burst hypothesis from the working paper Christensen, Oomen and Reno (2018) <DOI:10.2139/ssrn.2842535>. The authors MATLAB code is available upon request, see: <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2842535>.