This package performs analysis of popular experimental designs used in the field of biological research. The designs covered are completely randomized design, randomized complete block design, factorial completely randomized design, factorial randomized complete block design, split plot design, strip plot design and latin square design. The analysis include analysis of variance, coefficient of determination, normality test of residuals, standard error of mean, standard error of difference and multiple comparison test of means. The package has functions for transformation of data and yield data conversion. Some datasets are also added in order to facilitate examples.

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.

This package provides access to Dataverse APIs <https://dataverse.org/> (versions 4-5), enabling data search, retrieval, and deposit. For Dataverse versions <= 3.0, use the archived dvn package <https://cran.r-project.org/package=dvn>.

For working with the DataRobot predictive modeling platform's API <https://www.datarobot.com/>.

Draw, manipulate, and evaluate directed acyclic graphs and simulate corresponding data, as described in International Journal of Epidemiology 50(6):1772-1777.

Statistical modelling and forecasting in claims reserving in non-life insurance under the Double Chain Ladder framework by Martinez-Miranda, Nielsen and Verrall (2012).

Curated datasets and intuitive data management functions to streamline epidemiological data workflows. It is designed to support researchers in quickly accessing clean, structured data and applying essential cleaning, summarizing, visualization, and export operations with minimal effort. Whether you're preparing a cohort for analysis or creating reports, DIVINE makes the process more efficient, transparent, and reproducible.

Differential Item Functioning (DIF) Analysis with shiny application interfaces. You can run the functions in this package without any arguments and perform your DIF analysis using user-friendly interfaces.

Supports global health data analysis, including a publication-ready ggplot2 theme, a flextable defaults helper, a thin pie chart wrapper, built-in regional country-code datasets with a WHO region lookup helper, a geometric mean function for indicator aggregation, and convenience clients for the World Health Organization Global Health Observatory (GHO) OData API <https://ghoapi.azureedge.net/api/> and the United Nations Sustainable Development Goals (SDG) API <https://unstats.un.org/SDGAPI/swagger/>.

Density ratio estimation. The estimated density ratio function can be used in many applications such as anomaly detection, change-point detection, covariate shift adaptation. The implemented methods are uLSIF (Hido et al. (2011) <doi:10.1007/s10115-010-0283-2>), RuLSIF (Yamada et al. (2011) <doi:10.1162/NECO_a_00442>), and KLIEP (Sugiyama et al. (2007) <doi:10.1007/s10463-008-0197-x>).

Create donut charts positioned on maps from tidy data. The package provides helpers to compute donut polygon geometries, optional origin-destination flow lines, ready-to-use ggplot2 map layers, and interactive leaflet widgets.

Evaluation (S4-)classes based on package distr for evaluating procedures (estimators/tests) at data/simulation in a unified way.

An interactive image editing tool that can be added as part of the HTML in Shiny, R markdown or any type of HTML document. Often times, plots, photos are embedded in the web application/file. drawer can take screenshots of these image-like elements, or any part of the HTML document and send to an image editing space called canvas to allow users immediately edit the screenshot(s) within the same document. Users can quickly combine, compare different screenshots, upload their own images and maybe make a scientific figure.

This package provides a function toolkit to facilitate reproducible RNA-Seq Differential Gene Expression (DGE) analysis (Law (2015) <doi:10.12688/f1000research.9005.3>). The tools include both analysis work-flow and utility functions: mapping/unit conversion, count normalization, accounting for unknown covariates, and more. This is a complement/cohort to the DGEobj package that provides a flexible container to manage and annotate Differential Gene Expression analysis results.

This package provides functions to impute large gaps within time series based on Dynamic Time Warping methods. It contains all required functions to create large missing consecutive values within time series and to fill them, according to the paper Phan et al. (2017), <DOI:10.1016/j.patrec.2017.08.019>. Performance criteria are added to compare similarity between two signals (query and reference).

Helps to describe a data frame in hand. Has been developed during PhD work of the maintainer. More information may be obtained from Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202>.

Graphical interface for loading datasets in RStudio from all installed (including unloaded) packages, also includes command line interfaces.

This package provides functions for estimating parameters and hyperparameters of the linearized hyperbolic model, and testing equality of hyperparameters.

Comparison of the accuracy of two binary diagnostic tests in a "paired" study design, i.e. when each test is applied to each subject in the study.

This package provides a distance density clustering (DDC) algorithm in R. DDC uses dynamic time warping (DTW) to compute a similarity matrix, based on which cluster centers and cluster assignments are found. DDC inherits dynamic time warping (DTW) arguments and constraints. The cluster centers are centroid points that are calculated using the DTW Barycenter Averaging (DBA) algorithm. The clustering process is divisive. At each iteration, cluster centers are updated and data is reassigned to cluster centers. Early stopping is possible. The output includes cluster centers and clustering assignment, as described in the paper (Ma et al (2017) <doi:10.1109/ICDMW.2017.11>).

Output graphics to EMF+/EMF.

This package contains functions to help with generating tables with descriptive statistics. In addition, the package can display results of statistical tests for group comparisons. A wide range of test procedures is supported, and user-defined test functions can be incorporated.

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.

This package contains a single function dclust() for divisive hierarchical clustering based on recursive k-means partitioning (k = 2). Useful for clustering large datasets where computation of a n x n distance matrix is not feasible (e.g. n > 10,000 records). For further information see Steinbach, Karypis and Kumar (2000) <http://glaros.dtc.umn.edu/gkhome/fetch/papers/docclusterKDDTMW00.pdf>.