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 collection of functions for calculating the M2 model fit statistic for diagnostic classification models as described by Liu et al. (2016) <DOI:10.3102/1076998615621293>. These functions provide multiple sources of information for model fit according to the M2 statistic, including the M2 statistic, the *p* value for that M2 statistic, and the Root Mean Square Error of Approximation based on the M2 statistic.

As a distributed imputation strategy, the Distributed full information Multiple Imputation method is developed to impute missing response variables in distributed linear regression. The philosophy of the package is described in Guo (2025) <doi:10.1038/s41598-025-93333-6>.

This package contains Data frames and functions used in the book "Design and Analysis of Experiments with R", Lawson(2015) ISBN-13:978-1-4398-6813-3.

Implementation of the double/debiased machine learning framework of Chernozhukov et al. (2018) <doi:10.1111/ectj.12097> for partially linear regression models, partially linear instrumental variable regression models, interactive regression models and interactive instrumental variable regression models. DoubleML allows estimation of the nuisance parts in these models by machine learning methods and computation of the Neyman orthogonal score functions. DoubleML is built on top of mlr3 and the mlr3 ecosystem. The object-oriented implementation of DoubleML based on the R6 package is very flexible. More information available in the publication in the Journal of Statistical Software: <doi:10.18637/jss.v108.i03>.

Calculates Distinctiveness Centrality in social networks. For formulas and descriptions, see Fronzetti Colladon and Naldi (2020) <doi:10.1371/journal.pone.0233276>.

Tissue-specific enrichment analysis to assess lists of candidate genes or RNA-Seq expression profiles. Pei G., Dai Y., Zhao Z. Jia P. (2019) deTS: Tissue-Specific Enrichment Analysis to decode tissue specificity. Bioinformatics, In submission.

The dentomedical package provides a comprehensive suite of tools for medical and dental research. It includes automated descriptive statistics, bivariate analysis with intelligent test selection, logistic regression, and diagnostic accuracy assessment. All functions generate structured, publication-ready tables using flextable', ensuring reproducibility and clarity suitable for manuscripts, reports, and clinical research workflows.

The purpose of this package is to provide a comprehensive R interface to the Decision Support System for Agrotechnology Transfer Cropping Systems Model (DSSAT-CSM; see <https://dssat.net> for more information). The package provides cross-platform functions to read and write input files, run DSSAT-CSM, and read output files.

Prediction methods where explanatory information is coded as a matrix of distances between individuals. Distances can either be directly input as a distances matrix, a squared distances matrix, an inner-products matrix or computed from observed predictors.

Implementation of different statistical tools for the description and analysis of gene expression data based on the concept of data depth, namely, the scale curves for visualizing the dispersion of one or various groups of samples (e.g. types of tumors), a rank test to decide whether two groups of samples come from a single distribution and two methods of supervised classification techniques, the DS and TAD methods. All these techniques are based on the Modified Band Depth, which is a recent notion of depth with a low computational cost, what renders it very appropriate for high dimensional data such as gene expression data.

This package contains an implementation of the d-variable Hilbert Schmidt independence criterion and several hypothesis tests based on it, as described in Pfister et al. (2017) <doi:10.1111/rssb.12235>.

Analysis of preprocessed dramatic texts, with respect to literary research. The package provides functions to analyze and visualize information about characters, stage directions, the dramatic structure and the text itself. The dramatic texts are expected to be in CSV format, which can be installed from within the package, sample texts are provided. The package and the reasoning behind it are described in Reiter et al. (2017) <doi:10.18420/in2017_119>.

Improves the balance of optimal matching with near-fine balance by giving penalties on the unbalanced covariates with the unbalanced directions. Many directional penalties can also be viewed as Lagrange multipliers, pushing a matched sample in the direction of satisfying a linear constraint that would not be satisfied without penalization. Yu and Rosenbaum (2019) <doi:10.1111/biom.13098>.

Import data from a digital image; it requires user input for calibration and to locate the data points. The end result is similar to DataThief and other other programs that digitize published plots or graphs.

The implemented methods are: Standard Bass model, Generalized Bass model (with rectangular shock, exponential shock, and mixed shock. You can choose to add from 1 to 3 shocks), Guseo-Guidolin model and Variable Potential Market model, and UCRCD model. The Bass model consists of a simple differential equation that describes the process of how new products get adopted in a population, the Generalized Bass model is a generalization of the Bass model in which there is a "carrier" function x(t) that allows to change the speed of time sliding. In some real processes the reachable potential of the resource available in a temporal instant may appear to be not constant over time, because of this we use Variable Potential Market model, in which the Guseo-Guidolin has a particular specification for the market function. The UCRCD model (Unbalanced Competition and Regime Change Diachronic) is a diffusion model used to capture the dynamics of the competitive or collaborative transition.

Data science methods used in wind energy applications. Current functionalities include creating a multi-dimensional power curve model, performing power curve function comparison, covariate matching, and energy decomposition. Relevant works for the developed functions are: funGP() - Prakash et al. (2022) <doi:10.1080/00401706.2021.1905073>, AMK() - Lee et al. (2015) <doi:10.1080/01621459.2014.977385>, tempGP() - Prakash et al. (2022) <doi:10.1080/00401706.2022.2069158>, ComparePCurve() - Ding et al. (2021) <doi:10.1016/j.renene.2021.02.136>, deltaEnergy() - Latiffianti et al. (2022) <doi:10.1002/we.2722>, syncSize() - Latiffianti et al. (2022) <doi:10.1002/we.2722>, imptPower() - Latiffianti et al. (2022) <doi:10.1002/we.2722>, All other functions - Ding (2019, ISBN:9780429956508).

An R DataBase Interface ('DBI') compatible interface to various database platforms ('PostgreSQL', Oracle', Microsoft SQL Server', Amazon Redshift', Microsoft Parallel Database Warehouse', IBM Netezza', Apache Impala', Google BigQuery', Snowflake', Spark', SQLite', and InterSystems IRIS'). Also includes support for fetching data as Andromeda objects. Uses either Java Database Connectivity ('JDBC') or other DBI drivers to connect to databases.

We consider a set of sample counts obtained by sampling arbitrary fractions of a finite volume containing an homogeneously dispersed population of identical objects. This package implements a Bayesian derivation of the posterior probability distribution of the population size using a binomial likelihood and non-conjugate, discrete uniform priors under sampling with or without replacement. This can be used for a variety of statistical problems involving absolute quantification under uncertainty. See Comoglio et al. (2013) <doi:10.1371/journal.pone.0074388>.

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.

Statistical hypothesis testing using the Delta method as proposed by Deng et al. (2018) <doi:10.1145/3219819.3219919>. This method replaces the standard variance estimation formula in the Z-test with an approximate formula derived via the Delta method, which can account for within-user correlation.

Allows manual creation of themes and logos to be used in applications created using the shinydashboard package. Removes the need to change the underlying css code by wrapping it into a set of convenient R functions.

Non-normality could greatly distort the meta-analytic results, according to the simulation in Sun and Cheung (2020) <doi: 10.3758/s13428-019-01334-x>. This package aims to detect non-normality for two independent groups with only limited descriptive statistics, including mean, standard deviation, minimum, and maximum.

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.