Area under the curve (AUC; Myerson et al., 2001) <doi:10.1901/jeab.2001.76-235> is a popular measure used in discounting research. Although the calculation of AUC is standardized, there are differences in AUC based on some assumptions. For example, Myerson et al. (2001) <doi:10.1901/jeab.2001.76-235> assumed that (with delay discounting data) a researcher would impute an indifference point at zero delay equal to the value of the larger, later outcome. However, this practice is not clearly followed. This imputed zero-delay indifference point plays an important role in log and ordinal versions of AUC. Ordinal and log versions of AUC are described by Borges et al. (2016)<doi:10.1002/jeab.219>. The package can calculate all three versions of AUC [and includes a new version: IHS(AUC)], impute indifference points when x = 0, calculate ordinal AUC in the case of Halton sampling of x-values, and account for probability discounting AUC.

Implementation of different algorithms for analyzing randomly truncated data, one-sided and two-sided (i.e. doubly) truncated data. It serves to compute empirical cumulative distributions and also kernel density and hazard functions using different bandwidth selectors. Several real data sets are included.

Robust distance-based methods applied to matrices and data frames, producing distance matrices that can be used as input for various visualization techniques such as graphs, heatmaps, or multidimensional scaling configurations. See Boj and Grané (2024) <doi:10.1016/j.seps.2024.101992>.

This package creates survey designs for distance sampling surveys. These designs can be assessed for various effort and coverage statistics. Once the user is satisfied with the design characteristics they can generate a set of transects to use in their distance sampling survey. Many of the designs implemented in this R package were first made available in our Distance for Windows software and are detailed in Chapter 7 of Advanced Distance Sampling, Buckland et. al. (2008, ISBN-13: 978-0199225873). Find out more about estimating animal/plant abundance with distance sampling at <https://distancesampling.org/>.

Differential exon usage test for RNA-Seq data via an empirical Bayes shrinkage method for the dispersion parameter the utilizes inclusion-exclusion data to analyze the propensity to skip an exon across groups. The input data consists of two matrices where each row represents an exon and the columns represent the biological samples. The first matrix is the count of the number of reads expressing the exon for each sample. The second matrix is the count of the number of reads that either express the exon or explicitly skip the exon across the samples, a.k.a. the total count matrix. Dividing the two matrices yields proportions representing the propensity to express the exon versus skipping the exon for each sample.

Includes functions that researchers or practitioners may use to clean raw data, transferring html, xlsx, txt data file into other formats. And it also can be used to manipulate text variables, extract numeric variables from text variables and other variable cleaning processes. It is originated from a author's project which focuses on creative performance in online education environment. The resulting paper of that study will be published soon.

Using these tools to simplify the research process of political science and other social sciences. The current version can create folder system for academic project in political science, calculate psychological trait scores, visualize experimental and spatial data, and set up color-blind palette, functions used in academic research of political psychology or political science in general.

For checking the dataset from EDC(Electronic Data Capture) in clinical trials. dmtools reshape your dataset in a tidy view and check events. You can reshape the dataset and choose your target to check, for example, the laboratory reference range.

This package provides a decorator is a function that receives a function, extends its behaviour, and returned the altered function. Any caller that uses the decorated function uses the same interface as it were the original, undecorated function. Decorators serve two primary uses: (1) Enhancing the response of a function as it sends data to a second component; (2) Supporting multiple optional behaviours. An example of the first use is a timer decorator that runs a function, outputs its execution time on the console, and returns the original function's result. An example of the second use is input type validation decorator that during running time tests whether the caller has passed input arguments of a particular class. Decorators can reduce execution time, say by memoization, or reduce bugs by adding defensive programming routines.

This package provides landscape genomic functions to analyse SNP (single nuclear polymorphism) data, such as least cost path analysis and isolation by distance. Therefore each sample needs to have coordinate data attached (lat/lon) to be able to run most of the functions. dartR.spatial is a package that belongs to the dartRverse suit of packages and depends on dartR.base and dartR.data'.

Using a Gaussian copula approach, this package generates simulated data mimicking a target real dataset. It supports normal, Poisson, empirical, and DESeq2 (negative binomial with size factors) marginal distributions. It uses an low-rank plus diagonal covariance matrix to efficiently generate omics-scale data. Methods are described in: Yang, Grant, and Brooks (2025) <doi:10.1101/2025.01.31.634335>.

This package provides a modular package for measuring disparity (multidimensional space occupancy). Disparity can be calculated from any matrix defining a multidimensional space. The package provides a set of implemented metrics to measure properties of the space and allows users to provide and test their own metrics. The package also provides functions for looking at disparity in a serial way (e.g. disparity through time) or per groups as well as visualising the results. Finally, this package provides several statistical tests for disparity analysis.

This package provides the ability to generate images from documents of different types. Three main features are provided: generating document thumbnails, performing visual tests of documents, and updating fields and tables of contents of a Microsoft Word or RTF document. Microsoft Word and/or LibreOffice must be installed on the machine. If Microsoft Word is available, it can produce PDF documents or images identical to the originals; otherwise LibreOffice is used and the rendering may sometimes differ from the original documents.

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.

Inference functionalities for distributed-lag linear structural equation models (DLSEMs). DLSEMs are Markovian structural causal models where each factor of the joint probability distribution is a distributed-lag linear regression with constrained lag shapes (Magrini, 2018 <doi:10.2478/bile-2018-0012>; Magrini et al., 2019 <doi:10.1007/s11135-019-00855-z>). DLSEMs account for temporal delays in the dependence relationships among the variables through a single parameter per covariate, thus allowing to perform dynamic causal inference in a feasible fashion. Endpoint-constrained quadratic, quadratic decreasing, linearly decreasing and gamma lag shapes are available.

This package provides a collection of tools that support data diagnosis, exploration, and transformation. Data diagnostics provides information and visualization of missing values, outliers, and unique and negative values to help you understand the distribution and quality of your data. Data exploration provides information and visualization of the descriptive statistics of univariate variables, normality tests and outliers, correlation of two variables, and the relationship between the target variable and predictor. Data transformation supports binning for categorizing continuous variables, imputes missing values and outliers, and resolves skewness. And it creates automated reports that support these three tasks.

Analysis and visualization of dropout between conditions in surveys and (online) experiments. Features include computation of dropout statistics, comparing dropout between conditions (e.g. Chi squared), analyzing survival (e.g. Kaplan-Meier estimation), comparing conditions with the most different rates of dropout (Kolmogorov-Smirnov) and visualizing the result of each in designated plotting functions. Article published in _Behavior Research Methods_ on dropR by the authors: Dropout analysis: A method for data from Internet-based research and dropR', an R-based web app and package to analyze and visualize dropout. (2025) <doi:10.3758/s13428-025-02730-2>. Sources: Andrea Frick, Marie-Terese Baechtiger & Ulf-Dietrich Reips (2001) <doi:10.5167/uzh-19758>; Ulf-Dietrich Reips (2002) <doi:10.1026//1618-3169.49.4.243>.

Transform newswire and earnings call transcripts as PDF obtained from Nexis Uni to R data frames. Various newswires and FairDisclosure earnings call formats are supported. Further, users can apply several pre-defined dictionaries on the data based on Graffin et al. (2016)<doi:10.5465/amj.2013.0288> and Gamache et al. (2015)<doi:10.5465/amj.2013.0377>.

This package implements an efficient algorithm for solving sparse-penalized support vector machines with kernel density convolution. This package is designed for high-dimensional classification tasks, supporting lasso (L1) and elastic-net penalties for sparse feature selection and providing options for tuning kernel bandwidth and penalty weights. The dcsvm is applicable to fields such as bioinformatics, image analysis, and text classification, where high-dimensional data commonly arise. Learn more about the methodology and algorithm at Wang, Zhou, Gu, and Zou (2023) <doi:10.1109/TIT.2022.3222767>.

This package provides a set of functions and a class to connect, extract and upload information from the LSEG Datastream database. This package uses the DSWS API and server used by the Datastream DFO addin'. Details of this API are available at <https://www.lseg.com/en/data-analytics>. Please report issues at <https://github.com/CharlesCara/DatastreamDSWS2R/issues>.

This package provides a tool developed with the Golem framework which provides an easier way to check cells differences between two data frames. The user provides two data frames for comparison, selects IDs variables identifying each row of input data, then clicks a button to perform the comparison. Several R package functions are used to describe the data and perform the comparison in the server of the application. The main ones are comparedf() from arsenal and skim() from skimr'. For more details see the description of comparedf() from the arsenal package and that of skim() from the skimr package.

Weighted frequency and contingency tables of categorical variables and of the comparison of the mean value of a numerical variable by the levels of a factor, and methods to produce xtable objects of the tables and to plot them. There are also functions to facilitate the character encoding conversion of objects, to quickly convert fixed width files into csv ones, and to export a data.frame to a text file with the necessary R and SPSS codes to reread the data.

This package provides functions are provided that facilitate the import and analysis of SNP (single nucleotide polymorphism) and silicodart (presence/absence) data. The main focus is on data generated by DarT (Diversity Arrays Technology), however, data from other sequencing platforms can be used once SNP or related fragment presence/absence data from any source is imported. Genetic datasets are stored in a derived genlight format (package adegenet'), that allows for a very compact storage of data and metadata. Functions are available for importing and exporting of SNP and silicodart data, for reporting on and filtering on various criteria (e.g. CallRate', heterozygosity, reproducibility, maximum allele frequency). Additional functions are available for visualization (e.g. Principle Coordinate Analysis) and creating a spatial representation using maps. dartR supports also the analysis of 3rd party software package such as newhybrid', structure', NeEstimator and blast'. Since version 2.0.3 we also implemented simulation functions, that allow to forward simulate SNP dynamics under different population and evolutionary dynamics. Comprehensive tutorials and support can be found at our github repository: github.com/green-striped-gecko/dartR/. If you want to cite dartR', you find the information by typing citation('dartR') in the console.

Formatting of population and case data, calculation of Standardized Incidence Ratios, and fitting the BYM model using INLA'. For details see Brown (2015) <doi:10.18637/jss.v063.i12>.