Estimation of heterogeneity-robust difference-in-differences estimators, with a binary, discrete, or continuous treatment, in designs where past treatments may affect the current outcome.

Density estimation for possibly large data sets and conditional/unconditional random number generation or bootstrapping with distribution element trees. The function det.construct translates a dataset into a distribution element tree. To evaluate the probability density based on a previously computed tree at arbitrary query points, the function det.query is available. The functions det1 and det2 provide density estimation and plotting for one- and two-dimensional datasets. Conditional/unconditional smooth bootstrapping from an available distribution element tree can be performed by det.rnd'. For more details on distribution element trees, see: Meyer, D.W. (2016) <arXiv:1610.00345> or Meyer, D.W., Statistics and Computing (2017) <doi:10.1007/s11222-017-9751-9> and Meyer, D.W. (2017) <arXiv:1711.04632> or Meyer, D.W., Journal of Computational and Graphical Statistics (2018) <doi:10.1080/10618600.2018.1482768>.

This package provides a library of density, distribution function, quantile function, (bounded) raw moments and random generation for a collection of distributions relevant for the firm size literature. Additionally, the package contains tools to fit these distributions using maximum likelihood and evaluate these distributions based on (i) log-likelihood ratio and (ii) deviations between the empirical and parametrically implied moments of the distributions. We add flexibility by allowing the considered distributions to be combined into piecewise composite or finite mixture distributions, as well as to be used when truncated. See Dewitte (2020) <https://hdl.handle.net/1854/LU-8644700> for a description and application of methods available in this package.

This package provides a grammar of data manipulation with data.table', providing a consistent a series of utility functions that help you solve the most common data manipulation challenges.

Build donut/pie charts with ggplot2 layer by layer, exploiting the advantages of polar symmetry. Leverage layouts to distribute labels effectively. Connect labels to donut segments using pins. Streamline annotation and highlighting.

This package provides methods for testing the equality between groups of estimated density functions. The package implements FDET (Fourier-based Density Equality Testing) and MDET (Moment-based Density Equality Testing), two new approaches introduced by the author. Both methods extend an earlier testing approach by Delicado (2007), "Functional k-sample problem when data are density functions" <doi:10.1007/s00180-007-0047-y>, which is referred to as DET (Density Equality Testing) in this package for clarity. FDET compares groups of densities based on their global shape using Fourier transforms, while MDET tests for differences in distributional moments. All methods are described in Anarat, Krutmann and Schwender (2025), "Testing for Differences in Extrinsic Skin Aging Based on Density Functions" (Submitted).

This package contains a range of functions covering the present development of the distributional method for the dichotomisation of continuous outcomes. The method provides estimates with standard error of a comparison of proportions (difference, odds ratio and risk ratio) derived, with similar precision, from a comparison of means. See the URL below or <arXiv:1809.03279> for more information.

Connect to the DocuSign Rest API <https://www.docusign.com/p/RESTAPIGuide/RESTAPIGuide.htm>, which supports embedded signing, and sending of documents.

Tests whether multivariate ordinal data may stem from discretizing a multivariate normal distribution. The test is described by Foldnes and Grønneberg (2019) <doi:10.1080/10705511.2019.1673168>. In addition, an adjusted polychoric correlation estimator is provided that takes marginal knowledge into account, as described by Grønneberg and Foldnes (2022) <doi:10.1037/met0000495>.

This package provides methods for analyzing population dynamics and movement tracks simulated using the DEPONS model <https://www.depons.eu> (v.3.0), for manipulating input raster files, shipping routes and for analyzing sound propagated from ships.

Computes the first stage GMM estimate of a dynamic linear model with p lags of the dependent variables.

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.

This package implements the Improved Expectation Maximisation EM* and the traditional EM algorithm for clustering big data (gaussian mixture models for both multivariate and univariate datasets). This version implements the faster alternative-EM* that expedites convergence via structure based data segregation. The implementation supports both random and K-means++ based initialization. Reference: Parichit Sharma, Hasan Kurban, Mehmet Dalkilic (2022) <doi:10.1016/j.softx.2021.100944>. Hasan Kurban, Mark Jenne, Mehmet Dalkilic (2016) <doi:10.1007/s41060-017-0062-1>.

This package provides functionality for users who are learning R or the techniques of data analysis. Written as a collection of wrapper functions, the DTwrapper package facilitates many core operations of data processing. This is achieved with relatively few requirements about the order of the processing steps or knowledge of specialized syntax. DTwrappers creates coding results along with translations to data.table's code. This enables users to benefit from the speed and efficiency of data.table's calculations. Furthermore, the package also provides the translated code for educational purposes so that users can review working examples of coding syntax and calculations.

Data cloud geometry (DCG) applies random walks in finding community structures for social networks. Fushing, VanderWaal, McCowan, & Koehl (2013) (<doi:10.1371/journal.pone.0056259>).

Hash an expression with its dependencies and store its value, reloading it from a file as long as both the expression and its dependencies stay the same.

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

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'.

Individual gene expression patterns are encoded into a series of eigenvector patterns ('WGCNA package). Using the framework of linear model-based differential expression comparisons ('limma package), time-course expression patterns for genes in different conditions are compared and analyzed for significant pattern changes. For reference, see: Greenham K, Sartor RC, Zorich S, Lou P, Mockler TC and McClung CR. eLife. 2020 Sep 30;9(4). <doi:10.7554/eLife.58993>.

Geodesic distance between phylogenetic trees and associated functions. The theoretical background of distory is published in Billera et al. (2001) "Geometry of the space of phylogenetic trees." <doi:10.1006/aama.2001.0759>.

This package provides a function for plotting maps of agricultural field experiments that are laid out in grids. See Ryder (1981) <doi:10.1017/S0014479700011601>.

Fit latent variable linear models, estimating score distributions for groups of people, following Cohen and Jiang (1999) <doi:10.2307/2669917>. In this model, a latent distribution is conditional on students item response, item characteristics, and conditioning variables the user includes. This latent trait is then integrated out. This software is intended to fit the same models as the existing software AM <https://am.air.org/>. As of version 2, also allows the user to draw plausible values.

Generalised model for population dynamics of invasive Aedes mosquitoes. Rationale and model structure are described here: Da Re et al. (2021) <doi:10.1016/j.ecoinf.2020.101180> and Da Re et al. (2022) <doi:10.1101/2021.12.21.473628>.

Probability mass function, distribution function, quantile function, random generation and estimation for the skew discrete Laplace distributions.