Graphical methods for compactly illustrating probability distributions, including density strips, density regions, sectioned density plots and varying width strips, using base R graphics. Note that the ggdist package offers a similar set of tools for illustrating distributions, based on ggplot2'.

Fits, bootstraps, and evaluates two-component normal and lognormal mixture models. Includes diagnostic plots and statistical evaluation of mixture model fits using differential evolution optimization.

We provide 70 data sets of females of reproductive age from 19 Asian countries, ranging in age from 15 to 49. The data sets are extracted from demographic and health surveys that were conducted over an extended period of time. Moreover, the functions also provide Whippleâ s index as well as age reporting quality such as very rough, rough, approximate, accurate, and highly accurate.

Distribution (S4-)classes for elliptically contoured distributions (based on package distr').

Functionality for manipulating values of associative maps. The package is a dependency for mvp-type packages that use the STL map class: it traps plausible idiom that is ill-defined (implementation-specific) and returns an informative error, rather than returning a possibly incorrect result. To cite the package in publications please use Hankin (2022) <doi:10.48550/ARXIV.2210.03856>.

Identifies code blocks that have a high level of similarity within a set of R files.

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>.

This package provides density functions for the joint distribution of choice, response time and confidence for discrete confidence judgments as well as functions for parameter fitting, prediction and simulation for various dynamical models of decision confidence. All models are explained in detail by Hellmann et al. (2023; Preprint available at <https://osf.io/9jfqr/>, published version: <doi:10.1037/rev0000411>). Implemented models are the dynaViTE model, dynWEV model, the 2DSD model (Pleskac & Busemeyer, 2010, <doi:10.1037/a0019737>), and various race models. C++ code for dynWEV and 2DSD is based on the rtdists package by Henrik Singmann.

Simplifies and automates the process of exploring and merging data from relational databases. This package allows users to discover table relationships, create a map of all possible joins, and generate executable plans to merge data based on a structured metadata framework.

An R interface to the codediff JavaScript library (a copy of which is included in the package, see <https://github.com/danvk/codediff.js> for information). Allows for visualization of the difference between 2 files, usually text files or R scripts, in a browser.

Estimates a variety of Dynamic Conditional Correlation (DCC) models. More in detail, the dccmidas package allows the estimation of the corrected DCC (cDCC) of Aielli (2013) <doi:10.1080/07350015.2013.771027>, the DCC-MIDAS of Colacito et al. (2011) <doi:10.1016/j.jeconom.2011.02.013>, the Asymmetric DCC of Cappiello et al. <doi:10.1093/jjfinec/nbl005>, and the Dynamic Equicorrelation (DECO) of Engle and Kelly (2012) <doi:10.1080/07350015.2011.652048>. dccmidas offers the possibility of including standard GARCH <doi:10.1016/0304-4076(86)90063-1>, GARCH-MIDAS <doi:10.1162/REST_a_00300> and Double Asymmetric GARCH-MIDAS <doi:10.1016/j.econmod.2018.07.025> models in the univariate estimation. Moreover, also the scalar and diagonal BEKK <doi:10.1017/S0266466600009063> models can be estimated. Finally, the package calculates also the var-cov matrix under two non-parametric models: the Moving Covariance and the RiskMetrics specifications.

Extends package distr by functionals, distances, and conditional distributions.

Implementation of DetMCD, a new algorithm for robust and deterministic estimation of location and scatter. The benefits of robust and deterministic estimation are explained in Hubert, Rousseeuw and Verdonck (2012) <doi:10.1080/10618600.2012.672100>.

This package provides methods for reading, displaying, processing and writing files originally arranged for the DSSAT-CSM fixed width format. The DSSAT-CSM cropping system model is described at J.W. Jones, G. Hoogenboomb, C.H. Porter, K.J. Boote, W.D. Batchelor, L.A. Hunt, P.W. Wilkens, U. Singh, A.J. Gijsman, J.T. Ritchie (2003) <doi:10.1016/S1161-0301(02)00107-7>.

Shows you which rows have changed between two data frames with the same column structure. Useful for diffing slowly mutating data.

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

This package contains functions that check for formatting of the Subject Phenotype data set and data dictionary as specified by the National Center for Biotechnology Information (NCBI) Database of Genotypes and Phenotypes (dbGaP) <https://www.ncbi.nlm.nih.gov/gap/docs/submissionguide/>.

This package provides functions for (1) ranking, selecting, and prioritising genes, proteins, and metabolites from high dimensional biology experiments, (2) multivariate hit calling in high content screens, and (3) combining data from diverse sources.

Utilities for mixed frequency data. In particular, use to aggregate and normalize tabular mixed frequency data, index dates to end of period, and seasonally adjust tabular data.

This package provides a system for the management, assessment, and psychometric analysis of data from educational and psychological tests.

Testing and documenting code that communicates with remote databases can be painful. Although the interaction with R is usually relatively simple (e.g. data(frames) passed to and from a database), because they rely on a separate service and the data there, testing them can be difficult to set up, unsustainable in a continuous integration environment, or impossible without replicating an entire production cluster. This package addresses that by allowing you to make recordings from your database interactions and then play them back while testing (or in other contexts) all without needing to spin up or have access to the database your code would typically connect to.

This package provides a non-drawing graphic device for benchmarking purpose. In order to properly benchmark graphic drawing code it is necessary to factor out the device implementation itself so that results are not related to the specific graphics device used during benchmarking. The devoid package implements a graphic device that accepts all the required calls from R's graphic engine but performs no action. Apart from benchmarking it is unlikely that this device has any practical use.

Statistical hypothesis testing of pattern heterogeneity via differences in underlying distributions across multiple contingency tables. Five tests are included: the comparative chi-squared test (Song et al. 2014) <doi:10.1093/nar/gku086> (Zhang et al. 2015) <doi:10.1093/nar/gkv358>, the Sharma-Song test (Sharma et al. 2021) <doi:10.1093/bioinformatics/btab240>, the heterogeneity test, the marginal-change test (Sharma et al. 2020) <doi:10.1145/3388440.3412485>, and the strength test (Sharma et al. 2020) <doi:10.1145/3388440.3412485>. Under the null hypothesis that row and column variables are statistically independent and joint distributions are equal, their test statistics all follow an asymptotically chi-squared distribution. A comprehensive type analysis categorizes the relation among the contingency tables into type null, 0, 1, and 2 (Sharma et al. 2020) <doi:10.1145/3388440.3412485>. They can identify heterogeneous patterns that differ in either the first order (marginal) or the second order (differential departure from independence). Second-order differences reveal more fundamental changes than first-order differences across heterogeneous patterns.

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>.