Add-in to the RJDemetra package on seasonal adjustments. It allows to produce dashboards to summarise models and quickly check the quality of the seasonal adjustment.

This package provides a simple R -> Stata interface allowing the user to execute Stata commands (both inline and from a .do file) from R.

This package provides access to ArcGIS geoprocessing tools by building an interface between R and the ArcPy Python side-package via the reticulate package.

Oracle Database interface (DBI) driver for R. This is a DBI-compliant Oracle driver based on the OCI.

MCMC based sampling of binary matrices with fixed margins as used in exact Rasch model tests.

An easy way to get started with Generative Adversarial Nets (GAN) in R. The GAN algorithm was initially described by Goodfellow et al. 2014 <https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf>. A GAN can be used to learn the joint distribution of complex data by comparison. A GAN consists of two neural networks a Generator and a Discriminator, where the two neural networks play an adversarial minimax game. Built-in GAN models make the training of GANs in R possible in one line and make it easy to experiment with different design choices (e.g. different network architectures, value functions, optimizers). The built-in GAN models work with tabular data (e.g. to produce synthetic data) and image data. Methods to post-process the output of GAN models to enhance the quality of samples are available.

This package provides an interface with the Wildbook mark-recapture ecological database framework. It helps users to pull data from the Wildbook framework and format data for further analysis with mark-recapture applications like Program MARK (which can be accessed via the RMark package in R'). Further information on the Wildbook framework is available at: <http://www.wildbook.org/doku.php>.

Fast computing an ensemble of rank-based trees via boosting or random forest on binary and multi-class problems. It converts continuous gene expression profiles into ranked gene pairs, for which the variable importance indices are computed and adopted for dimension reduction. Decision rules can be extracted from trees.

Get the category of content hosted by a domain. Use Shallalist <http://shalla.de/>, Virustotal (which provides access to lots of services) <https://www.virustotal.com/>, Alexa <https://aws.amazon.com/awis/>, DMOZ <https://curlie.org/>, University Domain list <https://github.com/Hipo/university-domains-list> or validated machine learning classifiers based on Shallalist data to learn about the kind of content hosted by a domain.

Import Data from Relational Database Management Systems (RDBMS) and Health Information Systems ('HIS'). The current version of the package supports importing data from RDBMS including MS SQL', MySQL', PostGRESQL', and SQLite', as well as from two HIS platforms: DHIS2 and SORMAS'.

Plots the Receiver Operating Characteristics Surface for high-throughput class-skewed data, calculates the Volume under the Surface (VUS) and the FDR-Controlled Area Under the Curve (FCAUC), and conducts tests to compare two ROC surfaces. Computes eROC curve and the corresponding AUC for imperfect reference standard.

Rcmdr menu support for many of the functions in the HH package. The focus is on menu items for functions we use in our introductory courses.

This package provides efficient functions for detecting multiple change points in multidimensional time series. The models can be piecewise constant or polynomial. Adaptive threshold selection methods are available, see Fan and Wu (2024) <arXiv:2403.00600>.

Univariate and multivariate methods to analyze randomized response (RR) survey designs (e.g., Warner, S. L. (1965). Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association, 60, 63â 69, <doi:10.2307/2283137>). Besides univariate estimates of true proportions, RR variables can be used for correlations, as dependent variable in a logistic regression (with or without random effects), or as predictors in a linear regression (Heck, D. W., & Moshagen, M. (2018). RRreg: An R package for correlation and regression analyses of randomized response data. Journal of Statistical Software, 85(2), 1â 29, <doi:10.18637/jss.v085.i02>). For simulations and the estimation of statistical power, RR data can be generated according to several models. The implemented methods also allow to test the link between continuous covariates and dishonesty in cheating paradigms such as the coin-toss or dice-roll task (Moshagen, M., & Hilbig, B. E. (2017). The statistical analysis of cheating paradigms. Behavior Research Methods, 49, 724â 732, <doi:10.3758/s13428-016-0729-x>).

Hybrid Mortality Modelling (HMM) provides a framework in which mortality around "the accident hump" and at very old ages can be modelled under a single model. The graphics codes necessary for visualization of the models output are included here. Specifically, the graphics are based on the assumption that, the mortality rates can be expressed as a function of the area under the curve between the crude mortality rates plots and the tangential transform of the force of mortality.

This package provides popular sampling distributions C++ routines based in armadillo through a header file approach.

Data for the examples and exercises in the book "R by Example". Jim Albert and Maria Rizzo (2012, ISBN 978-1-4614-1365-3).

Create production-ready Rich Text Format (RTF) tables and figures with flexible format.

Wrapper for Datamuse API to find rhyming and other associated words. This includes words of similar meaning, spelling, or other related words. Learn more about the Datamuse API here <https://www.datamuse.com/api/>.

Ranked set sampling (RSS) is introduced as an advanced method for data collection which is substantial for the statistical and methodological analysis in scientific studies by McIntyre (1952) (reprinted in 2005) <doi:10.1198/000313005X54180>. This package introduces the first package that implements the RSS and its modified versions for sampling. With RSSampling', the researchers can sample with basic RSS and the modified versions, namely, Median RSS, Extreme RSS, Percentile RSS, Balanced groups RSS, Double RSS, L-RSS, Truncation-based RSS, Robust extreme RSS. The RSSampling also allows imperfect ranking using an auxiliary variable (concomitant) which is widely used in the real life applications. Applicants can also use this package for parametric and nonparametric inference such as mean, median and variance estimation, regression analysis and some distribution-free tests where the the samples are obtained via basic RSS.

This package provides a dataset of functions in all base and recommended packages of R versions 0.50 onwards.

The kappa statistic implemented by Fleiss is a very popular index for assessing the reliability of agreement among multiple observers. It is used both in the psychological and in the psychiatric field. Other fields of application are typically medicine, biology and engineering. Unfortunately,the kappa statistic may behave inconsistently in case of strong agreement between raters, since this index assumes lower values than it would have been expected. We propose a modification kappa implemented by Fleiss in case of nominal and ordinal variables. Monte Carlo simulations are used both to testing statistical hypotheses and to calculating percentile bootstrap confidence intervals based on proposed statistic in case of nominal and ordinal data.

This tool can be used to build binary interval trees using real number inputs. The tree supports queries of intervals overlapping a single number or an interval (start, end). Intervals with same bounds but different names are treated as distinct intervals. Insertion of intervals is also allowed. Deletion of intervals is not implemented at this point. See Mark de Berg, Otfried Cheong, Marc van Kreveld, Mark Overmars (2008). Computational Geometry: Algorithms and Applications, for a reference.

Streamlines the creation of reproducible analytical pipelines using default.nix expressions generated via the rix package for reproducibility. Define derivations in R', Python or Julia', chain them into a composition of pure functions and build the resulting pipeline using Nix as the underlying end-to-end build tool. Functions to plot the pipeline as a directed acyclic graph are included, as well as functions to load and inspect intermediary results for interactive analysis. User experience heavily inspired by the targets package.