Move elements between containers in Shiny without explicitly using JavaScript'. It can be used to build custom inputs or to change the positions of user interface elements like plots or tables.

Implementing algorithms and fitting models when sites (possibly remote) share computation summaries rather than actual data over HTTP with a master R process (using opencpu', for example). A stratified Cox model and a singular value decomposition are provided. The former makes direct use of code from the R survival package. (That is, the underlying Cox model code is derived from that in the R survival package.) Sites may provide data via several means: CSV files, Redcap API, etc. An extensible design allows for new methods to be added in the future and includes facilities for local prototyping and testing. Web applications are provided (via shiny') for the implemented methods to help in designing and deploying the computations.

These are data sets for the hit TV show, RuPaul's Drag Race. Data right now include episode-level data, contestant-level data, and episode-contestant-level data. This is a work in progress, and a love letter of a kind to RuPaul's Drag Race and the performers that have appeared on the show. This may not be the most productive use of my time, but I have tenure and what are you going to do about it? I think there is at least some value in this package if it allows the show's fandom to learn more about the R programming language around its contents.

This package provides a tool for hydrologic modelling using the Budyko framework and the Dynamic Water Balance model with Dynamical Dimension Search algorithm to calibrate the model and analyze the outputs from interactive graphics. It allows to calculate the water availability in basins and also some water fluxes represented by the structure of the model. See Zhang, L., N., Potter, K., Hickel, Y., Zhang, Q., Shao (2008) <DOI:10.1016/j.jhydrol.2008.07.021> "Water balance modeling over variable time scales based on the Budyko framework - Model development and testing", Journal of Hydrology, 360, 117â 131. See Tolson, B., C., Shoemaker (2007) <DOI:10.1029/2005WR004723> "Dynamically dimensioned search algorithm for computationally efficient watershed model calibration", Water Resources Research, 43, 1â 16.

While autoregressive distributed lag (ARDL) models allow for extremely flexible dynamics, interpreting substantive significance of complex lag structures remains difficult. This package is designed to assist users in dynamically simulating and plotting the results of various ARDL models. It also contains post-estimation diagnostics, including a test for cointegration when estimating the error-correction variant of the autoregressive distributed lag model (Pesaran, Shin, and Smith 2001 <doi:10.1002/jae.616>).

Compute the dynamic threshold panel model suggested by (Stephanie Kremer, Alexander Bick and Dieter Nautz (2013) <doi:10.1007/s00181-012-0553-9>) in which they extended the (Hansen (1999) <doi: 10.1016/S0304-4076(99)00025-1>) original static panel threshold estimation and the Caner and (Hansen (2004) <doi:10.1017/S0266466604205011>) cross-sectional instrumental variable threshold model, where generalized methods of moments type estimators are used.

Retrieves code comment decorations for C++ languages of the form \\ [[xyz]]', which are used for automated wrapping of C++ functions.

Allows humanitarian community, academia, media, government, and non-governmental organizations to utilize the data collected by the Displacement Tracking Matrix (<https://dtm.iom.int>), a unit in the International Organization for Migration. This also provides non-sensitive Internally Displaced Person figures, aggregated at the country, Admin 1 (states, provinces, or equivalent), and Admin 2 (smaller administrative areas) levels.

By adding over-relaxation factor to PXEM (Parameter Expanded Expectation Maximization) method, the MOPXEM (Monotonically Overrelaxed Parameter Expanded Expectation Maximization) method is obtained. Compare it with the existing EM (Expectation-Maximization)-like methods. Then, distribute and process five methods and compare them, achieving good performance in convergence speed and result quality.The philosophy of the package is described in Guo G. (2022) <doi:10.1007/s00180-022-01270-z>.

This package provides a graphical user interface (GUI) to the functions implemented in the R package DQAstats'. Publication: Mang et al. (2021) <doi:10.1186/s12911-022-01961-z>.

Software accompanying Gary King's book: A Solution to the Ecological Inference Problem. (1997). Princeton University Press. ISBN 978-0691012407.

This package contains a set of clustering methods and evaluation metrics to select the best number of the clusters based on clustering stability. Two references describe the methodology: Fahimeh Nezhadmoghadam, and Jose Tamez-Pena (2021)<doi:10.1016/j.compbiomed.2021.104753>, and Fahimeh Nezhadmoghadam, et al.(2021)<doi:10.2174/1567205018666210831145825>.

Utilities for managing egocentrically sampled network data and a wrapper around the ergm package to facilitate ERGM inference and simulation from such data. See Krivitsky and Morris (2017) <doi:10.1214/16-AOAS1010>.

This package implements choice models based on economic theory, including estimation using Markov chain Monte Carlo (MCMC), prediction, and more. Its usability is inspired by ideas from tidyverse'. Models include versions of the Hierarchical Multinomial Logit and Multiple Discrete-Continous (Volumetric) models with and without screening. The foundations of these models are described in Allenby, Hardt and Rossi (2019) <doi:10.1016/bs.hem.2019.04.002>. Models with conjunctive screening are described in Kim, Hardt, Kim and Allenby (2022) <doi:10.1016/j.ijresmar.2022.04.001>. Models with set-size variation are described in Hardt and Kurz (2020) <doi:10.2139/ssrn.3418383>.

This package provides a non-parametric framework based on estimation statistics principle. Its main purpose is to infer orders of empirical distributions from different categories based on a probability of finding a value in one distribution that is greater than an expectation of another distribution. Given a set of ordered-pair of real-category values the framework is capable of 1) inferring orders of domination of categories and representing orders in the form of a graph; 2) estimating magnitude of difference between a pair of categories in forms of mean-difference confidence intervals; and 3) visualizing domination orders and magnitudes of difference of categories. The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2020) <doi:10.1016/j.heliyon.2020.e05435>.

Data sets for the chapter "Ensemble Postprocessing with R" of the book Stephane Vannitsem, Daniel S. Wilks, and Jakob W. Messner (2018) "Statistical Postprocessing of Ensemble Forecasts", Elsevier, 362pp. These data sets contain temperature and precipitation ensemble weather forecasts and corresponding observations at Innsbruck/Austria. Additionally, a demo with the full code of the book chapter is provided.

Fits Leroux model in spectral domain to estimate causal spatial effect as detailed in Guan, Y; Page, G.L.; Reich, B.J.; Ventrucci, M.; Yang, S; (2020) <arXiv:2012.11767>. Both the parametric and semi-parametric models are available. The semi-parametric model relies on INLA'. The INLA package can be obtained from <https://www.r-inla.org/>.

This dataset contains population estimates of all European cities with at least 10,000 inhabitants during the period 1500-1800. These data are adapted from Jan De Vries, "European Urbanization, 1500-1800" (1984).

This package provides convenience functions for researching experiences including user, customer, patient, employee, and other human experiences. It provides a suite of tools to simplify data exploration such as benchmarking, comparing groups, and checking for differences. The outputs translate statistical approaches in applied experience research to human readable output.

Data and analysis of ecological multilayer networks, including standardization of data structures and functions to convert between them. Includes an interactive multilayer network visualizer (beta, paper forthcoming), and a collection of 78 empirical ecological multilayer network datasets. This work was supported by research grant ISF (Israel Science Foundation) 1281/20 to Shai Pilosof. Noa Frydman (2023) <doi:10.1111/2041-210X.14225>.

Analyses districted electoral systems of any magnitude by computing district-party conversion ratios and seats-to-votes deviations, decomposing the sources of deviation. Traditional indexes are also computed. References: Kedar, O., Harsgor, L. and Sheinerman, R.A. (2016). <doi:10.1111/ajps.12225>. Penades, A and Pavia, J.M. (2025) The decomposition of seats-to-votes distortion in elections: mean, variance, malapportionment and participation''. Acknowledgements: The authors wish to thank Consellerà a de Educación, Cultura, Universidades y Empleo, Generalitat Valenciana (grant CIACO/2023/031) for supporting this research.

Builds contingency tables that cross-tabulate multiple categorical variables and also calculates various summary measures. Export to a variety of formats is supported, including: HTML', LaTeX', and Excel'.

This package provides computational methods for detecting adverse high-order drug interactions from individual case safety reports using statistical techniques, allowing the exploration of higher-order interactions among drug cocktails.

Build entity relationship diagrams (ERD) to specify the nature of the relationship between tables in a database.