Estimation of k-Order time-varying Mixed Graphical Models and mixed VAR(p) models via elastic-net regularized neighborhood regression. For details see Haslbeck & Waldorp (2020) <doi:10.18637/jss.v093.i08>.

This package provides an implementation of particle swarm optimisation consistent with the standard PSO 2007/2011 by Maurice Clerc. Additionally a number of ancillary routines are provided for easy testing and graphics.

Some functions useful to perform a Peak Over Threshold analysis in univariate and bivariate cases, see Beirlant et al. (2004) <doi:10.1002/0470012382>. A user guide is available in the vignette.

This package provides convenience functions for user experience research with an emphasis on quantitative user experience testing and reporting. The functions are designed to translate statistical approaches to applied user experience research.

Search and download data from over 40 databases hosted by the World Bank, including the World Development Indicators ('WDI'), International Debt Statistics, Doing Business, Human Capital Index, and Sub-national Poverty indicators.

This package provides functions to generate response-surface designs, fit first- and second-order response-surface models, make surface plots, obtain the path of steepest ascent, and do canonical analysis. A good reference on these methods is Chapter 10 of Wu, C-F J and Hamada, M (2009) "Experiments: Planning, Analysis, and Parameter Design Optimization" ISBN 978-0-471-69946-0. An early version of the package is documented in Journal of Statistical Software <doi:10.18637/jss.v032.i07>.

Portfolio optimization is achieved through a combination of regularization techniques and ensemble methods that are designed to generate stable out-of-sample return predictions, particularly in the presence of strong correlations among assets. The package includes functions for data preparation, parallel processing, and portfolio analysis using methods such as Mean-Variance, James-Stein, LASSO, Ridge Regression, and Equal Weighting. It also provides visualization tools and performance metrics, such as the Sharpe ratio, volatility, and maximum drawdown, to assess the results.

This package implements the Bayesian paradigm for fractional polynomial models under the assumption of normally distributed error terms, see Sabanes Bove, D. and Held, L. (2011) <doi:10.1007/s11222-010-9170-7>.

Infrastructure for task views to CRAN-style repositories: Querying task views and installing the associated packages (client-side tools), generating HTML pages and storing task view information in the repository (server-side tools).

This package implements Markowitz Critical Line Algorithm ('CLA') for classical mean-variance portfolio optimization, see Markowitz (1952) <doi:10.2307/2975974>. Care has been taken for correctness in light of previous buggy implementations.

Dynamic graphical models for multivariate time series data to estimate directed dynamic networks in functional magnetic resonance imaging (fMRI), see Schwab et al. (2017) <doi:10.1016/j.neuroimage.2018.03.074>.

Informal implementation of some algorithms from Graph Theory and Combinatorial Optimization which arise in the subject "Graphs and Network Optimization" from first course of the EUPLA degree of Data Engineering in Industrial Processes.

This package provides a dependency free interface to the H3 geospatial indexing system utilizing the Rust library h3o <https://github.com/HydroniumLabs/h3o> via the extendr library <https://github.com/extendr/extendr>.

Java GUI for R - cross-platform, universal and unified Graphical User Interface for R. For full functionality on Windows and Mac OS X JGR requires a start application which depends on your OS.

Facilitates the creation of intuitive figures to describe metabolomics data by utilizing Kyoto Encyclopedia of Genes and Genomes (KEGG) hierarchy data, and gathers functional orthology and gene data from the KEGG-REST API.

Validation of risk predictions obtained from survival models and competing risk models based on censored data using inverse weighting and cross-validation. Most of the pec functionality has been moved to riskRegression'.

Normalization based a subset of negative control probes as described in Subset quantile normalization using negative control features'. Wu Z, Aryee MJ, J Comput Biol. 2010 Oct;17(10):1385-95 [PMID 20976876].

This package performs the change-point detection in regression coefficients of linear model by partitioning the regression coefficients into two classes of smoothness. The change-point and the regression coefficients are jointly estimated.

This package provides a wrapper around a CSS library called vov.css', intended for use in shiny applications. Simply wrap a UI element in one of the animation functions to see it move.

This package provides functions for fitting general linear structural equation models (with observed and latent variables) using the RAM approach, and for fitting structural equations in observed-variable models by two-stage least squares.

These functions were developed to support functional data analysis as described in Ramsay, J. O. and Silverman, B. W. (2005) Functional Data Analysis. The package includes data sets and script files working many examples.

rav1e is an AV1 video encoder. It is designed to eventually cover all use cases, though in its current form it is most suitable for cases where libaom (the reference encoder) is too slow.

Advanced response surface analysis. The main function RSA computes and compares several nested polynomial regression models (full second- or third-order polynomial, shifted and rotated squared difference model, rising ridge surfaces, basic squared difference model, asymmetric or level-dependent congruence effect models). The package provides plotting functions for 3d wireframe surfaces, interactive 3d plots, and contour plots. Calculates many surface parameters (a1 to a5, principal axes, stationary point, eigenvalues) and provides standard, robust, or bootstrapped standard errors and confidence intervals for them.

This package provides a collection of lightweight functions that can be used to determine the computing environment in which your code is running. This includes operating systems, continuous integration (CI) environments, containers, and more.