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

Combine multiple data files from a common directory. The data files will be read into R and bound together, creating a single large data.frame. A general function is provided along with a specific function for data that was collected using the open-source experiment builder OpenSesame <https://osdoc.cogsci.nl/>.

Designed for longitudinal data analysis using Hidden Markov Models (HMMs). Tailored for applications in healthcare, social sciences, and economics, the main emphasis of this package is on regularization techniques for fitting HMMs. Additionally, it provides an implementation for fitting HMMs without regularization, referencing Zucchini et al. (2017, ISBN:9781315372488).

Focused on (but not exclusive to) data sets hosted on PhysioNet (<https://physionet.org>), ricu provides utilities for download, setup and access of intensive care unit (ICU) data sets. In addition to functions for running arbitrary queries against available data sets, a system for defining clinical concepts and encoding their representations in tabular ICU data is presented.

Generate random user data from the Random User Generator API. For more information, see <https://randomuser.me/>.

This package provides a research infrastructure to develop and evaluate collaborative filtering recommender algorithms. This includes a sparse representation for user-item matrices, many popular algorithms, top-N recommendations, and cross-validation. Hahsler (2022) <doi:10.48550/arXiv.2205.12371>.

This package provides methods for calculation and visualization of the Repertoire Dissimilarity Index. Citation: Bolen and Rubelt, et al (2017) <doi:10.1186/s12859-017-1556-5>.

Presentation-ready results tables for epidemiologists in an automated, reproducible fashion. The user provides the final analytical dataset and specifies the design of the table, with rows and/or columns defined by exposure(s), effect modifier(s), and estimands as desired, allowing to show descriptors and inferential estimates in one table -- bridging the rift between epidemiologists and their data, one table at a time. See Rothman (2017) <doi:10.1007/s10654-017-0314-3>.

In order to make sure that web request ends up in the correct handler function a router is often used. routr is a package implementing a simple but powerful routing functionality for R based servers. It is a fully functional fiery plugin, but can also be used with other httpuv based servers.

We develop the entire solution paths for ROC-SVM presented by Rakotomamonjy. The ROC-SVM solution path algorithm greatly facilitates the tuning procedure for regularization parameter, lambda in ROC-SVM by avoiding grid search algorithm which may be computationally too intensive. For more information on the ROC-SVM, see the report in the ROC Analysis in AI workshop(ROCAI-2004) : Hernà ndez-Orallo, José, et al. (2004) <doi:10.1145/1046456.1046489>.

This package provides methods and tools for Singular Spectrum Analysis including decomposition, forecasting and gap-filling for univariate and multivariate time series. General description of the methods with many examples can be found in the book Golyandina (2018, <doi:10.1007/978-3-662-57380-8>). See citation("Rssa") for details.

Create plots to visualize the alignment of a corporate lending financial portfolio to climate change scenarios based on climate indicators (production and emission intensities) across key climate relevant sectors of the PACTA methodology (Paris Agreement Capital Transition Assessment; <https://www.transitionmonitor.com/>). Financial institutions use PACTA to study how their capital allocation decisions align with climate change mitigation goals.

Build regular expressions piece by piece using human readable code. This package contains core functionality, and is primarily intended to be used by package developers.

Queries data from WHOIS servers.

Implementation of the RESTK algorithm based on Markov's Inequality from Vilardell, Sergi, Serra, Isabel, Mezzetti, Enrico, Abella, Jaume, Cazorla, Francisco J. and Del Castillo, J. (2022). "Using Markov's Inequality with Power-Of-k Function for Probabilistic WCET Estimation". In 34th Euromicro Conference on Real-Time Systems (ECRTS 2022). Leibniz International Proceedings in Informatics (LIPIcs) 231 20:1-20:24. <doi:10.4230/LIPIcs.ECRTS.2022.20>. This work has been supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 772773).

In repeated measures studies with extreme large or small values it is common that the subjects measurements on average are closer to the mean of the basic population. Interpreting possible changes in the mean in such situations can lead to biased results since the values were not randomly selected, they come from truncated sampling. This method allows to estimate the range of means where treatment effects are likely to occur when regression toward the mean is present. Ostermann, T., Willich, Stefan N. & Luedtke, Rainer. (2008). Regression toward the mean - a detection method for unknown population mean based on Mee and Chua's algorithm. BMC Medical Research Methodology.<doi:10.1186/1471-2288-8-52>. Acknowledgments: We would like to acknowledge "Lena Roth" and "Nico Steckhan" for the package's initial updates (Q3 2024) and continued supervision and guidance. Both have contributed to discussing and integrating these methods into the package, ensuring they are up-to-date and contextually relevant.

An approach to age-depth modelling that uses Bayesian statistics to reconstruct accumulation histories for deposits, through combining radiocarbon and other dates with prior information on accumulation rates and their variability. See Blaauw & Christen (2011).

Interface to use and access Wilensky's NetLogo (Wilensky 1999) from R using either headless (no GUI) or interactive GUI mode. Provides functions to load models, execute commands, and get values from reporters. Mostly analogous to the NetLogo Mathematica Link <https://github.com/NetLogo/Mathematica-Link>.

Enhances the R Optimization Infrastructure ('ROI') package with the DEoptim and DEoptimR package. DEoptim is used for unconstrained optimization and DEoptimR for constrained optimization.

Analyze download logs from the CRAN RStudio mirror (<http://cran.rstudio.com/>). This CRAN mirror is the default one used in RStudio. The available data is the result of parsed and anonymised raw log data from that CRAN mirror.

We provide an implementation for Sum of Ranking Differences (SRD), a novel statistical test introduced by Héberger (2010) <doi:10.1016/j.trac.2009.09.009>. The test allows the comparison of different solutions through a reference by first performing a rank transformation on the input, then calculating and comparing the distances between the solutions and the reference - the latter is measured in the L1 norm. The reference can be an external benchmark (e.g. an established gold standard) or can be aggregated from the data. The calculated distances, called SRD scores, are validated in two ways, see Héberger and Kollár-Hunek (2011) <doi:10.1002/cem.1320>. A randomization test (also called permutation test) compares the SRD scores of the solutions to the SRD scores of randomly generated rankings. The second validation option is cross-validation that checks whether the rankings generated from the solutions come from the same distribution or not. For a detailed analysis about the cross-validation process see Sziklai, Baranyi and Héberger (2021) <doi:10.48550/arXiv.2105.11939>. The package offers a wide array of features related to SRD including the computation of the SRD scores, validation options, input preprocessing and plotting tools.

ARFIMA, in-mean, external regressors and various GARCH flavors, with methods for fit, forecast, simulation, inference and plotting.

Inference of relatedness coefficients from a bi-allelic genotype matrix using a Maximum Likelihood estimation, Laporte, F., Charcosset, A. and Mary-Huard, T. (2017) <doi:10.1111/biom.12634>.

Estimation of the conditional covariance matrix using the RiskMetrics 2006 methodology of Zumbach (2007) <doi:10.2139/ssrn.1420185>.