According to the order of the loci on the chromosome, the loci can be connected according to the interrelationship between them and classified according to different locus types.

Datasets with energy consumption data of different data measurement frequencies. The data stems from several publicly funded research projects of the Chair of Information Systems and Energy Efficient Systems at the University of Bamberg.

Finds a robust instrumental variables estimator using a high breakdown point S-estimator of multivariate location and scatter matrix.

This package provides functions for studying realized genetic relatedness between people. Users will be able to simulate inheritance patterns given pedigree structures, generate SNP marker data given inheritance patterns, and estimate realized relatedness between pairs of individuals using SNP marker data. See Wang (2017) <doi:10.1534/genetics.116.197004>. This work was supported by National Institutes of Health grants R37 GM-046255.

This package provides a suite of methods to fit and predict case count data using a compartmental SIRS (Susceptible â Infectious â Recovered â Susceptible) model, based on an assumed specification of the effective reproduction number. The significance of this approach is that it relates epidemic progression to the average number of contacts of infected individuals, which decays as a function of the total susceptible fraction remaining in the population. The main functions are pred.curve(), which computes the epidemic curve for a set of parameters, and estimate.mle(), which finds the best fitting curve to observed data. The easiest way to pass arguments to the functions is via a config file, which contains input settings required for prediction, and the package offers two methods, navigate_to_config() which points the user to the configuration file, and re_predict() for starting the fit-predict process. The main model was published in Razvan G. Romanescu et al. <doi:10.1016/j.epidem.2023.100708>.

This package provides a simple and efficient way to read data from Paradox database files (.db) directly into R as modern tibble data frames. It uses the underlying pxlib C library, to handle the low-level file format details and provides a clean, user-friendly R interface.

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.

Optimal linear combination predictive signatures for maximizing the area between two Receiver Operating Characteristic (ROC) curves (treatment vs. control).

To facilitate using cereal with R via cpp11 or Rcpp'. cereal is a header-only C++11 serialization library. cereal takes arbitrary data types and reversibly turns them into different representations, such as compact binary encodings, XML', or JSON'. cereal was designed to be fast, light-weight, and easy to extend - it has no external dependencies and can be easily bundled with other code or used standalone. Please see <https://uscilab.github.io/cereal/> for more information.

Extracts machine-readable variables from natural language text using AI APIs. Optimized for speed and cost efficiency through parallel processing and direct CSV-formatted responses from language models. Supports multiple AI providers with robust error handling and automatic retry mechanisms for failed extractions.

Download and access datasets from the Rdatasets archive (<https://vincentarelbundock.github.io/Rdatasets/>). The package provides functions to search, download, and view documentation for thousands of datasets from various R packages, available in both CSV and Parquet formats for efficient access.

This package provides a robust alternative to the aJIVE (angle based Joint and Individual Variation Explained) method (Feng et al 2018: <doi:10.1016/j.jmva.2018.03.008>) for the estimation of joint and individual components in the presence of outliers in multi-source data. It decomposes the multi-source data into joint, individual and residual (noise) contributions. The decomposition is robust to outliers and noise in the data. The method is illustrated in Ponzi et al (2021) <arXiv:2101.09110>.

Turns nested lists into data.frames in an orderly manner.

Adds menu items to the R Commander for parametric analysis of dichotomous choice contingent valuation (DCCV) data. CV is a question-based survey method to elicit individuals preferences for goods and services. This package depends on functions regarding parametric DCCV analysis in the package DCchoice. See Carson and Hanemann (2005) <doi:10.1016/S1574-0099(05)02017-6> for DCCV.

Easy-to-use functions for downloading air quality data from the Mexican National Air Quality Information System (SINAICA). Allows you to query pollution and meteorological parameters from more than a hundred monitoring stations located throughout Mexico. See <https://sinaica.inecc.gob.mx> for more information.

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

This package provides functions for detecting and analyzing rare events in data. Implements isolation forest (Liu et al., 2008, <doi:10.1109/ICDM.2008.17>) and clustering for anomaly detection in time series residuals. Decomposes time series using LOESS (Locally Estimated Scatterplot Smoothing) or STL (Seasonal-Trend decomposition using LOESS). Detects marine heatwaves and cold spells following Hobday et al. (2016) <doi:10.1016/j.pocean.2015.12.014>. Provides goodness-of-fit tests for quantile regression (Haupt et al., 2011, <doi:10.1080/02664763.2011.573542>), partial dependence with quantile random forests, MCC (Matthews Correlation Coefficient) computation and testing, knee-point detection via the Kneedle algorithm (Satopaa et al., 2011, <doi:10.1109/ICDCSW.2011.20>), and spatial point matching.

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.

The minimum covariance determinant estimator is used to perform robust quadratic discriminant analysis, including cross-validation. References: Friedman J., Hastie T. and Tibshirani R. (2009). "The elements of statistical learning", 2nd edition. Springer, Berlin. <doi:10.1007/978-0-387-84858-7>.

The TRUST4 or MiXCR is used to identify the clonotypes. The goal of rTCRBCRr is to process the results from these clonotyping tools, and analyze the clonotype repertoire metrics based on chain names and IGH isotypes. The manuscript is still under preparation for publication for now. The references describing the methods in this package will be added later.

Reproducibility is essential to the progress of research, yet achieving it remains elusive even in computational fields. Continuous Integration (CI) platforms offer a powerful way to launch automated workflows to check and document code, but often require considerable time, effort, and technical expertise to setup. We therefore developed the rworkflows suite to make robust CI workflows easy and freely accessible to all R package developers. rworkflows consists of 1) a CRAN/Bioconductor-compatible R package template, 2) an R package to quickly implement a standardised workflow, and 3) a centrally maintained GitHub Action.

This package provides a simple approach to configuring R projects with different parameter values. Configurations are specified using a reduced subset of base R and parsed accordingly.

This package provides a programmatic interface to the Species+ <https://speciesplus.net/> database via the Species+/CITES Checklist API <https://api.speciesplus.net/>.

The receiver operating characteristic (ROC) curve is one of the most widely used tools for evaluating diagnostic and prognostic biomarkers across diverse scientific fields, particularly in medicine. Despite its ubiquity, ROC estimation and testing methods differ substantially in their assumptions and resulting curve properties. This package provides a unified framework for constructing, visualizing, and comparing parametric, nonparametric, semiparametric, and Bayesian ROC curves. ROCModels helps researchers identify and implement ROC inference methods most suitable for their data. See the accompanying vignette ROCModels_Package_Doc for a detailed introduction. Alonzo, T. A., and Pepe, M. S. (2002) <doi: 10.1093/biostatistics/3.3.421>, Andrews, D. F., and Herzberg, A. M. (1985) <doi: 10.1007/978-1-4612-5098-2>, Bamber, D. (1975) <doi: 10.1016/0022-2496(75)90001-2>, Cox, D. R. (1972) <doi:10.1111/j.2517-6161.1972.tb00899.x>, Cox, D. R. (1975) <doi: 10.1093/biomet/62.2.269>, DeLong, E. R., DeLong, D. M., and Clarke-Pearson, D. L. (1988) <doi: 10.2307/2531595>, Dorfman, D. D., and Alf, E. (1969) <doi: 10.1016/0022-2496(69)90019-4>, Dorfman, D. D., Berbaum, K. S., and Metz, C. E. (1997) <doi: 10.1016/s1076-6332(97)80013-x>, Erkanli, A., Sung, L., and Stamey, J. D. (2006) <doi: 10.1002/sim.2496>, Faraggi, D., and Reiser, B. (2002) <doi: 10.1002/sim.1228>, Ghebremichael, M., and Habtemicael, S. (2018) <doi: 10.1080/02664763.2017.1420758>, Ghebremichael, M., and Michael, H. (2024) <doi: 10.1080/03610918.2022.2032159>, Ghebremichael, M., Michael, H., Tubbs, J., and Paintsil, E. (2019) <doi: 10.3844/jmssp.2019.55.64>, Gönen, M., and Heller, G. (2010) <doi: 10.1177/0272989X09360067>, Gopalakrishnan, V., Bose, E., Nair, U., Cheng, Y., and Ghebremichael, M. (2020) <doi: 10.1186/s12879-020-05458-w>, Green, D. M., and Swets, J. A. (1966, ISBN:0471324205), Gu, J., and Ghosal, S. (2009) <doi: 10.1016/j.jspi.2008.09.014>, Gu, Y., Ghosal, S., and Roy, A. (2008) <doi: 10.1002/sim.3366>, Guidoum, A. C. (2020) <doi: 10.32614/CRAN.package.kedd>, <doi: 10.48550/arXiv.2012.06102>, Guo, B. (2015) <https://d-scholarship.pitt.edu/23590/1/Guo_Ben_thesis_12-2014.pdf>, Hanley, J. A., and McNeil, B. J. (1982) <doi: 10.1148/radiology.143.1.7063747>, Hsieh, F., and Turnbull, B. W. (1996) <doi: 10.1214/aos/1033066197>, Hussain, E. (2012) <doi: 10.6000/1927-5129.2012.08.02.09>, Ishwaran, H., and James, L. F. (2002) <doi: 10.1198/106186002411>, Jokiel-Rokita, A., and Topolnicki, R. (2020) <doi: 10.1016/j.csda.2019.106820>, Krzanowski, W. J., and Hand, D. J. (2009) <doi: 10.1201/9781439800225>, Kundu, D., and Gupta, R. D. (2006) <doi: 10.1109/TR.2006.874918>, Lloyd, C. J. (1998) <doi: 10.1080/01621459.1998.10473797>, Lehmann, E. L. (1953) <doi: 10.1214/aoms/1177729080>, Metz, C. E., Herman, B. A., and Shen, J. H. (1998) <doi:10.1002/(SICI)1097-0258(19980515)17:9%3C1033::AID-SIM784%3E3.0.CO;2-Z>, Pepe, M. S. (2003) <doi: 10.1093/oso/9780198509844.001.0001>, Pundir, S., and Amala, R. (2014) <doi: 10.22237/jmasm/1398917940>, Silverman, B. W. (2018) <doi: 10.1201/9781315140919>, Yeo, I. K., and Johnson, R. A. (2000) <doi: 10.1093/biomet/87.4.954>, Zhou, X. H., McClish, D. K., and Obuchowski, N. A. (2009) <doi: 10.1002/9780470906514>, Zou, K. H., Hall, W. J., and Shapiro, D. E. (1997) <doi: 10.1002/(SICI)1097-0258(19971015)16:19%3C2143::AID-SIM655%3E3.0.CO;2-3>.