Fetches NCBI data (RefSeq <https://www.ncbi.nlm.nih.gov/refseq/> database) and provides an environment to extract information at the level of gene, mRNA or protein accessions.

Bundles the datasets and functions featured in Philip H. Pollock and Barry C. Edwards<https://edge.sagepub.com/pollock>, "An R Companion to Political Analysis, 3rd Edition," Thousand Oaks, CA: Sage Publications.

Read and write Matlab MAT files from R. The rmatio package supports reading MAT version 4, MAT version 5 and MAT compressed version 5. The rmatio package can write version 5 MAT files and version 5 files with variable compression.

Make your workflow faster and easier. Easily customizable plots (via ggplot2'), nice APA tables (following the style of the *American Psychological Association*) exportable to Word (via flextable'), easily run statistical tests or check assumptions, and automatize various other tasks.

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>.

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.

Facilities for running simulations from ordinary differential equation ('ODE') models, such as pharmacometrics and other compartmental models. A compilation manager translates the ODE model into C, compiles it, and dynamically loads the object code into R for improved computational efficiency. An event table object facilitates the specification of complex dosing regimens (optional) and sampling schedules. NB: The use of this package requires both C and Fortran compilers, for details on their use with R please see Section 6.3, Appendix A, and Appendix D in the "R Administration and Installation" manual. Also the code is mostly released under GPL. The VODE and LSODA are in the public domain. The information is available in the inst/COPYRIGHTS.

This package provides functionality to read files containing observations which consist of arbitrary key/value pairs.

This package provides spatial data analysis functionalities including Exploratory Spatial Data Analysis, Spatial Cluster Detection and Clustering Analysis, Regionalization, etc. based on the C++ source code of GeoDa', which is an open-source software tool that serves as an introduction to spatial data analysis. The GeoDa software and its documentation are available at <https://geodacenter.github.io>.

This package provides a user-friendly interface to NASA Exoplanets Archive API <https://exoplanetarchive.ipac.caltech.edu/>, enabling retrieval and analysis of exoplanetary and stellar data. Includes functions for querying, filtering, summarizing, and computing derived parameters from the Exoplanets catalog.

Exports an Rcpp interface for the Bessel functions in the Bessel package, which can then be called from the C++ code of other packages. For the original Fortran implementation of these functions see Amos (1995) <doi:10.1145/212066.212078>.

This package provides tools for preprocessing and processing canopy photographs with support for raw data reading. Provides methods to address variability in sky brightness and to mitigate errors from image acquisition in non-diffuse light. Works with all types of fish-eye lenses, and some methods also apply to conventional lenses.

This package provides a pair of functions for calculating mean residual life (MRL) , median residual life, and percentile residual life using the outputs of either the flexsurv package or parameters provided by the user. Input information about the distribution, the given life value, the percentile, and the type of residual life, and the function will return your desired values. For the flexsurv option, the function allows the user to input their own data for making predictions. This function is based on Jackson (2016) <doi:10.18637/jss.v070.i08>.

Import REDATAM formats into R via the Open REDATAM C++ library. The full context of this project and details about the implementation are available in <doi:10.1017/dap.2025.4> (Open Access).

This package provides tools to read, write, visualize Protein Data Bank (PDB) files and perform some structural manipulations.

Loading data from AppsFlyer Pull API <https://support.appsflyer.com/hc/en-us/articles/207034346-Using-Pull-API-aggregate-data>.

SyncroSim is a generalized framework for managing scenario-based datasets (<https://syncrosim.com/>). rsyncrosim provides an interface to SyncroSim'. Simulation models can be added to SyncroSim in order to transform these datasets, taking advantage of general features such as defining scenarios of model inputs, running Monte Carlo simulations, and summarizing model outputs. rsyncrosim requires SyncroSim 2.3.5 or higher (API documentation: <https://docs.syncrosim.com/>).

Includes Resourcecode hindcast database (see <https://resourcecode.ifremer.fr>) configuration data: nodes locations for both the sea-state parameters and the spectra data; examples of time series of 1D and 2D surface elevation variance spectral density.

This package provides a solution path for Reinforced Angle-based Multicategory Support Vector Machines, with linear learning, polynomial learning, and Gaussian kernel learning. C. Zhang, Y. Liu, J. Wang and H. Zhu. (2016) <doi:10.1080/10618600.2015.1043010>.

Additional matrix functionality for R including: (1) wrappers for the base matrix function that allow matrices to be created from character strings and lists (the former is especially useful for creating block matrices), (2) better printing of large matrices via the generic "pretty" print function, and (3) a number of convenience functions for users more familiar with other scientific languages like Julia', Matlab'/'Octave', or Python'+'NumPy'.

When assigned "R for Data Science" (Wickham, Ã etinkaya-Rundel, and Grolemund (2023, ISBN: 1492097402)), students should read the book and type in all the associated R commands themselves. Sadly, that never happens. These tutorials allow students to demonstrate (and their instructors to be sure) that all work has been completed. See Kane (2023) <https://ppbds.github.io/tutorial.helpers/articles/instructions.html> from the tutorial.helpers package for a background discussion.

Computes the influence functions time series of the returns for the risk and performance measures as mentioned in Chen and Martin (2018) <https://www.ssrn.com/abstract=3085672>, as well as in Zhang et al. (2019) <https://www.ssrn.com/abstract=3415903>. Also evaluates estimators influence functions at a set of parameter values and plots them to display the shapes of the influence functions.

Simplifies the creation of reproducible data science environments using the Nix package manager, as described in Dolstra (2006) <ISBN 90-393-4130-3>. The included `rix()` function generates a complete description of the environment as a `default.nix` file, which can then be built using Nix'. This results in project specific software environments with pinned versions of R, packages, linked system dependencies, and other tools or programming languages such as Python or Julia. Additional helpers make it easy to run R code in Nix software environments for testing and production.

The Radiant Multivariate menu includes interfaces for perceptual mapping, factor analysis, cluster analysis, and conjoint analysis. The application extends the functionality in radiant.data'.