This package performs model-free reinforcement learning in R. This implementation enables the learning of an optimal policy based on sample sequences consisting of states, actions and rewards. In addition, it supplies multiple predefined reinforcement learning algorithms, such as experience replay. Methodological details can be found in Sutton and Barto (1998) <ISBN:0262039249>.

Generate basic charts either by custom applications, or from a small script launched from the system console, or within the R console. Two ASCII text files are necessary: (1) The graph parameters file, which name is passed to the function rplotengine()'. The user can specify the titles, choose the type of the graph, graph output formats (e.g. png, eps), proportion of the X-axis and Y-axis, position of the legend, whether to show or not a grid at the background, etc. (2) The data to be plotted, which name is specified as a parameter ('data_filename') in the previous file. This data file has a tabulated format, with a single character (e.g. tab) between each column. Optionally, the file could include data columns for showing confidence intervals.

Proper L2-penalized maximum likelihood estimators for precision matrices and supporting functions to employ these estimators in a graphical modeling setting. For details, see Peeters, Bilgrau, & van Wieringen (2022) <doi:10.18637/jss.v102.i04> and associated publications.

Calculates rainfall erosivity from timestamped precipitation records. The package identifies rainfall events, calculates continuous rolling rainfall intensities, rainfall kinetic energy, and event EI30 erosivity, aggregates contributing event erosivity to monthly and yearly totals, and calculates multi-year mean monthly and annual rainfall erosivity. Storm separation, event omission criteria, intensity durations, and rainfall kinetic-energy equations are configurable. Rainfall kinetic energy can be calculated using the formulations of Brown and Foster (1987) <doi:10.13031/2013.31957>, McGregor et al. (1995) <doi:10.13031/2013.27921>, and Laws and Parsons (1943) <doi:10.1029/TR024i002p00452>.

This package contains functions to create regulatory-style statistical reports. Originally designed to create tables, listings, and figures for the pharmaceutical, biotechnology, and medical device industries, these reports are generalized enough that they could be used in any industry. Generates text, rich-text, PDF, HTML, and Microsoft Word file formats. The package specializes in printing wide and long tables with automatic page wrapping and splitting. Reports can be produced with a minimum of function calls, and without relying on other table packages. The package supports titles, footnotes, page header, page footers, spanning headers, page by variables, and automatic page numbering.

This package provides tools for grading the coding style and documentation of R scripts. This is the R component of Roger the Omni Grader, an automated grading system for computer programming projects based on Unix shell scripts; see <https://gitlab.com/roger-project>. The package also provides an R interface to the shell scripts. Inspired by the lintr package.

Create densities, probabilities, random numbers, quantiles, and maximum likelihood estimation for several distributions, mainly the symmetric and asymmetric power exponential (AEP), a.k.a. the Subbottin family of distributions, also known as the generalized error distribution. Estimation is made using the design of Bottazzi (2004) <https://ideas.repec.org/p/ssa/lemwps/2004-14.html>, where the likelihood is maximized by several optimization procedures using the GNU Scientific Library (GSL)', translated to C++ code, which makes it both fast and accurate. The package also provides methods for the gamma, Laplace, and Asymmetric Laplace distributions.

Utilities for reading, writing, and managing RCDF files, including encryption and decryption support. It offers a flexible interface for handling data stored in encrypted Parquet format, along with metadata extraction, key management, and secure operations using AES and RSA encryptions.

Interface to libKriging C++ library <https://github.com/libKriging> that should provide most standard Kriging / Gaussian process regression features (like in DiceKriging', kergp or RobustGaSP packages). libKriging relies on Armadillo linear algebra library (Apache 2 license) by Conrad Sanderson, lbfgsb_cpp is a C++ port around by Pascal Have of lbfgsb library (BSD-3 license) by Ciyou Zhu, Richard Byrd, Jorge Nocedal and Jose Luis Morales used for hyperparameters optimization.

Random forest with a variety of additional features for regression, classification and survival analysis. The features include: parallel computing with OpenMP, embedded model for selecting the splitting variable, based on Zhu, Zeng & Kosorok (2015) <doi:10.1080/01621459.2015.1036994>, subject weight, variable weight, tracking subjects used in each tree, etc.

Fits an Ising model to a binary dataset using L1 regularized logistic regression and extended BIC. Also includes a fast lasso logistic regression function for high-dimensional problems. Uses the libLBFGS optimization library by Naoaki Okazaki.

Retrieve, map and summarize data from the VertNet.org archives (<https://vertnet.org/>). Functions allow searching by many parameters, including taxonomic names, places, and dates. In addition, there is an interface for conducting spatially delimited searches, and another for requesting large datasets via email.

Build robust and maintainable software with object-oriented design patterns in R. Design patterns abstract and present in neat, well-defined components and interfaces the experience of many software designers and architects over many years of solving similar problems. These are solutions that have withstood the test of time with respect to re-usability, flexibility, and maintainability. R6P provides abstract base classes with examples for a few known design patterns. The patterns were selected by their applicability to analytic projects in R. Using these patterns in R projects have proven effective in dealing with the complexity that data-driven applications possess.

This package provides algorithms to locate multiple distributional change-points in piecewise stationary time series. The algorithms are provably consistent, even in the presence of long-range dependencies. Knowledge of the number of change-points is not required. The code is written in Go and interfaced with R.

Work with the Macrostrat (<https://macrostrat.org/>) Web Service (v.2, <https://macrostrat.org/api/v2>) to fetch geological data relevant to the spatial and temporal distribution of sedimentary, igneous, and metamorphic rocks as well as data extracted from them.

This package provides a structural, reproducible workflow for the processing and analysis of respirometry data. It contains analytical functions and utilities for working with oxygen time-series to determine respiration or oxygen production rates, and to make it easier to report and share analyses. See Harianto et al. 2019 <doi:10.1111/2041-210X.13162>.

Downloads and imports agricultural data from the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) <https://www.agriculture.gov.au/abares> and the Australian Bureau of Statistics (ABS) <https://www.abs.gov.au>. Supports multiple data formats including spreadsheets, commaâ separated value (CSV) files, and geospatial data such as shapefiles and GeoTIFFs. Covers topics such as broadacre crops, livestock, soils, commodities and related agricultural information. The package standardises field names and data formats to improve interoperability and simplify analysis. It also streamlines the import of geospatial data and corrects common issues found in these data sources upon loading.

Calculates relevance and significance values for simple models and for many types of regression models. These are introduced in Stahel, Werner A. (2021) "Measuring Significance and Relevance instead of p-values." <https://stat.ethz.ch/~stahel/relevance/stahel-relevance2103.pdf>. These notions are also applied to replication studies, as described in the manuscript Stahel, Werner A. (2022) "'Replicability': Terminology, Measuring Success, and Strategy" available in the documentation.

Connector to the REST API of a Rock R server, to perform operations on a remote R server session, or administration tasks. See Rock documentation at <https://rockdoc.obiba.org/>.

An implementation of the radviz projection in R. It enables the visualization of multidimensional data while maintaining the relation to the original dimensions. This package provides functions to create and plot radviz projections, and a number of summary plots that enable comparison and analysis. For reference see Hoffman *et al.* (1999) (<doi:10.1145/331770.331775>) for original implementation, see Di Caro *et al* (2012) (<doi:10.1007/978-3-642-13672-6_13>), for the original method for dimensional anchor arrangements, see Demsar *et al.* (2007) (<doi:10.1016/j.jbi.2007.03.010>) for the original Freeviz implementation.

Leverages the functionality of clipboard.js', a JavaScript library for HMTL5-based copy to clipboard from web pages (see <https://clipboardjs.com> for more information), and provides a reactive copy-to-clipboard UI button component, called rclipButton', and a a reactive copy-to-clipboard UI link component, called rclipLink', for shiny R applications.

Wrapper and plotting utilities for the spatial population genetics tool EEMS (Estimated Effective Migration Surfaces) for SNP (Single Nucleotide Polymorphism) data, originally provided as a command-line tool written in C++ together with an accompanying R package for plotting the output of the EEMS tool itself (<https://github.com/dipetkov/eems/>). There are four main motivations for offering this to R users as a package. Firstly, to remove the installation and configuration burden for the EEMS command-line tool, which relies on manually installed Boost and Eigen system libraries and configuring their location; secondly, to streamline the workflow by having a singe environment (the R system) for the entire analysis rather than a file-based command-line executable whose output files are then to be imported and analysed by a separate R script; thirdly, to make the input formats compatible with other, R'-based spatial population genetics tools such as the ConStruct package; and lastly, to allow for easily running several chains in parallel and combining them for plotting and further analysis. The package also adds more intuitive, streamlined tooling around creating more complex habitats. The method of estimating effective migration surfaces was first described by Petkova, D., Novembre, J. & Stephens, M. (2016) <doi:10.1038/ng.3464>.

The ropenblas package (<https://prdm0.github.io/ropenblas/>) is useful for users of any GNU/Linux distribution. It will be possible to download, compile and link the OpenBLAS library (<https://www.openblas.net/>) with the R language, always by the same procedure, regardless of the GNU/Linux distribution used. With the ropenblas package it is possible to download, compile and link the latest version of the OpenBLAS library even the repositories of the GNU/Linux distribution used do not include the latest versions of OpenBLAS'. If of interest, older versions of the OpenBLAS library may be considered. Linking R with an optimized version of BLAS (<https://netlib.org/blas/>) may improve the computational performance of R code. The OpenBLAS library is an optimized implementation of BLAS that can be easily linked to R with the ropenblas package.

Makes easier the creation of R package or research compendium (i.e. a predefined files/folders structure) so that users can focus on the code/analysis instead of wasting time organizing files. A full ready-to-work structure is set up with some additional features: version control, remote repository creation, CI/CD configuration (check package integrity under several OS, test code with testthat', and build and deploy website using pkgdown'). This package heavily relies on the R packages devtools and usethis and follows recommendations made by Wickham H. (2015) <ISBN:9781491910597> and Marwick B. et al. (2018) <doi:10.7287/peerj.preprints.3192v2>.