This package provides functions for connecting to and interfacing with an Arduino or similar device. Functionality includes uploading of sketches, setting and reading digital and analog pins, and rudimentary servo control. This project is not affiliated with the Arduino company, <https://www.arduino.cc/>.

Iterative least cost path and minimum spanning tree methods for projecting forest road networks. The methods connect a set of target points to an existing road network using igraph <https://igraph.org> to identify least cost routes. The cost of constructing a road segment between adjacent pixels is determined by a user supplied weight raster and a weight function; options include the average of adjacent weight raster values, and a function of the elevation differences between adjacent cells that penalizes steep grades. These road network projection methods are intended for integration into R workflows and modelling frameworks used for forecasting forest change, and can be applied over multiple time-steps without rebuilding a graph at each time-step.

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.

This package provides tools for large, sparse optimal matching of treated units and control units in observational studies. Provisions are made for refined covariate balance constraints, which include fine and near-fine balance as special cases. Matches are optimal in the sense that they are computed as solutions to network optimization problems rather than greedy algorithms. See Pimentel, et al.(2015) <doi:10.1080/01621459.2014.997879> and Pimentel (2016), Obs. Studies 2(1):4-23. The rrelaxiv package, which provides an alternative solver for the underlying network flow problems, carries an academic license and is not available on CRAN, but may be downloaded from Github at <https://github.com/josherrickson/rrelaxiv/>.

Simple and fast tool for transforming phytosociological vegetation data into digital form for the following analysis. Danihelka, Chrtek, and Kaplan (2012, ISSN:00327786). Hennekens, and Schaminée (2001) <doi:10.2307/3237010>. Tichý (2002) <doi:10.1111/j.1654-1103.2002.tb02069.x>. Wickham, François, Henry, Müller (2022) <https://CRAN.R-project.org/package=dplyr>.

JDemetra+ (<https://github.com/jdemetra/jdemetra-app>) is the seasonal adjustment software officially recommended to the members of the European Statistical System and the European System of Central Banks. Seasonal adjustment models performed with JDemetra+ can be stored into workspaces. JWSACruncher (<https://github.com/jdemetra/jwsacruncher/releases> for v2 and <https://github.com/jdemetra/jdplus-main/releases> for v3) is a console tool that re-estimates all the multi-processing defined in a workspace and to export the result. rjwsacruncher allows to launch easily the JWSACruncher'.

This package provides functionality to read settings, statuses and readings of weather stations from the ZENTRA Cloud API <https://zentracloud.com/api/v1/guide#APIGuidelines>.

Robust tests (RW, RPB and RGF) are provided for testing the equality of several long-tailed symmetric (LTS) means when the variances are unknown and arbitrary. RW, RPB and RGF tests are robust versions of Welch's F test proposed by Welch (1951) <doi:10.2307/2332579>, parametric bootstrap test proposed by Krishnamoorthy et. al (2007) <doi:10.1016/j.csda.2006.09.039>; and generalized F test proposed by Weerahandi (1995) <doi:10.2307/2532947>;, respectively. These tests are based on the modified maximum likelihood (MML) estimators proposed by Tiku(1967, 1968) <doi:10.2307/2333859>, <doi:10.1080/01621459.1968.11009228>.

Time the execution of overlapping or unique Rcpp code chunks using convenient methods, seamlessly write timing results to an RcppClock object in the R global environment, and summarize and/or plot the results in R.

This package performs Principal Components Analysis (also known as PCA) dimensionality reduction in the context of a linear regression. In most cases, PCA dimensionality reduction is performed independent of the response variable for a regression. This captures the majority of the variance of the model's predictors, but may not actually be the optimal dimensionality reduction solution for a regression against the response variable. An alternative method, optimized for a regression against the response variable, is to use both PCA and a relative importance measure. This package applies PCA to a given data frame of predictors, and then calculates the relative importance of each PCA factor against the response variable. It outputs ordered factors that are optimized for model fit. By performing dimensionality reduction with this method, an individual can achieve a the same r-squared value as performing just PCA, but with fewer PCA factors. References: Yuri Balasanov (2017) <https://ilykei.com>.

This package provides a translation layer between R and CDO operators. Each operator is it's own function with documentation. Nested or piped functions will be translated into CDO chains.

The use of proxies is required in certain network environments. Despite the power of system level software, it is still inconvenient to switch proxy networks at random in R's console. This package is designed to provide one-click switching between proxy and non-proxy states.

REDCap Data Management - REDCap (Research Electronic Data CAPture; <https://projectredcap.org>) is a web application developed at Vanderbilt University, designed for creating and managing online surveys and databases and the REDCap API is an interface that allows external applications to connect to REDCap remotely, and is used to programmatically retrieve or modify project data or settings within REDCap, such as importing or exporting data. REDCapDM is an R package that allows users to manage data exported directly from REDCap or using an API connection. This package includes several functions designed for pre-processing data, generating reports of queries such as outliers or missing values, and following up on previously identified queries.

This package provides a set of tools for creation, manipulation, and modeling of tensors with arbitrary number of modes. A tensor in the context of data analysis is a multidimensional array. rTensor does this by providing a S4 class Tensor that wraps around the base array class. rTensor provides common tensor operations as methods, including matrix unfolding, summing/averaging across modes, calculating the Frobenius norm, and taking the inner product between two tensors. Familiar array operations are overloaded, such as index subsetting via [ and element-wise operations. rTensor also implements various tensor decomposition, including CP, GLRAM, MPCA, PVD, and Tucker. For tensors with 3 modes, rTensor also implements transpose, t-product, and t-SVD, as defined in Kilmer et al. (2013). Some auxiliary functions include the Khatri-Rao product, Kronecker product, and the Hadamard product for a list of matrices.

Downloading, customizing, and processing time series of satellite images for a region of interest. rsat functions allow a unified access to multispectral images from Landsat, MODIS and Sentinel repositories. rsat also offers capabilities for customizing satellite images, such as tile mosaicking, image cropping and new variables computation. Finally, rsat covers the processing, including cloud masking, compositing and gap-filling/smoothing time series of images (Militino et al., 2018 <doi:10.3390/rs10030398> and Militino et al., 2019 <doi:10.1109/TGRS.2019.2904193>).

This package provides a complete interface to LibBi', a library for Bayesian inference (see <https://libbi.org> and Murray, 2015 <doi:10.18637/jss.v067.i10> for more information). This includes functions for manipulating LibBi models, for reading and writing LibBi input/output files, for converting LibBi output to provide traces for use with the coda package, and for running LibBi to conduct inference.

Efficient algorithms for generating ensembles of robust, sparse and diverse models via robust multi-model subset selection (RMSS). The robust ensembles are generated by minimizing the sum of the least trimmed square loss of the models in the ensembles under constraints for the size of the models and the sharing of the predictors. Tuning parameters for the robustness, sparsity and diversity of the robust ensemble are selected by cross-validation.

Offers a suite of tools designed to enhance the responsiveness and interactivity of web-based documents and applications created with R. It provides an automatic, configurable resizing toolbar that can be seamlessly integrated with HTML elements such as containers, images, and tables, allowing end-users to dynamically adjust their dimensions. Beyond the toolbar, the package includes a rich collection of flexible, expandable, and interactive container functionalities, such as highly customizable split-screen layouts (splitCard), versatile sizeable cards (sizeableCard), dynamic window-like elements (windowCard), visually engaging emphasis cards (empahsisCard), and sophisticated flexible and elastic card layouts (flexCard, elastiCard). Furthermore, it offers an elegant image viewer and resizer (shinyExpandImage) perfect for interactive galleries. r2resize is particularly well-suited for developers and data scientists looking to create modern, responsive, and user-friendly shiny applications, markdown reports, and quarto documents that adapt gracefully to different screen sizes and user preferences, significantly improving the user experience.

Processes standard recommendation datasets (e.g., a user-item rating matrix) as input and generates rating predictions and lists of recommended items. Standard algorithm implementations which are included in this package are the following: Global/Item/User-Average baselines, Weighted Slope One, Item-Based KNN, User-Based KNN, FunkSVD, BPR and weighted ALS. They can be assessed according to the standard offline evaluation methodology (Shani, et al. (2011) <doi:10.1007/978-0-387-85820-3_8>) for recommender systems using measures such as MAE, RMSE, Precision, Recall, F1, AUC, NDCG, RankScore and coverage measures. The package (Coba, et al.(2017) <doi: 10.1007/978-3-319-60042-0_36>) is intended for rapid prototyping of recommendation algorithms and education purposes.

The RBP curve is a visual tool to assess the performance of prediction models.

Use R to interface with the TD Ameritrade API <https://developer.tdameritrade.com/>. Functions include authentication, trading, price requests, account information, and option chains. A user will need a TD brokerage account and TD Ameritrade developer app. See README for authentication process and examples.

This tool enables the user to choose a randomization procedure based on sound scientific criteria. It comprises the generation of randomization sequences as well the assessment of randomization procedures based on carefully selected criteria. Furthermore, randomizeR provides a function for the comparison of randomization procedures.

This package provides a robust and powerful approach is developed for replicability analysis of two Genome-wide association studies (GWASs) accounting for the linkage disequilibrium (LD) among genetic variants. The LD structure in two GWASs is captured by a four-state hidden Markov model (HMM). The unknowns involved in the HMM are estimated by an efficient expectation-maximization (EM) algorithm in combination with a non-parametric estimation of functions. By incorporating information from adjacent locations via the HMM, this approach identifies the entire clusters of genotype-phenotype associated signals, improving the power of replicability analysis while effectively controlling the false discovery rate.

This package provides estimation and inference procedures for boundary regression discontinuity (RD) designs using local polynomial methods, based on either bivariate coordinates or distance-based approaches. Methods are developed in Cattaneo, Titiunik, and Yu (2025) <https://mdcattaneo.github.io/papers/Cattaneo-Titiunik-Yu_2025_BoundaryRD.pdf>.