Implementation of the tests for rotational symmetry on the hypersphere proposed in Garcà a-Portugués, Paindaveine and Verdebout (2020) <doi:10.1080/01621459.2019.1665527>. The package also implements the proposed distributions on the hypersphere, based on the tangent-normal decomposition, and allows for the replication of the data application considered in the paper.

Designed to streamline data analysis and statistical testing, reducing the length of R scripts while generating well-formatted outputs in pdf', Microsoft Word', and Microsoft Excel formats. In essence, the package contains functions which are sophisticated wrappers around existing R functions that are called by using f_ (user f_riendly) prefix followed by the normal function name. This third version of the rfriend package focuses primarily on data exploration, including tools for creating summary tables, f_summary(), summary figures, f_scan(), outlier detection and removal, f_outlier() and f_remove_outliers(), performing data transformations, f_boxcox() in part based on MASS/boxcox and rcompanion', and f_bestNormalize() which wraps and extends functionality from the bestNormalize package. Furthermore, rfriend can automatically (or on request) generate visualizations such as boxplots, f_boxplot(), QQ-plots, f_qqnorm(), histograms f_hist(), and density plots f_density(). Additionally, the package includes several statistical test functions: f_aov(), f_chisq_test(), f_corplot(), f_kruskal_test(), f_lmer(), f_glm(), f_t_test(), f_wilcox_test(), for sequential testing and visualisation of the similar named stats functions. These functions, except for f_chisq_test(), support testing multiple response variables and predictors, while also handling assumption checks, data transformations, and post hoc tests. Post hoc results are automatically summarized in a table using the compact letter display (cld) format for easy interpretation. The package also provides a function to do model comparison, f_model_comparison(), and several utility functions to simplify common R tasks. For example, f_clear() clears the workspace and restarts R with a single command; f_setwd() sets the working directory to match the directory of the current script; f_theme() quickly changes RStudio themes; and f_factors() converts multiple columns of a data frame to factors, and much more. If you encounter any issues or have feature requests, please feel free to contact me via email.

Distance-sampling (<doi:10.1007/978-3-319-19219-2>) is a field survey and analytical method that estimates density and abundance of survey targets (e.g., animals) when detection probability declines with observation distance. Distance-sampling is popular in ecology, especially when survey targets are observed from aerial platforms (e.g., airplane or drone), surface vessels (e.g., boat or truck), or along walking transects. Analysis involves fitting smooth (parametric) curves to histograms of observation distances and using those functions to adjust density estimates for missed targets. Routines included here fit curves to observation distance histograms, estimate effective sampling area, density of targets in surveyed areas, and the abundance of targets in a surrounding study area. Confidence interval estimation uses built-in bootstrap resampling. Help files are extensive and have been vetted by multiple authors. Many tutorials are available on the package's website (URL below).

We provide a toolbox to fit univariate and multivariate linear mixed models via data transforming augmentation. Users can also fit these models via typical data augmentation for a comparison. It returns either maximum likelihood estimates of unknown model parameters (hyper-parameters) via an EM algorithm or posterior samples of those parameters via MCMC. Also see Tak et al. (2019) <doi:10.1080/10618600.2019.1704295>.

Supports concordances in R Markdown documents. This currently allows the original source location in the .Rmd file of errors detected by HTML tidy to be found more easily, and potentially allows forward and reverse search in HTML and LaTeX documents produced from R Markdown'. The LaTeX support has been included in the most recent development version of the patchDVI package.

Read raw and processed data from acoustic ejection mass spectrometry (AEMS) files produced by the Sciex EchoMS instrument. Includes functions to create interactive reader objects, extract raw intensity measurements, mass spectra, and fully-processed mass-transition intensity areas. Methods for data processing and analysis are described in Rimmer et al. (2025) <doi:10.1021/acs.analchem.5c03730>. Supports both multiple reaction monitoring (MRM) and full-scan (neutral loss and precursor ion) data formats.

This package provides a tool for undergraduate and graduate courses in open-channel hydraulics. Provides functions for computing normal and critical depths, steady-state water surface profiles (e.g. backwater curves) and unsteady flow computations (e.g. flood wave routing) as described in Koohafkan MC, Younis BA (2015). "Open-channel computation with R." The R Journal, 7(2), 249â 262. <doi: 10.32614/RJ-2015-034>.

Insert/extract text "reminders" into/from function source code comments or as the "comment" attribute of any object. The former can be handy in development as reminders of e.g. argument requirements, expected objects in the calling environment, required options settings, etc. The latter can be used to provide information of the object and as simple manual "tooltips" for users, among other things.

Generation of Box-Cox based ROC curves and several aspects of inferences and hypothesis testing. Can be used when inferences for one biomarker (Bantis LE, Nakas CT, Reiser B. (2018)<doi:10.1002/bimj.201700107>) are of interest or when comparisons of two correlated biomarkers (Bantis LE, Nakas CT, Reiser B. (2021)<doi:10.1002/bimj.202000128>) are of interest. Provides inferences and comparisons around the AUC, the Youden index, the sensitivity at a given specificity level (and vice versa), the optimal operating point of the ROC curve (in the Youden sense), and the Youden based cutoff.

An interface to the BaM (Bayesian Modeling) engine, a Fortran'-based executable aimed at estimating a model with a Bayesian approach and using it for prediction, with a particular focus on uncertainty quantification. Classes are defined for the various building blocks of BaM inference (model, data, error models, Markov Chain Monte Carlo (MCMC) samplers, predictions). The typical usage is as follows: (1) specify the model to be estimated; (2) specify the inference setting (dataset, parameters, error models...); (3) perform Bayesian-MCMC inference; (4) read, analyse and use MCMC samples; (5) perform prediction experiments. Technical details are available (in French) in Renard (2017) <https://hal.science/hal-02606929v1>. Examples of applications include Mansanarez et al. (2019) <doi:10.1029/2018WR023389>, Le Coz et al. (2021) <doi:10.1002/hyp.14169>, Perret et al. (2021) <doi:10.1029/2020WR027745>, Darienzo et al. (2021) <doi:10.1029/2020WR028607> and Perret et al. (2023) <doi:10.1061/JHEND8.HYENG-13101>.

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>.

This package provides a series of functions to call AD Model Builder (i.e., compile and run models) from within R, read the results back into R as admb objects, and provide standard accessors (i.e. coef(), vcov(), etc.).

This package provides a programmatic interface to the Request Tracker (RT) HTTP API <https://rt-wiki.bestpractical.com/wiki/REST>. RT is a popular ticket tracking system.

This package provides a novel bias-bound approach for non-parametric inference is introduced, focusing on both density and conditional expectation estimation. It constructs valid confidence intervals that account for the presence of a non-negligible bias and thus make it possible to perform inference with optimal mean squared error minimizing bandwidths. This package is based on Schennach (2020) <doi:10.1093/restud/rdz065>.

This package provides XML parsing capability through the Rapidxml C++ header-only library.

Sets a significance level for Random Forest MDI (Mean Decrease in Impurity, Gini or sum of squares) variable importance scores, using an empirical Bayes approach. See Dunne et al. (2022) <doi:10.1101/2022.04.06.487300>.

Fast and efficient computation of rolling and expanding statistics for time-series data.

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

An R and Repast integration tool for running individual-based (IbM) simulation models developed using Repast Simphony Agent-Based framework directly from R code supporting multicore execution. This package integrates Repast Simphony models within R environment, making easier the tasks of running and analyzing model output data for automated parameter calibration and for carrying out uncertainty and sensitivity analysis using the power of R environment.

Interactively build and explore public transit routes with r5r package via a graphical user interface in a shiny app. The underlying routing methods are described in Pereira et al. (2021) <doi:10.32866/001c.21262>.

Construct an explainable nomogram for a machine learning (ML) model to improve availability of an ML prediction model in addition to a computer application, particularly in a situation where a computer, a mobile phone, an internet connection, or the application accessibility are unreliable. This package enables a nomogram creation for any ML prediction models, which is conventionally limited to only a linear/logistic regression model. This nomogram may indicate the explainability value per feature, e.g., the Shapley additive explanation value, for each individual. However, this package only allows a nomogram creation for a model using categorical without or with single numerical predictors. Detailed methodologies and examples are documented in our vignette, available at <https://htmlpreview.github.io/?https://github.com/herdiantrisufriyana/rmlnomogram/blob/master/doc/ml_nomogram_exemplar.html>.

The header-only modern C++ template library Magic Enum for static reflection of enums (to string, from string, iteration) is provided by this package. More information about the underlying library can be found at its repository at <https://github.com/Neargye/magic_enum>.

This package provides tools to derive species-level phylogenies from large synthesis mega-trees for a wide range of taxonomic groups, including plants, birds, mammals, amphibians, reptiles, fish, bees, butterflies, and sharks. When a queried species is absent from the mega-tree, it is grafted onto the tree using one of two placement strategies: attachment at the basal node of the most closely related genus or family ('at_basal_node'), or random attachment below that basal node with probability proportional to branch length ('random_below_basal'). See Li (2023) <doi:10.1111/ecog.06643> for details. Multiple species from a genus not represented in the mega-tree are placed as a polytomy to preserve clade coherence. The package interfaces with the megatrees data package, which bundles or downloads on demand curated mega-trees. Users can also provide their own mega-trees.

An interactive data visualization and exploration toolkit that implements Breiman and Cutler's original random forest Java based visualization tools in R, for supervised and unsupervised classification and regression within the algorithm random forest.