This package provides functions to calculate several ecological indices of individual and population niche width (Araujo's E, clustering and pairwise similarity among individuals, IS, Petraitis W, and Roughgarden's WIC/TNW) to assess individual specialization based on data of resource use. Resource use can be quantified by counts of categories, measures of mass or length, or proportions. Monte Carlo resampling procedures are available for hypothesis testing against multinomial null models. Details are provided in Zaccarelli et al. (2013) <doi:10.1111/2041-210X.12079> and associated references.

This package provides classes and functions for modelling health care interventions using decision trees and semi-Markov models. Mechanisms are provided for associating an uncertainty distribution with each source variable and for ensuring transparency of the mathematical relationships between variables. The package terminology follows Briggs "Decision Modelling for Health Economic Evaluation" (2006, ISBN:978-0-19-852662-9).

Use this package to identify indicators of transparency within the published literature. It can identify and extract text related to indicators of transparency from specifically formatted TXT files and from PMC XML files (i.e. XML files downloaded from the PubMed Central). It builds on the original rtransparent tool of Serghiou et al. (2021) <doi:10.1371/journal.pbio.3001107>.

The Rearrangement Correlation Coefficient is an adjusted version of Pearson's correlation coefficient that accurately measures monotonic dependence relationships, including both linear and nonlinear associations. This method addresses the underestimation problem of classical correlation coefficients in nonlinear monotonic scenarios through improved statistical bounds derived from rearrangement inequalities. For more details, see Ai (2024) <doi:10.52202/079017-1180>.

Defines submission profiles per regulatory authority with authority-specific pillar weights, decision thresholds, indicator requirements, and risk configuration. Supports the U.S. Food and Drug Administration (FDA), European Medicines Agency (EMA), Pharmaceuticals and Medical Devices Agency (PMDA), Health Canada, Therapeutic Goods Administration (TGA), and Medicines and Healthcare products Regulatory Agency (MHRA). Integrates with r4subscore and r4subrisk configuration systems.

Image data used as examples in the loon R package.

An integrated package for constructing random forest prediction intervals using a fast implementation package ranger'. This package can apply the following three methods described in Haozhe Zhang, Joshua Zimmerman, Dan Nettleton, and Daniel J. Nordman (2019) <doi:10.1080/00031305.2019.1585288>: the out-of-bag prediction interval, the split conformal method, and the quantile regression forest.

Implementation of Gibbs sampling algorithm for Bayesian Estimation of the Reduced Reparameterized Unified Model ('rrum'), described by Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>.

Adds menu items for case 2 (profile case) best-worst scaling (BWS2) to the R Commander. BWS2 is a question-based survey method that constructs profiles (combinations of attribute levels) using an orthogonal array, asks respondents to select the best and worst levels in each profile, and measures preferences for attribute levels by analyzing the responses. For details, see Aizaki and Fogarty (2019) <doi:10.1016/j.jocm.2019.100171>.

This package provides a common framework for calculating distance matrices.

Generic functions to analyze the distribution of two continuous variables: conf2d to calculate a smooth empirical confidence region, and freq2d to calculate a frequency distribution.

Import SGF (Smart Game File) into R.

For a multisite replication project, computes the consistency metric P_orig, which is the probability that the original study would observe an estimated effect size as extreme or more extreme than it actually did, if in fact the original study were statistically consistent with the replications. Other recommended metrics are: (1) the probability of a true effect of scientifically meaningful size in the same direction as the estimate the original study; and (2) the probability of a true effect of meaningful size in the direction opposite the original study's estimate. These two can be computed using the package \codeMetaUtility::prop_stronger. Additionally computes older metrics used in replication projects (namely expected agreement in "statistical significance" between an original study and replication studies as well as prediction intervals for the replication estimates). See Mathur and VanderWeele (under review; <https://osf.io/apnjk/>) for details.

Provide function for get data from YouTube Data API <https://developers.google.com/youtube/v3/docs/>, YouTube Analytics API <https://developers.google.com/youtube/analytics/reference/> and YouTube Reporting API <https://developers.google.com/youtube/reporting/v1/reports>.

This package provides functions for assessing variable relations and associations prior to modeling with a Random Forest algorithm (although these are relevant for any predictive model). Metrics such as partial correlations and variance inflation factors are tabulated as well as plotted for the user. A function is available for tuning the main Random Forest hyper-parameter based on model performance and variable importance metrics. This grid-search technique provides tables and plots showing the effect of the main hyper-parameter on each of the assessment metrics. It also returns each of the evaluated models to the user. The package also provides superior variable importance plots for individual models. All of the plots are developed so that the user has the ability to edit and improve further upon the plots. Derivations and methodology are described in Bladen (2022) <https://digitalcommons.usu.edu/etd/8587/>.

This package implements a series of robust Kalman filtering approaches. It implements the additive outlier robust filters of Ruckdeschel et al. (2014) <arXiv:1204.3358> and Agamennoni et al. (2018) <doi:10.1109/ICRA.2011.5979605>, the innovative outlier robust filter of Ruckdeschel et al. (2014) <arXiv:1204.3358>, as well as the innovative and additive outlier robust filter of Fisch et al. (2020) <arXiv:2007.03238>.

The Radiant Model menu includes interfaces for linear and logistic regression, naive Bayes, neural networks, classification and regression trees, model evaluation, collaborative filtering, decision analysis, and simulation. The application extends the functionality in radiant.data'.

Test Statistics for Independence in High-Dimensional Datasets. This package consists of two functions to perform the complete independence test based on test statistics proposed by Bulut (unpublished yet) and suggested by Najarzadeh (2021) <doi: 10.1080/03610926.2019.1702699>. The Bulut's statistic is not sensitive to outliers in high-dimensional data, unlike one of Najarzadeh (2021) <doi: 10.1080/03610926.2019.1702699>. So, the Bulut's statistic can be performed robustly by using RDnp function.

This package provides a collection of efficient and effective tools and algorithms for subgroup discovery and analytics. The package integrates an R interface to the org.vikamine.kernel library of the VIKAMINE system <http://www.vikamine.org> implementing subgroup discovery, pattern mining and analytics in Java.

Read the data from Origin(R) project files ('*.opj') <https://www.originlab.com/doc/User-Guide/Origin-File-Types>. No write support is planned.

Read and write las and laz binary file formats. The LAS file format is a public file format for the interchange of 3-dimensional point cloud data between data users. The LAS specifications are approved by the American Society for Photogrammetry and Remote Sensing <https://community.asprs.org/leadership-restricted/leadership-content/public-documents/standards>. The LAZ file format is an open and lossless compression scheme for binary LAS format versions 1.0 to 1.4 <https://laszip.org/>.

This package provides 3D plotting routines that facilitate the use of the rgl package and extend its functionality. For example, the routines allow the user to directly control the camera position & orientation, as well as to generate 3D movies with a moving observer.

EZR (Easy R) adds a variety of statistical functions, including survival analyses, ROC analyses, metaanalyses, sample size calculation, and so on, to the R commander. EZR enables point-and-click easy access to statistical functions, especially for medical statistics. EZR is platform-independent and runs on Windows, Mac OS X, and UNIX. Its complete manual is available only in Japanese (Chugai Igakusha, ISBN: 978-4-498-10918-6, Nankodo, ISBN: 978-4-524-21861-5, Ohmsha, ISBN: 978-4-274-22632-8), but an report that introduced the investigation of EZR was published in Bone Marrow Transplantation (Nature Publishing Group) as an Open article. This report can be used as a simple manual. It can be freely downloaded from the journal website as shown below. This report has been cited in more than 14,000 scientific articles.

This package provides a function for multivariate outlier detection named Modified Stahel-Donoho (MSD) estimators is contained. The function is for elliptically distributed datasets and recognizes outliers based on Mahalanobis distance. The function is called the single core version in Wada & Tsubaki (2013) <doi:10.1109/CLOUDCOM-ASIA.2013.86> and evaluated with other methods in Wada, Kawano & Tsubaki (2020) <doi:10.17713/ajs.v49i2.872>.