Fast C++ agglomerative hierarchical clustering algorithm packaged into easily callable R functions, designed to help cluster biological terms based on how similar of genes are expressed in their activation.

This package provides a comprehensive set of tools designed for optimizing likelihood within a tie-oriented (Butts, C., 2008, <doi:10.1111/j.1467-9531.2008.00203.x>) or an actor-oriented modelling framework (Stadtfeld, C., & Block, P., 2017, <doi:10.15195/v4.a14>) in relational event networks. The package accommodates both frequentist and Bayesian approaches. The frequentist approaches that the package incorporates are the Maximum Likelihood Optimization (MLE) and the Gradient-based Optimization (GDADAMAX). The Bayesian methodologies included in the package are the Bayesian Sampling Importance Resampling (BSIR) and the Hamiltonian Monte Carlo (HMC). The flexibility of choosing between frequentist and Bayesian optimization approaches allows researchers to select the estimation approach which aligns the most with their analytical preferences.

Execute FOCAL (<https://en.wikipedia.org/wiki/FOCAL_(programming_language)>) source code directly in R'. This is achieved by translating FOCAL code into equivalent R commands and controlling the sequence of execution.

Analyzing the performance of artificial intelligence (AI) systems/algorithms characterized by a search-and-report strategy. Historically observer performance has dealt with measuring radiologists performances in search tasks, e.g., searching for lesions in medical images and reporting them, but the implicit location information has been ignored. The implemented methods apply to analyzing the absolute and relative performances of AI systems, comparing AI performance to a group of human readers or optimizing the reporting threshold of an AI system. In addition to performing historical receiver operating receiver operating characteristic (ROC) analysis (localization information ignored), the software also performs free-response receiver operating characteristic (FROC) analysis, where lesion localization information is used. A book using the software has been published: Chakraborty DP: Observer Performance Methods for Diagnostic Imaging - Foundations, Modeling, and Applications with R-Based Examples, Taylor-Francis LLC; 2017: <https://www.routledge.com/Observer-Performance-Methods-for-Diagnostic-Imaging-Foundations-Modeling/Chakraborty/p/book/9781482214840>. Online updates to this book, which use the software, are at <https://dpc10ster.github.io/RJafrocQuickStart/>, <https://dpc10ster.github.io/RJafrocRocBook/> and at <https://dpc10ster.github.io/RJafrocFrocBook/>. Supported data collection paradigms are the ROC, FROC and the location ROC (LROC). ROC data consists of single ratings per images, where a rating is the perceived confidence level that the image is that of a diseased patient. An ROC curve is a plot of true positive fraction vs. false positive fraction. FROC data consists of a variable number (zero or more) of mark-rating pairs per image, where a mark is the location of a reported suspicious region and the rating is the confidence level that it is a real lesion. LROC data consists of a rating and a location of the most suspicious region, for every image. Four models of observer performance, and curve-fitting software, are implemented: the binormal model (BM), the contaminated binormal model (CBM), the correlated contaminated binormal model (CORCBM), and the radiological search model (RSM). Unlike the binormal model, CBM, CORCBM and RSM predict proper ROC curves that do not inappropriately cross the chance diagonal. Additionally, RSM parameters are related to search performance (not measured in conventional ROC analysis) and classification performance. Search performance refers to finding lesions, i.e., true positives, while simultaneously not finding false positive locations. Classification performance measures the ability to distinguish between true and false positive locations. Knowing these separate performances allows principled optimization of reader or AI system performance. This package supersedes Windows JAFROC (jackknife alternative FROC) software V4.2.1, <https://github.com/dpc10ster/WindowsJafroc>. Package functions are organized as follows. Data file related function names are preceded by Df', curve fitting functions by Fit', included data sets by dataset', plotting functions by Plot', significance testing functions by St', sample size related functions by Ss', data simulation functions by Simulate and utility functions by Util'. Implemented are figures of merit (FOMs) for quantifying performance and functions for visualizing empirical or fitted operating characteristics: e.g., ROC, FROC, alternative FROC (AFROC) and weighted AFROC (wAFROC) curves. For fully crossed study designs significance testing of reader-averaged FOM differences between modalities is implemented via either Dorfman-Berbaum-Metz or the Obuchowski-Rockette methods. Also implemented is single treatment analysis, which allows comparison of performance of a group of radiologists to a specified value, or comparison of AI to a group of radiologists interpreting the same cases. Crossed-modality analysis is implemented wherein there are two crossed treatment factors and the aim is to determined performance in each treatment factor averaged over all levels of the second factor. Sample size estimation tools are provided for ROC and FROC studies; these use estimates of the relevant variances from a pilot study to predict required numbers of readers and cases in a pivotal study to achieve the desired power. Utility and data file manipulation functions allow data to be read in any of the currently used input formats, including Excel, and the results of the analysis can be viewed in text or Excel output files. The methods are illustrated with several included datasets from the author's collaborations. This update includes improvements to the code, some as a result of user-reported bugs and new feature requests, and others discovered during ongoing testing and code simplification.

Computes linear Bayesian spectral estimates from multirate data for second-order stationary time series. Provides credible intervals and methods for plotting various spectral estimates. Please see the paper `Should we sample a time series more frequently? (doi below) for a full description of and motivation for the methodology.

Regularized (polychotomous) logistic regression by Gibbs sampling. The package implements subtly different MCMC schemes with varying efficiency depending on the data type (binary v. binomial, say) and the desired estimator (regularized maximum likelihood, or Bayesian maximum a posteriori/posterior mean, etc.) through a unified interface. For details, see Gramacy & Polson (2012 <doi:10.1214/12-BA719>).

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>.

Efficient reading of raw markdown tables into tibbles. Designed to accept content from strings, files, and URLs with the ability to extract and read multiple tables from markdown for analysis.

The evaluation criteria of rangeland health, condition and landscape function analysis based on species diversity and functional diversity of rangeland plant communities.

An R Commander plug-in providing an integrated solution to perform a series of text mining tasks such as importing and cleaning a corpus, and analyses like terms and documents counts, vocabulary tables, terms co-occurrences and documents similarity measures, time series analysis, correspondence analysis and hierarchical clustering. Corpora can be imported from spreadsheet-like files, directories of raw text files, as well as from Dow Jones Factiva', LexisNexis', Europresse and Alceste files.

This package contains functions for analysing relative survival data, including nonparametric estimators of net (marginal relative) survival, relative survival ratio, crude mortality, methods for fitting and checking additive and multiplicative regression models, transformation approach, methods for dealing with population mortality tables. Work has been described in Pohar Perme, Pavlic (2018) <doi:10.18637/jss.v087.i08>.

This package implements random variables by means of S4 classes and methods.

Handle JSON-stat format (<https://json-stat.org>) in R. Not all features are supported, especially the extensive metadata features of JSON-stat'.

R Commander plugin for teaching statistical methods. It adds a new menu for making easier the teaching of the main concepts about the main statistical methods.

Installs OpenCV for use by other packages. OpenCV <https://opencv.org/> is library of programming functions mainly aimed at real-time computer vision. This Lite version installs the stable base version of OpenCV and some of its experimental externally contributed modules. It does not provide R bindings directly.

Process phylogenetic trees with tropical support vector machine and principal component analysis defined with tropical geometry. Details about tropical support vector machine are available in : Tang, X., Wang, H. & Yoshida, R. (2020) <arXiv:2003.00677>. Details about tropical principle component analysis are available in : Page, R., Yoshida, R. & Zhang L. (2020) <doi:10.1093/bioinformatics/btaa564> and Yoshida, R., Zhang, L. & Zhang, X. (2019) <doi:10.1007/s11538-018-0493-4>.

R-based access to a large set of data variables relevant to forest ecology in British Columbia (BC), Canada. Layers are in raster format at 100m resolution in the BC Albers projection, hosted at the Federated Research Data Repository (FRDR) with <doi:10.20383/101.0283>. The collection includes: elevation; biogeoclimatic zone; wildfire; cutblocks; forest attributes from Hansen et al. (2013) <doi:10.1139/cjfr-2013-0401> and Beaudoin et al. (2017) <doi:10.1139/cjfr-2017-0184>; and rasterized Forest Insect and Disease Survey (FIDS) maps for a number of insect pest species, all covering the period 2001-2018. Users supply a polygon or point location in the province of BC, and rasterbc will download the overlapping raster tiles hosted at FRDR, merging them as needed and returning the result in R as a SpatRaster object. Metadata associated with these layers, and code for downloading them from their original sources can be found in the github repository <https://github.com/deankoch/rasterbc_src>.

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>.

Efficient solvers for 10 regularized multi-task learning algorithms applicable for regression, classification, joint feature selection, task clustering, low-rank learning, sparse learning and network incorporation. Based on the accelerated gradient descent method, the algorithms feature a state-of-art computational complexity O(1/k^2). Sparse model structure is induced by the solving the proximal operator. The detail of the package is described in the paper of Han Cao and Emanuel Schwarz (2018) <doi:10.1093/bioinformatics/bty831>.

Reads in continuous glucose monitor data of many different formats, calculates a host of glycemic variability metrics, and plots glucose over time.

You can easily share url pages using React Router in shiny applications and Quarto documents. The package wraps the react-router-dom React library and provides access to hash routing to navigate on multiple url pages.

This package provides access to the Ravelry API <https://www.ravelry.com/groups/ravelry-api>. An R wrapper for pulling data from Ravelry.com', an organizational tool for crocheters, knitters, spinners, and weavers. You can retrieve pattern, yarn, author, and shop information by search or by a given id.

This package provides functions to construct efficient row-column designs for 3-level factorial experiments in 3 rows. The designs ensure the estimation of all main effects (full efficiency) and two factor interactions in minimum replications. For more details, see Dey, A. and Mukerjee, R. (2012) <doi:10.1016/j.spl.2012.06.014> and Dash, S., Parsad, R., and Gupta, V. K. (2013) <doi:10.1007/s40003-013-0059-5>.

Screens all .R', .Rmd', and .qmd files to extract the name of packages used in a project. This package detects packages called with library(foo)', require(foo)', foo::bar() and use("foo", "bar") and adds these dependencies in the DESCRIPTION file in the sections Depends, Imports, and Suggests.