Display a randomly selected quote about Richard M. Stallman based on the collection in the GNU Octave function fact() which was aggregated by Jordi Gutiérrez Hermoso based on the (now defunct) site stallmanfacts.com (which is accessible only via <http://archive.org>).

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.

This package provides functions and examples for testing hypothesis about the population mean and variance on samples drawn by r-size biased sampling schemes.

BEAST is a Bayesian estimator of abrupt change, seasonality, and trend for decomposing univariate time series and 1D sequential data. Interpretation of time series depends on model choice; different models can yield contrasting or contradicting estimates of patterns, trends, and mechanisms. BEAST alleviates this by abandoning the single-best-model paradigm and instead using Bayesian model averaging over many competing decompositions. It detects and characterizes abrupt changes (changepoints, breakpoints, structural breaks, joinpoints), cyclic or seasonal variation, and nonlinear trends. BEAST not only detects when changes occur but also quantifies how likely the changes are true. It estimates not just piecewise linear trends but also arbitrary nonlinear trends. BEAST is generically applicable to any real-valued time series, such as those from remote sensing, economics, climate science, ecology, hydrology, and other environmental and biological systems. Example applications include identifying regime shifts in ecological data, mapping forest disturbance and land degradation from satellite image time series, detecting market trends in economic indicators, pinpointing anomalies and extreme events in climate records, and analyzing system dynamics in biological time series. Details are given in Zhao et al. (2019) <doi:10.1016/j.rse.2019.04.034>.

This package provides a novel numerical algorithm that provides functionality for estimating the exact 95% confidence interval of the location parameter in the random effects model, and is much faster than the naive method. Works best when the number of studies is between 6-20.

This package provides functions to analyse DNA fragment samples (i.e. derived from RFLP-analysis) and standalone BLAST report files (i.e. DNA sequence analysis).

The provided benchmark suite enables the automated evaluation and comparison of any existing and novel indirect method for reference interval ('RI') estimation in a systematic way. Indirect methods take routine measurements of diagnostic tests, containing pathological and non-pathological samples as input and use sophisticated statistical methods to derive a model describing the distribution of the non-pathological samples, which can then be used to derive reference intervals. The benchmark suite contains 5,760 simulated test sets with varying difficulty. To include any indirect method, a custom wrapper function needs to be provided. The package offers functions for generating the test sets, executing the indirect method and evaluating the results. See ?RIbench or vignette("RIbench_package") for a more comprehensive description of the features. A detailed description and application is described in Ammer T., Schuetzenmeister A., Prokosch H.-U., Zierk J., Rank C.M., Rauh M. "RIbench: A Proposed Benchmark for the Standardized Evaluation of Indirect Methods for Reference Interval Estimation". Clinical Chemistry (2022) <doi:10.1093/clinchem/hvac142>.

This package provides a complete seven-step workflow for copy number variation (CNV) analysis applicable to any disease or condition where samples with genomic copy number data is available. Supports built-in grading and risk stratification presets for seven major cancers (viz. prostate, breast, colorectal, lung, cervical, lymphoma, melanoma) based on clinically validated systems including ISUP Grade Groups, Nottingham Grading System, Dukes staging, IASLC TNM, FIGO, Ann Arbor/Lugano classification, and Breslow depth. Generalizable to other disease types. An automatic mode derives a normalised Risk Score from the data using min-max normalisation and adaptive binning. Custom user-defined thresholds are supported for any other disease type. Downstream functions for CNV aberration detection, recurrence analysis, gene annotation, CNV matrix generation, and CNV-RNA expression correlation are disease-type agnostic.

This package provides methods for analysis of compositional data including robust methods (<doi:10.1007/978-3-319-96422-5>), imputation of missing values (<doi:10.1016/j.csda.2009.11.023>), methods to replace rounded zeros (<doi:10.1080/02664763.2017.1410524>, <doi:10.1016/j.chemolab.2016.04.011>, <doi:10.1016/j.csda.2012.02.012>), count zeros (<doi:10.1177/1471082X14535524>), methods to deal with essential zeros (<doi:10.1080/02664763.2016.1182135>), (robust) outlier detection for compositional data, (robust) principal component analysis for compositional data, (robust) factor analysis for compositional data, (robust) discriminant analysis for compositional data (Fisher rule), robust regression with compositional predictors, functional data analysis (<doi:10.1016/j.csda.2015.07.007>) and p-splines (<doi:10.1016/j.csda.2015.07.007>), contingency (<doi:10.1080/03610926.2013.824980>) and compositional tables (<doi:10.1111/sjos.12326>, <doi:10.1111/sjos.12223>, <doi:10.1080/02664763.2013.856871>) and (robust) Anderson-Darling normality tests for compositional data as well as popular log-ratio transformations (addLR, cenLR, isomLR, and their inverse transformations). In addition, visualisation and diagnostic tools are implemented as well as high and low-level plot functions for the ternary diagram.

This package provides a research infrastructure to develop and evaluate collaborative filtering recommender algorithms. This includes a sparse representation for user-item matrices, many popular algorithms, top-N recommendations, and cross-validation. Hahsler (2022) <doi:10.48550/arXiv.2205.12371>.

With this package we provide an easy method to compute robust and conditional Data Envelopment Analysis (DEA), Free Disposal Hull (FDH) and Benefit of the Doubt (BOD) scores. The robust approach is based on the work of Cazals, Florens and Simar (2002) <doi:10.1016/S0304-4076(01)00080-X>. The conditional approach is based on Daraio and Simar (2007) <doi:10.1007/s11123-007-0049-3>. Besides we provide graphs to help with the choice of m. We relay on the Benchmarking package to compute the efficiency scores and on the np package to compute non parametric estimation of similarity among units.

Defines functions that can be used to collect provenance as an R script executes or during a console session. The output is a text file in PROV-JSON format.

This package provides tools for robust regression model fitting using the RANSAC (Random Sample Consensus) algorithm. RANSAC is an iterative method to estimate parameters of a model from a dataset that contains outliers. This package allows fitting both linear lm and nonlinear nls models using RANSAC, helping users obtain more reliable models in the presence of noisy or corrupted data. The methods are particularly useful in contexts where traditional least squares regression fails due to the influence of outliers. Implementations include support for performance metrics such as RMSE, MAE, and R² based on the inlier subset. For further details, see Fischler and Bolles (1981) <doi:10.1145/358669.358692>.

Interface to the UNU.RAN library for Universal Non-Uniform RANdom variate generators. Thus it allows to build non-uniform random number generators from quite arbitrary distributions. In particular, it provides an algorithm for fast numerical inversion for distribution with given density function. In addition, the package contains densities, distribution functions and quantiles from a couple of distributions.

This package provides tools for constructing longitudinal decision paths, quantifying temporal drift, tracking subgroup disparity trajectories, and stress-testing longitudinal conclusions under hidden bias. Implements three signature metrics: the Drift Intensity Index (DII), which measures structural instability in transition dynamics using the Frobenius norm of consecutive transition matrix differences; the Bias Amplification Index (BAI), which quantifies whether group disparities widen or converge over time; and the Temporal Fragility Index (TFI), which estimates the minimum hidden-bias perturbation required to nullify a longitudinal trend conclusion. An interactive shiny application supports exploratory analysis, visualization, and reproducible reporting. Methods are motivated by applications in educational and social science research, including the Early Childhood Longitudinal Study (ECLS). The DII is based on the Frobenius norm as described in Golub and Van Loan (2013, ISBN:9781421407944). The TFI extends the hidden-bias sensitivity framework of Rosenbaum (2002, ISBN:9781441912633). The BAI draws on disparity-trajectory methods discussed in Duncan and Murnane (2011, ISBN:9780871542731).

This framework aims to provide classes and methods for manipulating and processing of raster time series data (e.g. a time series of satellite images).

Fast computing an ensemble of rank-based trees via boosting or random forest on binary and multi-class problems. It converts continuous gene expression profiles into ranked gene pairs, for which the variable importance indices are computed and adopted for dimension reduction. Decision rules can be extracted from trees.

This package provides methods to compute chemical similarity between two or more reactions and molecules. Allows masking of chemical substructures for weighted similarity computations. Uses packages rCDK and fingerprint for cheminformatics functionality. Methods for reaction similarity and sub-structure masking are as described in: Giri et al. (2015) <doi:10.1093/bioinformatics/btv416>.

This package provides functions and command-line user interface to generate allocation sequence by response-adaptive randomization for clinical trials. The package currently supports two families of frequentist response-adaptive randomization procedures, Doubly Adaptive Biased Coin Design ('DBCD') and Sequential Estimation-adjusted Urn Model ('SEU'), for binary and normal endpoints. One-sided proportion (or mean) difference and Chi-square (or ANOVA') hypothesis testing methods are also available in the package to facilitate the inference for treatment effect. Additionally, the package provides comprehensive and efficient tools to allow one to evaluate and compare the performance of randomization procedures and tests based on various criteria. For example, plots for relationship among assumed treatment effects, sample size, and power are provided. Five allocation functions for DBCD and six addition rule functions for SEU are implemented to target allocations such as Neyman', Rosenberger Rosenberger et al. (2001) <doi:10.1111/j.0006-341X.2001.00909.x> and Urn allocations.

Using a CSV, LaTeX and R to easily build attractive resumes.

This package provides Java graphical user interfaces for viewing, manipulating and plotting graphs. Graphs may be directed or undirected.

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>.

Generate boilerplate code for R6 classes. Given R6 class create getters and/or setters for selected class fields or use RStudio addins to insert methods straight into class definition.

An optimized method for identifying mutually exclusive genomic events. Its main contribution is a statistical analysis based on the Poisson-Binomial distribution that takes into account that some samples are more mutated than others. See [Canisius, Sander, John WM Martens, and Lodewyk FA Wessels. (2016) "A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains most co-occurrence." Genome biology 17.1 : 1-17. <doi:10.1186/s13059-016-1114-x>]. The mutations matrices are sparse matrices. The method developed takes advantage of the advantages of this type of matrix to save time and computing resources.