This package provides functions to estimate the optimal threshold of diagnostic markers or treatment selection markers. The optimal threshold is the marker value that maximizes the utility of the marker based-strategy (for diagnostic or treatment selection) in a given population. The utility function depends on the type of marker (diagnostic or treatment selection), but always takes into account the preferences of the patients or the physician in the decision process. For estimating the optimal threshold, ones must specify the distributions of the marker in different groups (defined according to the type of marker, diagnostic or treatment selection) and provides data to estimate the parameters of these distributions. Ones must also provide some features of the target populations (disease prevalence or treatment efficacies) as well as the preferences of patients or physicians. The functions rely on Bayesian inference which helps producing several indicators derived from the optimal threshold. See Blangero, Y, Rabilloud, M, Ecochard, R, and Subtil, F (2019) <doi:10.1177/0962280218821394> for the original article that describes the estimation method for treatment selection markers and Subtil, F, and Rabilloud, M (2019) <doi:10.1002/bimj.200900242> for diagnostic markers.

Verified interval arithmetic for R, in the inf-sup (endpoint) representation of the set-based flavor of the interval standard. Every operation returns an enclosure that provably contains the exact result: outward rounding is obtained from the predecessor and successor formulas of Rump, Zimmermann, Boldo and Melquiond (2009) <doi:10.1007/s10543-009-0218-z>, which are valid under round-to-nearest and therefore need no change to the floating-point rounding mode. That mode is not reachable from R, and changing it would not be a local act: it is per-thread state of the processor, so it would govern every floating-point operation executed afterwards on that thread, in this package or anywhere else. Elementary functions are provided at two levels: a fast level over the included correctly rounded binary64 implementation, comprising fifteen kernels from CORE-MATH <doi:10.1109/ARITH54963.2022.00014> and the hardware square root, widened by the pre-registered slack of two outward steps; and a rigorous level over Rmpfr with a directed-rounding bridge, reached by an escalation ladder of precisions when a verdict would otherwise fall inside the slack. Fast-level enclosures retain measured provenance because correct rounding of the included software is verified numerically rather than established here as a theorem for every kernel. On top of the kernel the package builds natural and centered interval extensions of expressions, a monotonicity test, the Hansen-Sengupta interval Newton operator with extended division and epsilon-inflated candidate verification, and a subdivision (paving) engine whose only failure mode is a named abstention with its budget printed. Conformance with IEEE Std 1788.1-2017 <doi:10.1109/IEEESTD.2018.8277144> is not claimed, and the reason is the standard's own: its subclause 1.5 makes conformance a list of requirements that an implementation shall satisfy, with no partial grade to claim. What this package follows, measured one requirement at a time and stated in the package documentation, is the interval type and the decoration system of clause 5, 22 of the 39 arithmetic operations of Table 4.1, and the seven numeric functions of Table 4.3. What it does not provide is the cancellative operations, the interval comparison relations, the text input and output of subclause 6.8, the interchange representation of subclause 7.3, and the tightest accuracy that subclause 6.5.2 requires of the basic operations, which here are one unit in the last place wider at each end.

This package provides tools for building brick-proof, reproducible, self-contained data capsules. Resolves open-data sources through the Comprehensive Knowledge Archive Network ('CKAN', <https://ckan.org/>) package_show and package_search endpoints, records and verifies provenance with Secure Hash Algorithm 256 ('SHA-256') digests and Internet Archive Wayback Machine (<https://web.archive.org/>) snapshots, validates downloaded data against a pinned schema, and falls back to schema-driven synthetic data when the real source is unreachable. Run records are captured in a manifest plus a plain-language summary so any result can be traced back to its inputs. Distributional drift between a pinned capsule and a fresh fetch is tested with Kolmogorov-Smirnov, chi-square, population stability index, Jensen-Shannon divergence and Benford first-digit screens, because a re-released extract can be statistically identical yet differ byte-for-byte, and a column can keep its name and type while having been silently rescaled. Manifests can be authenticated rather than only checksum-verified, with keyed digests ('HMAC-SHA-256', RFC 2104) or post-quantum hash-based signatures ('Winternitz one-time signatures under a Merkle tree, RFC 8391), and pinned chunk-wise through a Merkle tree so a mismatch identifies which part of a capsule moved. Also ships a compiled C++ core (summary, robust and rank statistics, SHA-256', SHA-512 and CRC-32') that sibling packages in the rmorie ecosystem reach through LinkingTo for a single, shared numeric and provenance-hashing backend. For the published administrative tables these capsules usually hold, it computes period-over-period change matched on the period rather than the row, with the exact conditional-binomial interval for a ratio of counts and with a percentage-point reading kept distinct from a percent change, rendered to Hypertext Markup Language ('HTML'), Portable Document Format ('PDF'), delimited text, JavaScript Object Notation ('JSON') or Markdown. Interval categories such as "2 to 5" or "50+" are parsed to bounds and the dependence of any derived figure on the open top band is measured rather than assumed. Concentration is summarised by the Gini coefficient, the Lorenz curve and tail-index estimation by exact discrete maximum likelihood; trend in a series of a few periods by the Mann-Kendall test with Theil-Sen slopes, a permutation step-change scan and Poisson rate ratios; and region-coded counts by indirect standardisation, exact standardised incidence ratios, the empirical Bayes shrinkage of Clayton and Kaldor (1987) <doi:10.2307/2532003>, funnel-plot limits and Moran's I.

This package implements the alpha-power hazard model and regression frameworks for survival data based on the flexible hazard rate function h(x; alpha, beta) = alpha^x + x^(beta-1) (Pal et al., 2026 <doi:10.1007/s41096-026-00297-5>). Provides standard distribution functions (d, p, q, r, h, H, s) and distributional properties including raw/central moments, variance, skewness, kurtosis, quantile statistics (Bowley's skewness, Moors's kurtosis), Lambert W hazard rate function minimum (Corless et al., 1996), order statistics, and stochastic ordering (Shaked & Shanthikumar, 1994). Computes five classical estimation methods for baseline parameters: Maximum Likelihood Estimation (Casella & Berger, 2002), Least Squares Estimation (Swain et al., 1988), Weighted Least Squares Estimation (Styan, 1973), Maximum Product of Spacings Estimation (Cheng & Amin, 1983 <doi:10.1111/j.2517-6161.1983.tb01241.x>), and Cramer-von Mises Estimation (Macdonald, 1971). Supports four hazard regression models (M1-M4) within proportional hazards and parametric frameworks across uncensored data, right censoring, left censoring, interval censoring, and progressive Type-I and Type-II censoring schemes (Lee & Wang, 2003; Lawless, 2011; Balakrishnan & Aggarwala, 2000). Includes comprehensive model diagnostics, Cox-Snell, martingale, deviance, standardized, and studentized residuals, leverage, Cook's distance, DFFITS, DFBETAS, model comparisons (AIC, BIC, WAIC), k-fold cross-validation, prediction suites, random data generators, and an eight-panel diagnostic visualization suite.

An implementation of metaheuristic algorithms for continuous optimization. Currently, the package contains the implementations of 21 algorithms, as follows: particle swarm optimization (Kennedy and Eberhart, 1995), ant lion optimizer (Mirjalili, 2015 <doi:10.1016/j.advengsoft.2015.01.010>), grey wolf optimizer (Mirjalili et al., 2014 <doi:10.1016/j.advengsoft.2013.12.007>), dragonfly algorithm (Mirjalili, 2015 <doi:10.1007/s00521-015-1920-1>), firefly algorithm (Yang, 2009 <doi:10.1007/978-3-642-04944-6_14>), genetic algorithm (Holland, 1992, ISBN:978-0262581110), grasshopper optimisation algorithm (Saremi et al., 2017 <doi:10.1016/j.advengsoft.2017.01.004>), harmony search algorithm (Mahdavi et al., 2007 <doi:10.1016/j.amc.2006.11.033>), moth flame optimizer (Mirjalili, 2015 <doi:10.1016/j.knosys.2015.07.006>, sine cosine algorithm (Mirjalili, 2016 <doi:10.1016/j.knosys.2015.12.022>), whale optimization algorithm (Mirjalili and Lewis, 2016 <doi:10.1016/j.advengsoft.2016.01.008>), clonal selection algorithm (Castro, 2002 <doi:10.1109/TEVC.2002.1011539>), differential evolution (Das & Suganthan, 2011), shuffled frog leaping (Eusuff, Landsey & Pasha, 2006), cat swarm optimization (Chu et al., 2006), artificial bee colony algorithm (Karaboga & Akay, 2009), krill-herd algorithm (Gandomi & Alavi, 2012), cuckoo search (Yang & Deb, 2009), bat algorithm (Yang, 2012), gravitational based search (Rashedi et al., 2009) and black hole optimization (Hatamlou, 2013).

This package provides deterministic tools for auditing whether artificial intelligence systems preserve the numerical, semantic, contextual, temporal, geographic, unit, provenance, revision, transformation, and uncertainty properties of official statistics. Structured reference statistics and machine-generated claims can be compared using non-compensatory critical-error rules, weakest-link and geometric fidelity summaries, provenance graphs, and portable SHA-256 proof bundles. The package provides bounded connectors for official statistical services, an easy schema-detection and file-import layer for arbitrary official organisations, extensible provider registries, and a search-first natural- language verification layer that classifies statistical claims, selects suitable official sources, retrieves candidate evidence, matches statistical dimensions, and compares claimed values. If no reference year is stated, verification uses the latest available matching official observation and discloses the resolved year. Source attribution is optional: automatic routing can choose suitable providers when none is named, while explicitly named supported sources are respected by default. Automatic catalogue-to-observation verification is implemented for the World Bank, WHO, the United Nations Statistics Division Sustainable Development Goals service, and the European Commission statistical service, while other providers remain available through bounded direct connectors or generic official-data import. Prompt perturbation, statistical red-team generation, minimal-pair tests, and benchmark data support reproducible evaluation of generative, retrieval-augmented, and agentic statistical systems. An embedded alignment layer maps claim-level controls to relevant activities of the Generic Statistical Business Process Model (GSBPM) 5.2, including Analyse, Disseminate, Evaluate, Quality Management, and Metadata Management.

Generate continuous (normal or non-normal), binary, ordinal, and count (Poisson or Negative Binomial) variables with a specified correlation matrix. It can also produce a single continuous variable. This package can be used to simulate data sets that mimic real-world situations (i.e. clinical or genetic data sets, plasmodes). All variables are generated from standard normal variables with an imposed intermediate correlation matrix. Continuous variables are simulated by specifying mean, variance, skewness, standardized kurtosis, and fifth and sixth standardized cumulants using either Fleishman's third-order (<DOI:10.1007/BF02293811>) or Headrick's fifth-order (<DOI:10.1016/S0167-9473(02)00072-5>) polynomial transformation. Binary and ordinal variables are simulated using a modification of the ordsample() function from GenOrd'. Count variables are simulated using the inverse cdf method. There are two simulation pathways which differ primarily according to the calculation of the intermediate correlation matrix. In Correlation Method 1, the intercorrelations involving count variables are determined using a simulation based, logarithmic correlation correction (adapting Yahav and Shmueli's 2012 method, <DOI:10.1002/asmb.901>). In Correlation Method 2, the count variables are treated as ordinal (adapting Barbiero and Ferrari's 2015 modification of GenOrd, <DOI:10.1002/asmb.2072>). There is an optional error loop that corrects the final correlation matrix to be within a user-specified precision value of the target matrix. The package also includes functions to calculate standardized cumulants for theoretical distributions or from real data sets, check if a target correlation matrix is within the possible correlation bounds (given the distributions of the simulated variables), summarize results (numerically or graphically), to verify valid power method pdfs, and to calculate lower standardized kurtosis bounds.

Automatically runs 18 individual models and 14 ensembles on numeric data, for a total of 32 models. The package automatically returns complete results on all 32 models, 25 charts and six tables. The user simply provides the tidy data, and answers a few questions (for example, how many times would you like to resample the data). From there the package randomly splits the data into train, test and validation sets as the user requests (for example, train = 0.60, test = 0.20, validation = 0.20), fits each of models on the training data, makes predictions on the test and validation sets, measures root mean squared error (RMSE), removes features above a user-set level of Variance Inflation Factor, and has several optional features including scaling all numeric data, four different ways to handle strings in the data. Perhaps the most significant feature is the package's ability to make predictions using the 32 pre trained models on totally new (untrained) data if the user selects that feature. This feature alone represents a very effective solution to the issue of reproducibility of models in data science. The package can also randomly resample the data as many times as the user sets, thus giving more accurate results than a single run. The graphs provide many results that are not typically found. For example, the package automatically calculates the Kolmogorov-Smirnov test for each of the 32 models and plots a bar chart of the results, a bias bar chart of each of the 32 models, as well as several plots for exploratory data analysis (automatic histograms of the numeric data, automatic histograms of the numeric data). The package also automatically creates a summary report that can be both sorted and searched for each of the 32 models, including RMSE, bias, train RMSE, test RMSE, validation RMSE, overfitting and duration. The best results on the holdout data typically beat the best results in data science competitions and published results for the same data set.

This package performs Bayesian point estimation using Lindley's Approximation (1980) <doi:10.1111/j.2517-6161.1980.tb01102.x> for arbitrary univariate probability distributions under numerous censoring and truncation schemes. Users supply the probability density function (PDF), cumulative distribution function (CDF), survival function, log-prior density, initial parameter vector, support bounds, and observed data; the package automatically computes Bayesian point estimates under various loss functions using Lindley's approximation. Supported schemes include complete data, right censoring, left censoring, interval censoring, random censoring, block random censoring, Type-I censoring, Type-II censoring, progressive Type-II censoring, progressive first failure censoring, joint Type-I censoring, joint Type-II censoring, balanced joint progressive Type-II censoring, hybrid censoring, hybrid Type-I censoring, hybrid Type-II censoring, Type-I hybrid censoring, Type-II progressively hybrid censoring, doubly Type-II censoring, middle censoring, right truncation, and left truncation. The package computes posterior expectations of arbitrary smooth functions, supports multiple loss functions (squared error loss function (SELF), weighted squared error loss function (WSELF), modified quadratic squared error loss function (MQSELF), precautionary loss function (PLF), entropy loss function (ELF), linear-exponential (LINEX), generalized entropy loss function (GELF), Kullback-Leibler loss function (K-Loss), and user-defined), provides model selection criteria (Akaike information criterion (AIC), Bayesian information criterion (BIC), corrected Akaike information criterion (AICc), Hannan-Quinn information criterion (HQIC), consistent Akaike information criterion (CAIC), Kullback information criterion (KIC)), goodness-of-fit statistics (Kolmogorov-Smirnov, Anderson-Darling, Cramer-von Mises, Watson, Chi-square), residual analysis (Cox-Snell, Martingale, Deviance, Pearson, Generalized, Randomized quantile), comprehensive visualization tools, prediction utilities, and simulation functions for benchmarking estimators. Methods are described in Lindley (1980) <doi:10.1111/j.2517-6161.1980.tb01102.x>, Tierney and Kadane (1986) <doi:10.2307/2234555>, Tierney, Kass, and Kadane (1989) <doi:10.2307/2335663>, Nagar, Kumar, and Krishna (2026) <doi:10.59467/IJASS.2026.22.1>, Goel, Kumar, and Krishna (2026, "Estimation in power Lindley distributions using balanced joint progressively Type-II censored data"), Wu and Kus (2009) <doi:10.1016/j.csda.2009.03.010>, Goel and Krishna (2026) <doi:10.1007/s13198-026-03208-w>, Balakrishnan and Aggarwala (2000, ISBN:978-1-4612-1334-5), Mondal and Kundu (2020) <doi:10.1080/03610926.2018.1554128>, Ding and Gui (2023) <doi:10.3390/math11092003>, Prajapati, Mitra, and Kundu (2019) <doi:10.1007/s13571-018-0167-0>, Yadav, Jaiswal, and Yadav (2026) <doi:10.1007/s11135-026-02647-8>, Iyer, Jammalamadaka, and Kundu (2008) <doi:10.1016/j.jspi.2007.03.062>, Banerjee and Kundu (2008) <doi:10.1109/TR.2008.916890>, and Kundu and Joarder (2006) <doi:10.1016/j.csda.2005.05.002>.

Documentation at https://melpa.org/#/ruby-refactor

Documentation at https://melpa.org/#/org-re-reveal

Rubygems-task provides Rake tasks for managing and releasing Ruby Gems.

This package provides a modest JavaScript framework for the HTML you already have.

Platform Design Info for Affymetrix RUSGene-1_1-st.

Platform Design Info for Affymetrix RUSGene-1_0-st.

Enhances the R Optimization Infrastructure ('ROI') package with the optimx package.

OpenCL 2.0 compatible language runtime, supporting offline and in-process/in-memory compilation.

Sorbet's runtime type checking component. Sorbet is a powerful type checker for Ruby.

Platform Design Info for Affymetrix RheGene-1_1-st.

Platform Design Info for Affymetrix RheGene-1_0-st.

Platform Design Info for Affymetrix RabGene-1_0-st.

Platform Design Info for Affymetrix RabGene-1_1-st.

Platform Design Info for Affymetrix RCnGene-1_1-st.

Platform Design Info for Affymetrix RJpGene-1_1-st.