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Mathematical datasets, statistical benchmarks, probability, optimization, operations research
2,485 datasets
Statnet is a collection of R packages for statistical network analysis designed to work together through shared data representations and API design. The integrated suite provides tools for the representation, visualization, analysis, and simulation of many different forms of network data. It is authored by Mark S. Handcock and maintained by the statnet project team.
Frank E. Harrell's 'rmsb' package provides a Bayesian companion to the 'rms' package for regression modeling. It implements Bayesian logistic binary and ordinal regression models, including options for clustering, censoring, and partial proportional odds. The package's 'blrm()' fitting function produces objects compatible with established 'rms' utilities for estimation and visualization.
Alexis Dinno's 'dunn.test' computes Dunn's test (1964) for stochastic dominance among multiple groups following a Kruskal-Wallis test. The method makes k(k-1)/2 pairwise comparisons using z-test approximations of rank statistics and accounts for tied ranks. Its null hypothesis corresponds to that of the Wilcoxon-Mann-Whitney rank-sum test.
Vikas Sharma's experimental dataset evaluates the mechanical and structural properties of self-compacting concrete. The work applies the Okamora technique for mix design optimization, replacing cement with GGBS and fine aggregate with Robo sand. Tests measure fresh properties, hardened compression strength, split tensile strength, and flexural strength of beams.
Lynn Boyd Hinds authored a historical analysis of the Cold War's political rhetoric and key doctrines. The work includes chapters on the Truman Doctrine, the Marshall Plan, and the political realities of the era. It is sourced from Davidson College and listed on the paperswithcode platform.
Distribution mapping of breeding sites for species within the Ross Sea Marine Protected Area, derived from aerial photography. The dataset was created by the organization AMD_KOPRI. The temporal coverage and data volume are unknown.
A distribution map and breeding population monitoring data for pinnipeds within the Ross Sea Marine Protected Area. The data was produced by AMD_KOPRI. The specific temporal coverage and data volume are unknown.
String manipulation library providing SIMD-accelerated operations for search, hashing, and sorting across 8 programming languages. Developed by ashvardanian and updated in December 2025, it targets performance optimization using AVX-512, NEON, and CUDA.
The APRIL dataset contains 258,103 examples of Lean proof-repair tuples, including compiler diagnostics and explanations. It was created by the organization uw-math-ai and was last updated on the Hugging Face platform in February 2026. The dataset is intended for tasks related to formal verification and theorem proving.
A Bayesian network model with 76 nodes and 112 arcs, representing performance and system interactions for Windows 95. The model contains 574 parameters and was sourced from the bnlearn Bayesian Network Repository. Its structure includes an average Markov blanket size of 5.92 and a maximum in-degree of 7.
OpenML hosts the Win95pts Bayesian network, a discrete probabilistic model with 76 nodes and 112 arcs. The network contains 574 parameters and is referenced in the bnlearn Bayesian Network Repository. Its license is designated as us-pd.
Win95pts is a Bayesian network with 76 nodes and 112 arcs, modeling relationships within a computer system. It contains 574 parameters and is sourced from the bnlearn Bayesian Network Repository. The dataset's specific temporal and geographic scope is not provided.
OpenML hosts the Win95pts Bayesian Network, a discrete probabilistic graphical model. The network contains 76 nodes and 112 arcs, with 574 parameters and an average Markov blanket size of 5.92. Its license is listed as us-pd.
OpenML hosts the win95pts_6 dataset, a discrete Bayesian network with 76 nodes and 112 arcs. The network contains 574 parameters and is sourced from the bnlearn Bayesian Network Repository. Its author and last update date are unknown.
76 nodes and 112 arcs define this discrete Bayesian network from the bnlearn repository. The network contains 574 parameters and has an average Markov blanket size of 5.92. It is a sample from the Win95pts model, used as a benchmark for probabilistic reasoning and structure learning algorithms.
A Bayesian network named Win95pts, containing 76 nodes and 112 arcs. The network has 574 parameters, with an average Markov blanket size of 5.92 and a maximum in-degree of 7. It is a discrete, large-scale network from the bnlearn repository, hosted on OpenML.
A Bayesian network sample from the bnlearn repository, containing 76 nodes and 112 arcs. The network has 574 parameters, with an average Markov blanket size of 5.92 and a maximum in-degree of 7. Its license is designated as us-pd.
A Bayesian network with 76 nodes and 112 arcs, representing a discrete probabilistic model. The network contains 574 parameters and has an average Markov blanket size of 5.92. It is a sample from the bnlearn Bayesian Network Repository, a known source for discrete graphical models.
DiceOptim is an R package implementing the Efficient Global Optimization (EGO) algorithm for computer experiments. It was authored by Victor Picheny and includes adaptations for problems with noise, parallel infill, and constraints as described in referenced papers from 2012 and 2013. The dataset likely contains simulation or experimental results used to demonstrate and benchmark these optimization methods.
markovchain is an R package authored by Giorgio Alfredo Spedicato, providing S4 methods and functions for creating and managing discrete time Markov chains. The package includes tools for statistical fitting, drawing random variates, and analyzing structural properties of Markov chains. Some functions also support continuous time Markov chains through the suggested ctmcd package.