• Many-body delocalization with random vector potentials
• Characterization of intersecting families of maximum size in $PSL(2,q)$
• Learning in concave games with imperfect information
• Fundamental Limits of Budget-Fidelity Trade-off in Label Crowdsourcing
• Bayesian Projection of Life Expectancy Accounting for the HIV/AIDS Epidemic
• Playing Anonymous Games using Simple Strategies
• Lower bounds for the smallest singular value of structured random matrices
• Algorithms for Colourful Simplicial Depth and Medians in the Plane
• Phase Transition in Conditional Curie-Weiss Model
• Johnson-Schechtman and Khinchine inequalities in noncommutative probability theory
• $Φ$-moment inequalities for independent and freely independent random variables
• Applying Topological Persistence in Convolutional Neural Network for Music Audio Signals
• Restricted completion of sparse partial Latin squares
• Toll number of the Cartesian and the lexicographic product of graphs
• Proceedings First Workshop on Causal Reasoning for Embedded and safety-critical Systems Technologies
• $χ$-bounds, operations and chords
• Skew-t Filter and Smoother with Improved Covariance Matrix Approximation
• Activity Networks with Delays An application to toxicity analysis
• Hard Negative Mining for Metric Learning Based Zero-Shot Classification
• Using an epidemiological approach to maximize data survival in the internet of things
• Test for Temporal Homogeneity of Means in High-dimensional Longitudinal Data
• An invariant for minimum triangle-free graphs
• Frobenius and Cartier algebras of Stanley-Reisner rings (II)
• Estimating the Number of Clusters via Normalized Cluster Instability
• Entity Embedding-based Anomaly Detection for Heterogeneous Categorical Events
• A Note on the Practicality of Maximal Planar Subgraph Algorithms
• Maximum Correntropy Unscented Filter
• Well-Posedness and Stability for a Class of Stochastic Delay Differential Equations with Singular Drift
• Leveraging over intact priors for boosting control and dexterity of prosthetic hands by amputees
• Graphic TSP in 2-connected cubic graphs
Format Outputs of Statistical Tests According to APA Guidelines (apa)
Formatter functions in the ‘apa’ package take the return value of a statistical test function, e.g. a call to chisq.test() and return a string formatted according to the guidelines of the APA (American Psychological Association).
Substance Flow Computation (sfc)
Provides a function sfc() to compute the substance flow with the input files — ‘data’ and ‘model’. If sample.size is set more than 1, uncertainty analysis will be executed while the distributions and parameters are supplied in the file ‘data’.
Parallel Utilities for Lambda Selection along a Regularization Path (pulsar)
Model selection for penalized graphical models using the Stability Approach to Regularization Selection (‘StARS’), with options for speed-ups including Bounded StARS (B-StARS), batch computing, and other stability metrics (e.g., graphlet stability G-StARS).
Survival and Competing Risk Analyses with Time-to-Event Data as Covariates (time2event)
Cox proportional hazard and competing risk regression analyses can be performed with time-to-event data as covariates.
Markov-Switching GARCH Models (MSGARCH)
The MSGARCH package offers methods to fit (by Maximum Likelihood or Bayesian), simulate, and forecast various Markov-Switching GARCH processes.
Local Association Measures (zebu)
Implements the estimation of local (and global) association measures: Ducher’s Z, pointwise mutual information and normalized pointwise mutual information. The significance of local (and global) association is accessed using p-values estimated by permutations. Finally, using local association subgroup analysis, it identifies if the association between variables is dependent on the value of another variable.
Flow is powerful. Think about a great conversation you’ve had, with no awkwardness or selfconsciousness: just effortless communication. In data visualization, flow is crucial. Your audience should smoothly absorb and use the information in a dashboard without distractions or turbulence. Lack of flow means lack of communication, which means failure. Psychologist Mihaly Czikszentmihalyi has studied flow extensively. Czikszentmihalyi and other researchers have found that flow is correlated with happiness, creativity, and productivity. People experience flow when their skills are engaged and they’re being challenged just the right amount. The experience is not too challenging or too easy: flow is a just-right, Goldilocks state of being. So how do you create flow for an audience? By tailoring the presentation of data to that audience. If you focus on the skills, motivations, and needs of an audience, you’ll have a better chance of creating a positive experience of flow with your dashboards. And by creating that flow, you’ll be able to persuade, inform, and engage. How to Build Dashboards That Persuade, Inform and Engage
In statistics, the jackknife is a resampling technique especially useful for variance and bias estimation. The jackknife predates other common resampling methods such as the bootstrap. The jackknife estimator of a parameter is found by systematically leaving out each observation from a dataset and calculating the estimate and then finding the average of these calculations. Given a sample of size N, the jackknife estimate is found by aggregating the estimates of each N – 1 estimate in the sample.
The jackknife technique was developed in Quenouille (1949, 1956). Tukey (1958) expanded on the technique and proposed the name “jackknife” since, like a Boy Scout’s jackknife, it is a “rough and ready” tool that can solve a variety of problems even though specific problems may be more efficiently solved with a purpose-designed tool.
The jackknife represents a linear approximation of the bootstrap.
… Jackknife Resampling
“Distinguishing between feature selection and dimensionality reduction might seem counter-intuitive at first, since feature selection will eventually lead (reduce dimensionality) to a smaller feature space. In practice, the key difference between the terms “feature selection” and “dimensionality reduction” is that in feature selection, we keep the “original feature axis”, whereas dimensionality reduction usually involves a transformation technique.” Sebastian Raschka ( August 24, 2014 )
Censored and Truncated Quantile Regression (ctqr)
Estimation of quantile regression models for survival data.
Multidimensional Gauss-Hermite Quadrature (MultiGHQuad)
Uses a transformed, rotated and optionally adapted n-dimensional grid of quadrature points to calculate the numerical integral of n multivariate normal distributed parameters.
Miscellaneous Utilities and Functions (JWileymisc)
A collection of miscellaneous tools and functions, such as tools to generate descriptive statistics tables, format output, visualize relations among variables or check distributions.
Evolutionary Computing in R (ecr)
Provides a powerful framework for evolutionary computing in R. The user can easily construct powerful evolutionary algorithms for tackling both single- and multi-objective problems by plugging in different predefined evolutionary building blocks, e. g., operators for mutation, recombination and selection with just a few lines of code. Your problem cannot be easily solved with a standard EA which works on real-valued vectors, permutations or binary strings? No problem, ‘ecr’ has been developed with that in mind. Extending the framework with own operators is also possible. Additionally there are various comfort functions, like monitoring, logging and more.
Phase II Clinical Trial Design for Multinomial Endpoints (ph2mult)
Provide multinomial design methods under intersection-union test (IUT) and union-intersection test (UIT) scheme for Phase II trial. The design types include : Minimax (minimize the maximum sample size), Optimal (minimize the expected sample size), Admissible (minimize the Bayesian risk) and Maxpower (maximize the exact power level).
Stack Data Type as an ‘R6’ Class (rstack)
An extremely simple stack data type, implemented with ‘R6’ classes. The size of the stack increases as needed, and the amortized time complexity is O(1). The stack may contain arbitrary objects.
In computer science, MinHash (or the min-wise independent permutations locality sensitive hashing scheme) is a technique for quickly estimating how similar two sets are. The scheme was invented by Andrei Broder (1997), and initially used in the AltaVista search engine to detect duplicate web pages and eliminate them from search results. It has also been applied in large-scale clustering problems, such as clustering documents by the similarity of their sets of words. … Min-Wise Independent Permutations Locality Sensitive Hashing Scheme (MinHash)