See Miroshnikov and Conlon (2014) <doi:10.1371/journal.pone.0108425>. Recent Bayesian Markov chain Monto Carlo (MCMC) methods have been developed for big data sets that are too large to be analyzed using traditional statistical methods. These methods partition the data into non-overlapping subsets, and perform parallel independent Bayesian MCMC analyses on the data subsets, creating independent subposterior samples for each data subset. These independent subposterior samples are combined through four functions in this package, including averaging across subset samples, weighted averaging across subsets samples, and kernel smoothing across subset samples. The four functions assume the user has previously run the Bayesian analysis and has produced the independent subposterior samples outside of the package; the functions use as input the array of subposterior samples. The methods have been demonstrated to be useful for Bayesian MCMC models including Bayesian logistic regression, Bayesian Gaussian mixture models and Bayesian hierarchical Poisson-Gamma models. The methods are appropriate for Bayesian hierarchical models with hyperparameters, as long as data values in a single level of the hierarchy are not split into subsets.
This package provides functions to calculate commonly used public health statistics and their confidence intervals using methods approved for use in the production of Public Health England indicators such as those presented via Fingertips (<https://fingertips.phe.org.uk/>). It provides functions for the generation of proportions, crude rates, means, directly standardised rates, indirectly standardised rates, standardised mortality ratios, slope and relative index of inequality and life expectancy. Statistical methods are referenced in the following publications. Breslow NE, Day NE (1987) <doi:10.1002/sim.4780080614>. Dobson et al (1991) <doi:10.1002/sim.4780100317>. Armitage P, Berry G (2002) <doi:10.1002/9780470773666>. Wilson EB. (1927) <doi:10.1080/01621459.1927.10502953>. Altman DG et al (2000, ISBN: 978-0-727-91375-3). Chiang CL. (1968, ISBN: 978-0-882-75200-6). Newell C. (1994, ISBN: 978-0-898-62451-9). Eayres DP, Williams ES (2004) <doi:10.1136/jech.2003.009654>. Silcocks PBS et al (2001) <doi:10.1136/jech.55.1.38>. Low and Low (2004) <doi:10.1093/pubmed/fdh175>. Fingertips Public Health Technical Guide: <https://fingertips.phe.org.uk/static-reports/public-health-technical-guidance/>.
Custom rubocop cops used by Discourse
No-dep range header parser
This package provides AWS credential tooling.
Interoperability library for Rust Windowing applications.
Interoperability library for Rust Windowing applications.
Interoperability library for Rust Windowing applications.
Interoperability library for Rust Windowing applications.
Implementation detail of the konst crate.
Support for rust-rust-i18n crate.
This package provides the Jester Dataset for package recommenderlab.
This package provides No-dep range header parser.
This package provides mimalloc_rust hand written sys bindings.
This package provides glue code for Rustls and synchronous Hyper.
This package provides a range header parser without any dependencies.
This package provides a range header parser without any dependencies.
This package provides an RDF4J-based implementation of RDF 1.1 concepts.
R Commander plug-in to demonstrate various actuarial and financial risks. It includes valuation of bonds and stocks, portfolio optimization, classical ruin theory, demography and epidemic.
This package provides a double-ended queue that deref's into a slice.
This package provides a library for fast image resizing with use of SIMD instructions.
This package provides a library for fast image resizing with use of SIMD instructions.
This package provides a basic, unsupported DOM structure for use by tests in html5ever/xml5ever
This package provides a basic, unsupported DOM structure for use by tests in html5ever/xml5ever