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/profile/guidance/supporting-information/PH-methods/>.

Comprehensive computational routines for random data generation, Maximum Likelihood Estimation (MLE), Maximum Product of Spacings Estimation (MPSE), and MCMC Bayesian estimation under the Improved Adaptive Type-II Progressive Censoring Scheme (IAT-II PCS). Users can supply custom probability density functions (PDF), cumulative distribution functions (CDF), survival functions, parameter ranges, and progressive censoring plans for any continuous univariate lifetime distribution, or rely on built-in parametric models (e.g., Generalized Exponential). Point estimation methods include MLE via optimization algorithms (Broyden-Fletcher-Goldfarb-Shanno (BFGS), Newton-Raphson (NR), Nelder-Mead (NM), Conjugate Gradients (CG), L-BFGS-B, Simulated Annealing (SANN), and Berndt-Hall-Hall-Hausman (BHHH)) and MPSE. Bayesian inference utilizes Metropolis-Hastings within Gibbs sampling under Squared Error Loss (SEL) and LINEX Loss (LL) functions to compute point estimates and Highest Posterior Density (HPD) credible intervals. Asymptotic confidence intervals for parameters, reliability, and hazard rate functions are constructed using asymptotic normality and delta method. Methods are based on Dev and Chacko (2026, Journal of the Iranian Statistical Society, 25, 1-29), Yan, Zhang, and Dong (2021, Journal of Computational and Applied Mathematics, 381, 113022, <doi:10.1016/j.cam.2020.113022>), Ng, Kundu, and Chan (2004, Naval Research Logistics, 51, 1145-1168, <doi:10.1002/nav.20045>), Cheng and Amin (1983, Journal of the Royal Statistical Society Series B, 45, 394-403, <doi:10.1111/j.2517-6161.1983.tb01268.x>), Kundu and Gupta (1999, Australian & New Zealand Journal of Statistics, 41, 173-188, <doi:10.1111/1467-842X.00072>), and Berndt, Hall, Hall, and Hausman (1974, Annals of Economic and Social Measurement, 3, 653-665).

A Javascript Ribbit Scheme runtime.

This package provides the Jester Dataset for package recommenderlab.

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 gem can compare HTML and assert certain elements exists. This is useful when writing tests.

This RSpec plugin can be used to stub environment variables in a scoped context for testing.

This package provides a collection of RuboCop cops to check for downstream compatibility issues in the Ruby code.

Mixed Treatment Comparison is a methodology to compare directly and/or indirectly health strategies (drugs, treatments, devices). This package provides an Rcmdr plugin to perform Mixed Treatment Comparison for binary outcome using BUGS code from Bristol University (Lu and Ades).

The Radiant Multivariate menu includes interfaces for perceptual mapping, factor analysis, cluster analysis, and conjoint analysis. The application extends the functionality in radiant.data'.

This package provides a shiny module to facilitate page layouts with resizable panes for page content based on split.js JavaScript library (<https://split.js.org>).

This package provides a low-level interface for analysing Agricultural Production Systems sIMulator ('APSIM') Next Generation simulation outputs to support structured decision-making workflows.

This RSpec plugin makes it easy to mark test cases as pending or skipped for a specific Ruby engine (e.g. MRI or JRuby) or version combinations.

Documentation at https://melpa.org/#/request-deferred

Documentation at https://melpa.org/#/redpen-paragraph

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

Documentation at https://melpa.org/#/railscasts-theme

Documentation at https://melpa.org/#/desktop-registry

Documentation at https://melpa.org/#/pip-requirements

Documentation at https://melpa.org/#/project-rootfile

Documentation at https://melpa.org/#/russian-calendar

Documentation at https://melpa.org/#/readable-numbers