Takes user-provided baseline data from groups of randomised controlled data and assesses whether the observed distribution of baseline p-values, numbers of participants in each group, or categorical variables are consistent with the expected distribution, as an aid to the assessment of integrity concerns in published randomised controlled trials. References (citations in PubMed format in details of each function): Bolland MJ, Avenell A, Gamble GD, Grey A. (2016) <doi:10.1212/WNL.0000000000003387>. Bolland MJ, Gamble GD, Avenell A, Grey A, Lumley T. (2019) <doi:10.1016/j.jclinepi.2019.05.006>. Bolland MJ, Gamble GD, Avenell A, Grey A. (2019) <doi:10.1016/j.jclinepi.2019.03.001>. Bolland MJ, Gamble GD, Grey A, Avenell A. (2020) <doi:10.1111/anae.15165>. Bolland MJ, Gamble GD, Avenell A, Cooper DJ, Grey A. (2021) <doi:10.1016/j.jclinepi.2020.11.012>. Bolland MJ, Gamble GD, Avenell A, Grey A. (2021) <doi:10.1016/j.jclinepi.2021.05.002>. Bolland MJ, Gamble GD, Avenell A, Cooper DJ, Grey A. (2023) <doi:10.1016/j.jclinepi.2022.12.018>. Carlisle JB, Loadsman JA. (2017) <doi:10.1111/anae.13650>. Carlisle JB. (2017) <doi:10.1111/anae.13938>.

This package provides a robust alternative to the aJIVE (angle based Joint and Individual Variation Explained) method (Feng et al 2018: <doi:10.1016/j.jmva.2018.03.008>) for the estimation of joint and individual components in the presence of outliers in multi-source data. It decomposes the multi-source data into joint, individual and residual (noise) contributions. The decomposition is robust to outliers and noise in the data. The method is illustrated in Ponzi et al (2021) <arXiv:2101.09110>.

Resource Selection (Probability) Functions for use-availability wildlife data based on weighted distributions as described in Lele and Keim (2006) <doi:10.1890/0012-9658(2006)87%5B3021:WDAEOR%5D2.0.CO;2>, Lele (2009) <doi:10.2193/2007-535>, and Solymos & Lele (2016) <doi:10.1111/2041-210X.12432>.

Various statistical, graphics, and data-management functions used by the Rcmdr package in the R Commander GUI for R.

Upload R data.frame to Arm Treasure Data, see <https://www.treasuredata.com/>. You can execute database or table handling for resources on Arm Treasure Data.

This package provides functions to write messages to the syslog system logger API, available on all POSIX'-compatible operating systems. Features include tagging messages with a priority level and application type, as well as masking (hiding) messages below a given priority level.

The Bayesian modelling of relative sea-level data using a comprehensive approach that incorporates various statistical models within a unifying framework. Details regarding each statistical models; linear regression (Ashe et al 2019) <doi:10.1016/j.quascirev.2018.10.032>, change point models (Cahill et al 2015) <doi:10.1088/1748-9326/10/8/084002>, integrated Gaussian process models (Cahill et al 2015) <doi:10.1214/15-AOAS824>, temporal splines (Upton et al 2023) <arXiv:2301.09556>, spatio-temporal splines (Upton et al 2023) <arXiv:2301.09556> and generalised additive models (Upton et al 2023) <arXiv:2301.09556>. This package facilitates data loading, model fitting and result summarisation. Notably, it accommodates the inherent measurement errors found in relative sea-level data across multiple dimensions, allowing for their inclusion in the statistical models.

This package performs regularization of differential item functioning (DIF) parameters in item response theory (IRT) models (Belzak & Bauer, 2020) <https://pubmed.ncbi.nlm.nih.gov/31916799/> using a penalized expectation-maximization algorithm.

This package provides functions for assessing variable relations and associations prior to modeling with a Random Forest algorithm (although these are relevant for any predictive model). Metrics such as partial correlations and variance inflation factors are tabulated as well as plotted for the user. A function is available for tuning the main Random Forest hyper-parameter based on model performance and variable importance metrics. This grid-search technique provides tables and plots showing the effect of the main hyper-parameter on each of the assessment metrics. It also returns each of the evaluated models to the user. The package also provides superior variable importance plots for individual models. All of the plots are developed so that the user has the ability to edit and improve further upon the plots. Derivations and methodology are described in Bladen (2022) <https://digitalcommons.usu.edu/etd/8587/>.

Inspired by the classic RSA', we developed the improved Generalized Reporter Score-based Analysis (GRSA) method, implemented in the R package ReporterScore', along with comprehensive visualization methods and pathway databases. GRSA is a threshold-free method that works well with all types of biomedical features, such as genes, chemical compounds, and microbial species. Importantly, the GRSA supports multi-group and longitudinal experimental designs, because of the included multi-group-compatible statistical methods.

This package provides a general-purpose optimisation engine that supports i) Monte Carlo optimisation with Metropolis criterion [Metropolis et al. (1953) <doi:10.1063/1.1699114>, Hastings (1970) <doi:10.1093/biomet/57.1.97>] and Acceptance Ratio Simulated Annealing [Kirkpatrick et al. (1983) <doi:10.1126/science.220.4598.671>, Ä erný (1985) <doi:10.1007/BF00940812>] on multiple cores, and ii) Acceptance Ratio Replica Exchange Monte Carlo Optimisation. In each case, the system pseudo-temperature is dynamically adjusted such that the observed acceptance ratio is kept near to the desired (fixed or changing) acceptance ratio.

Facilitates the design and generation of optimal color (or symbol) codes that can be used to mark and identify individual animals. These codes are made such that the IDs are robust to partial erasure: even if sections of the code are lost, the entire identity of the animal can be reconstructed. Thus, animal subjects are not confused and no ambiguity is introduced.

Interface for the Google Ads API'. Google Ads is an online advertising service that enables advertisers to display advertising to web users (see <https://developers.google.com/google-ads/> for more information).

Database data model management utilities for R packages in the Observational Health Data Sciences and Informatics programme. ResultModelManager provides utility functions to allow package maintainers to migrate existing SQL database models, export and import results in consistent patterns.

This package provides two general frameworks to generate a multi-layer network. This also provides several methods to reveal the embedding of both nodes and layers. The reference paper can be found from the URL mentioned below. Ting Li, Zhongyuan Lyu, Chenyu Ren, Dong Xia (2023) <arXiv:2302.04437>.

When creating a package, authors may sometimes struggle with coming up with easy and straightforward function names, and at the same time hoping that other packages do not already have the same function names. In trying to meet this goal, sometimes, function names are not descriptive enough and may confuse the potential users. The purpose of this package is to serve as a package function short form generator and also provide shorthand names for other functions. Having this package will entice authors to create long function names without the fear of users not wanting to use their packages because of the long names. In a way, everyone wins - the authors can use long descriptive function names, and the users can use this package to make short functions names while still using the package in question.

It streamlines the evaluation of regression model assumptions, enhancing result reliability. With integrated tools for assessing key aspects like linearity, homoscedasticity, and more. It's a valuable asset for researchers and analysts working with regression models.

This package provides a lightweight toolkit to validate new observations when computing their predictions with a predictive model. The validation process consists of two steps: (1) record relevant statistics and meta data of the variables in the original training data for the predictive model and (2) use these data to run a set of basic validation tests on the new set of observations.

This package provides an API to work with Redatam (see <https://redatam.org>) databases in both formats: RXDB (new format) and DICX (old format) and running Redatam programs written in SPC language. It's a wrapper around Redatam core and provides functions to open/close a database (redatam_open()/redatam_close()), list entities and variables from the database (redatam_entities(), redatam_variables()) and execute a SPC program and gets the results as data frames (redatam_query(), redatam_run()).

Eurostat is the statistical office of the European Union and provides high quality statistics for Europe. Large set of the data is disseminated through the Eurostat database (<https://ec.europa.eu/eurostat/web/main/data/database>). The tools are using the REST API with the Statistical Data and Metadata eXchange (SDMX) Web Services (<https://ec.europa.eu/eurostat/web/user-guides/data-browser/api-data-access/api-detailed-guidelines/sdmx2-1>) to search and download data from the Eurostat database using the SDMX standard.

This package implements the Zig-Zag algorithm (Bierkens, Fearnhead, Roberts, 2016) <arXiv:1607.03188> applied and Bouncy Particle Sampler <arXiv:1510.02451> for a Gaussian target and Student distribution.

R implementation of the FAIR Data Pipeline API'. The FAIR Data Pipeline is intended to enable tracking of provenance of FAIR (findable, accessible and interoperable) data used in epidemiological modelling.

This package provides functions for calculating life history metrics using matrix population models ('MPMs'). Described in Jones et al. (2021) <doi:10.1101/2021.04.26.441330>.

This package provides a tool to read and manipulate data generated from RiverWare'(TM) <https://www.riverware.org/> simulations. RiverWare and RiverSMART generate data in "rdf", "csv", and "nc" format. This package provides an interface to read, aggregate, and summarize data from one or more simulations in a dplyr pipeline.