This package provides comprehensive analytics, reporting, and testing capabilities for systematic review search strategies. The package focuses on validating search performance, generating standardized PRISMA'-compliant reports, and ensuring reproducibility in evidence synthesis. Features include precision-recall analysis, cross-database performance comparison, benchmark validation against gold standards, sensitivity analysis, temporal coverage assessment, automated report generation, and statistical comparison of search strategies. Supports multiple export formats including CSV', Excel', RIS', BibTeX', and EndNote'. Includes tools for duplicate detection, search strategy optimization, cross-validation frameworks, meta-analysis of benchmark results, power analysis for study design, and reproducibility package creation. Optionally connects to PubMed for direct database searching and real-time strategy comparison using the E-utilities API'. Enhanced with bootstrap comparison methods, McNemar test for strategy evaluation, and comprehensive visualization tools for performance assessment. Methods based on Manning et al. (2008) for information retrieval metrics, Moher et al. (2009) for PRISMA guidelines, and Sampson et al. (2006) for systematic review search methodology.

Tidies up the forecasting modeling and prediction work flow, extends the broom package with sw_tidy', sw_glance', sw_augment', and sw_tidy_decomp functions for various forecasting models, and enables converting forecast objects to "tidy" data frames with sw_sweep'.

Simulation methods to study the effect of management policies on efforts to restore populations back to their original genetic composition. Allows for single-scenario simulation and for optimization of specific chosen scenarios. Further information can be found in Hernandez, Janzen and Lavretsky (2023) <doi:10.1111/1755-0998.13892>.

Seeded Sequential LDA can classify sentences of texts into pre-define topics with a small number of seed words (Watanabe & Baturo, 2023) <doi:10.1177/08944393231178605>. Implements Seeded LDA (Lu et al., 2010) <doi:10.1109/ICDMW.2011.125> and Sequential LDA (Du et al., 2012) <doi:10.1007/s10115-011-0425-1> with the distributed LDA algorithm (Newman, et al., 2009) for parallel computing.

Collection of shiny application styling that are the based on the GOV.UK Design System. See <https://design-system.service.gov.uk/components/> for details.

Last.fm'<https://www.last.fm> is a music platform focussed on building a detailed profile of a users listening habits. It does this by scrobbling (recording) every track you listen to on other platforms ('spotify', youtube', soundcloud etc) and transferring them to your Last.fm database. This allows Last.fm to act as a complete record of your entire listening history. scrobbler provides helper functions to download and analyse your listening history in R.

Provide data generation and estimation tools for the multivariate scale mixtures of normal presented in Lange and Sinsheimer (1993) <doi:10.2307/1390698>, the multivariate scale mixtures of skew-normal presented in Zeller, Lachos and Vilca (2011) <doi:10.1080/02664760903406504>, the multivariate skew scale mixtures of normal presented in Louredo, Zeller and Ferreira (2021) <doi:10.1007/s13571-021-00257-y> and the multivariate scale mixtures of skew-normal-Cauchy presented in Kahrari et al. (2020) <doi:10.1080/03610918.2020.1804582>.

Ratings, votes, swear words and sentiments are analysed for the show SouthPark through a Shiny application after web scraping from IMDB and the website <https://southpark.fandom.com/wiki/South_Park_Archives>.

This package implements named semaphores from the boost C++ library <https://www.boost.org/> for interprocess communication. Multiple R sessions on the same host can block (with optional timeout) on a semaphore until it becomes positive, then atomically decrement it and unblock. Any session can increment the semaphore.

Estimate Bayesian nested mixture models via Markov Chain Monte Carlo methods. Specifically, the package implements the common atoms model (Denti et al., 2023), and hybrid finite-infinite models. All models use Gaussian mixtures with a normal-inverse-gamma prior distribution on the parameters. Additional functions are provided to help analyzing the results of the fitting procedure. References: Denti, Camerlenghi, Guindani, Mira (2023) <doi:10.1080/01621459.2021.1933499>, Dâ Angelo, Denti (2024) <doi:10.1214/24-BA1458>.

R language bindings for SolveBio's API. SolveBio is a biomedical knowledge hub that enables life science organizations to collect and harmonize the complex, disparate "multi-omic" data essential for today's R&D and BI needs.

Database of genes which frequently sustain somatic mutations, but are unlikely to drive cancer.

An algorithm for identifying high-resolution driver elements for datasets from a high-definition reporter assay library. Xinchen Wang, Liang He, Sarah Goggin, Alham Saadat, Li Wang, Melina Claussnitzer, Manolis Kellis (2017) <doi:10.1101/193136>.

This package contains functions for estimating the STARTS model of Kenny and Zautra (1995, 2001) <DOI:10.1037/0022-006X.63.1.52>, <DOI:10.1037/10409-008>. Penalized maximum likelihood estimation and Markov Chain Monte Carlo estimation are also provided, see Luedtke, Robitzsch and Wagner (2018) <DOI:10.1037/met0000155>.

Fits a semiparametric spatiotemporal model for data with mixed frequencies, specifically where the response variable is observed at a lower frequency than some covariates. The estimation uses an iterative backfitting algorithm that combines a non-parametric smoothing spline for high-frequency data, parametric estimation for low-frequency and spatial neighborhood effects, and an autoregressive error structure. Methodology based on Malabanan, Lansangan, and Barrios (2022) <https://scienggj.org/2022/SciEnggJ%202022-vol15-no02-p90-107-Malabanan%20et%20al.pdf>.

Access Amazon Web Service Simple Storage Service ('S3') <https://aws.amazon.com/s3/> as if it were a file system. Interface based on the R package fs'.

This package provides a wrapper for Blizzard's Starcraft II (a 2010 real-time strategy game) Application Programming Interface (API). All documented API calls are implemented in an easy-to-use and consistent manner.

This package provides a mixture model for clustering individuals (or sampling groups) into stocks based on their genetic profile. Here, sampling groups are individuals that are sure to come from the same stock (e.g. breeding adults or larvae). The mixture (log-)likelihood is maximised using the EM-algorithm after finding good starting values via a K-means clustering of the genetic data. Details can be found in: Foster, S. D.; Feutry, P.; Grewe, P. M.; Berry, O.; Hui, F. K. C. & Davies (2020) <doi:10.1111/1755-0998.12920>.

Computes the entire regularization path for the two-class svm classifier with essentially the same cost as a single SVM fit.

Estimates previously compiled state-space modeling for mouse-tracking experiments using the rstan package, which provides the R interface to the Stan C++ library for Bayesian estimation.

This implementation of the Empirical Mode Decomposition (EMD) works in 2 dimensions simultaneously, and can be applied on spatial data. It can handle both gridded or un-gridded datasets.

Shiny Module to create, visualize, customize and export Excel-like pivot table.

Stop signal task data of go and stop trials is generated per participant. The simulation process is based on the generally non-independent horse race model and fixed stop signal delay or tracking method. Each of go and stop process is assumed having exponentially modified Gaussian(ExG) or Shifted Wald (SW) distributions. The output data can be converted to BEESTS software input data enabling researchers to test and evaluate various brain stopping processes manifested by ExG or SW distributional parameters of interest. Methods are described in: Soltanifar M (2020) <https://hdl.handle.net/1807/101208>, Matzke D, Love J, Wiecki TV, Brown SD, Logan GD and Wagenmakers E-J (2013) <doi:10.3389/fpsyg.2013.00918>, Logan GD, Van Zandt T, Verbruggen F, Wagenmakers EJ. (2014) <doi:10.1037/a0035230>.

Implementation of the SAM prior and generation of its operating characteristics for dynamically borrowing information from historical data. For details, please refer to Yang et al. (2023) <doi:10.1111/biom.13927>.