Simplifies integration with Amazon Cognito (<https://aws.amazon.com/cognito/>) for R developers, enabling easy management of user authentication, registration, and password flows.

Robust tests (RW, RPB and RGF) are provided for testing the equality of several long-tailed symmetric (LTS) means when the variances are unknown and arbitrary. RW, RPB and RGF tests are robust versions of Welch's F test proposed by Welch (1951) <doi:10.2307/2332579>, parametric bootstrap test proposed by Krishnamoorthy et. al (2007) <doi:10.1016/j.csda.2006.09.039>; and generalized F test proposed by Weerahandi (1995) <doi:10.2307/2532947>;, respectively. These tests are based on the modified maximum likelihood (MML) estimators proposed by Tiku(1967, 1968) <doi:10.2307/2333859>, <doi:10.1080/01621459.1968.11009228>.

R Markdown output formats based on JavaScript libraries such as Scrollama (<https://github.com/russellsamora/scrollama>) for storytelling.

Rcpp bindings for PLANC', a highly parallel and extensible NMF/NTF (Non-negative Matrix/Tensor Factorization) library. Wraps algorithms described in Kannan et. al (2018) <doi:10.1109/TKDE.2017.2767592> and Eswar et. al (2021) <doi:10.1145/3432185>. Implements algorithms described in Welch et al. (2019) <doi:10.1016/j.cell.2019.05.006>, Gao et al. (2021) <doi:10.1038/s41587-021-00867-x>, and Kriebel & Welch (2022) <doi:10.1038/s41467-022-28431-4>.

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.

This package provides methods for calculation and visualization of the Repertoire Dissimilarity Index. Citation: Bolen and Rubelt, et al (2017) <doi:10.1186/s12859-017-1556-5>.

This package contains a collection of helper functions to use with rbi', the R interface to LibBi', described in Murray et al. (2015) <doi:10.18637/jss.v067.i10>. It contains functions to adapt the proposal distribution and number of particles in particle Markov-Chain Monte Carlo, as well as calculating the Deviance Information Criterion (DIC) and converting between times in LibBi results and R time/dates.

Efficient solvers for 10 regularized multi-task learning algorithms applicable for regression, classification, joint feature selection, task clustering, low-rank learning, sparse learning and network incorporation. Based on the accelerated gradient descent method, the algorithms feature a state-of-art computational complexity O(1/k^2). Sparse model structure is induced by the solving the proximal operator. The detail of the package is described in the paper of Han Cao and Emanuel Schwarz (2018) <doi:10.1093/bioinformatics/bty831>.

Implementation of JQuery <https://jquery.com> and CSS styles to allow easy incorporation of various social media elements on a page. The elements include addition of share buttons or connect with us buttons or hyperlink buttons to Shiny applications or dashboards and Rmarkdown documents.Sharing capability on social media platforms including Facebook <https://www.facebook.com>, Linkedin <https://www.linkedin.com>, X/Twitter <https://x.com>, Tumblr <https://www.tumblr.com>, Pinterest <https://www.pinterest.com>, Whatsapp <https://www.whatsapp.com>, Reddit <https://www.reddit.com>, Baidu <https://www.baidu.com>, Blogger <https://www.blogger.com>, Weibo <https://www.weibo.com>, Instagram <https://www.instagram.com>, Telegram <https://www.telegram.me>, Youtube <https://www.youtube.com>.

Fits measurement error models using Monte Carlo Expectation Maximization (MCEM). For specific details on the methodology, see: Greg C. G. Wei & Martin A. Tanner (1990) A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms, Journal of the American Statistical Association, 85:411, 699-704 <doi:10.1080/01621459.1990.10474930> For more examples on measurement error modelling using MCEM, see the RMarkdown vignette: "'refitME R-package tutorial".

Network-based regularization has achieved success in variable selection for high-dimensional biological data due to its ability to incorporate correlations among genomic features. This package provides procedures of network-based variable selection for generalized linear models (Ren et al. (2017) <doi:10.1186/s12863-017-0495-5> and Ren et al.(2019) <doi:10.1002/gepi.22194>). Continuous, binary, and survival response are supported. Robust network-based methods are available for continuous and survival responses.

Automatic resizing toolbar for containers, images and tables. Various resizer or expandable container functionalities are also included. Most suitable to include in shiny', markdown and quarto documents.

This package provides methods and tools for implementing functional singular spectrum analysis and related techniques.

Rank-based (R) estimation and inference for linear models. Estimation is for general scores and a library of commonly used score functions is included.

This is a meta-package designed to support the installation of Rmosek (>= 6.0) and bring the optimization facilities of MOSEK (>= 6.0) to the R-language. The interface supports large-scale optimization of many kinds: Mixed-integer and continuous linear, second-order cone, exponential cone and power cone optimization, as well as continuous semidefinite optimization. Rmosek and the R-language are open-source projects. MOSEK is a proprietary product, but unrestricted trial and academic licenses are available.

This package provides a small language extension for succinct conditional assignment using `?` and `:`, emulating the conditional ternary operator syntax using in C, Java, JavaScript and other languages.

The rearrangement operator (Hardy, Littlewood, and Polya 1952) for univariate, bivariate, and trivariate point estimates of monotonic functions. The package additionally provides a function that creates simultaneous confidence intervals for univariate functions and applies the rearrangement operator to these confidence intervals.

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 color schemes for maps and other graphics designed by CARTO as described at <https://carto.com/carto-colors/>. It includes four types of palettes: aggregation, diverging, qualitative, and quantitative.

R^2 measure of explained variation under the semiparametric additive hazards model is estimated. The measure can be used as a measure of predictive capability and therefore it can be adopted in model selection process. Rava, D. and Xu, R. (2020) <arXiv:2003.09460>.

Fits linear models to repeated ordinal scores using GEE methodology.

This is a Google Forms and Google Classroom API Wrapper for R for managing Google Classrooms from R. The documentation for these APIs is here <https://developers.google.com/forms/api/guides> .

Interface to access data via the United States Department of Agriculture's National Agricultural Statistical Service (NASS) Quick Stats web API <https://quickstats.nass.usda.gov/api/>. Convenience functions facilitate building queries based on available parameters and valid parameter values. This product uses the NASS API but is not endorsed or certified by NASS.

This package provides methods for model building and model evaluation of mixed effects models using Monolix <https://monolix.lixoft.com>. Monolix is a software tool for nonlinear mixed effects modeling that must have been installed in order to use Rsmlx'. Among other tasks, Rsmlx provides a powerful tool for automatic PK model building, performs statistical tests for model assessment, bootstrap simulation and likelihood profiling for computing confidence intervals. Rsmlx also proposes several automatic covariate search methods for mixed effects models.