Allows for multiple group item response theory alignment a la Mplus to be applied to lists of single-group models estimated in lavaan or mirt'. Allows item sets that are overlapping but not identical, facilitating alignment in secondary data analysis where not all items may be shared across assessments.

Acoustic target strength (TS) represents the intensity of an echo returning from an individual scatterer such as bubbles, fish, or zooplankton. TS can be used to convert integrated or volumetric backscatter collected from fisheries acoustic surveys into units of number density (e.g. animals per m^3), abundance (e.g. number of animals), and biomass (e.g. kg). This parameter can also be used to aid in classifying backscatter, such as separating likely echoes of large predatory fish (e.g. adult cod) from smaller prey (e.g. shrimp). One way to estimate TS is to use physics-based models to calculate theoretical TS that comprise exact and approximate solutions as well as analytical approaches. The models provided can help provide TS estimates over broad statistical distributions of model parameters. Applications are described by Lucca et al. (2023) <doi:10.1121/10.0022459>, with fisheries-acoustics principles from Simmonds and MacLennan (2005) <doi:10.1002/9780470995303>.

Interface package for sala', the spatial network analysis library from the depthmapX software application. The R parts of the code are based on the rdepthmap package. Allows for the analysis of urban and building-scale networks and provides metrics and methods usually found within the Space Syntax domain. Methods in this package are described by K. Al-Sayed, A. Turner, B. Hillier, S. Iida and A. Penn (2014) "Space Syntax methodology", and also by A. Turner (2004) <https://discovery.ucl.ac.uk/id/eprint/2651> "Depthmap 4: a researcher's handbook".

This package implements Bayesian estimation and inference for alpha-mixture survival models, including Weibull and Exponential based components, with tools for simulation and posterior summaries. The methods target applications in reliability and biomedical survival analysis. The package implements Bayesian estimation for the alpha-mixture methodology introduced in Asadi et al. (2019) <doi:10.1017/jpr.2019.72>.

Fit, compare, and visualise linear and nonlinear regression models tailored to field-trial and dose-response agricultural data. Provides S3 classes for mixed-effects models (via lme4'), nonlinear growth curves (logistic, Gompertz', asymptotic, linear-plateau, quadratic), and four/five-parameter log-logistic dose-response models (via drc'). Includes automated starting-value heuristics, goodness-of-fit statistics, residual diagnostics, and ggplot2'-based visualisation. Methods are based on Bates and Watts (1988, ISBN:9780471816430), Ritz and others (2015) <doi:10.1371/journal.pone.0146021>, and Bates and others (2015) <doi:10.18637/jss.v067.i01>.

Connect to the Adobe Analytics API v2.0 <https://github.com/AdobeDocs/analytics-2.0-apis> which powers Analysis Workspace'. The package was developed with the analyst in mind, and it will continue to be developed with the guiding principles of iterative, repeatable, timely analysis.

Simulates battles in the board game Axis and Allies Spring 1942, and calculates your probability of winning a battle. This speeds the game up significantly.

Raw and processed versions of the data from De Cock (2011) <http://ww2.amstat.org/publications/jse> are included in the package.

This package provides automated machine learning workflows for survival analysis, binary classification, continuous outcomes, and ordinal outcomes. The package trains and combines model variants across user-supplied multi-cohort data, evaluates survival models by leave-one-out cross-validation using Harrell's concordance index, binary models by leave-one-out cross-validation using receiver operating characteristic area under the curve, continuous models by out-of-fold root mean squared error and R-squared, and ordinal models by out-of-fold quadratic weighted kappa. It renders reproducible reports in Hypertext Markup Language (HTML) with figures and diagnostics. The survival workflow supports penalized and tree-based Cox proportional hazards models, stepwise Cox models, partial least squares regression for Cox models, supervised principal components, gradient boosting machine Cox models, survival support vector machines (survival-SVM), random survival forests, and optional CoxBoost'. The binary workflow supports penalized logistic regression, logistic baselines, gradient boosting machines, random forests, principal component analysis (PCA) logistic regression, and Gaussian naive Bayes variants. Continuous and ordinal workflows reuse an 18-variant regression registry with penalized, linear, boosted, forest, PCA, and baseline families. The optional CoxBoost model is enabled when the suggested CoxBoost package is installed; it is used conditionally and is not a strong dependency. Optional model backends are checked at run time so missing backend packages skip only the affected model variants rather than blocking installation of the whole package. Methods build on Friedman et al. (2010) <doi:10.18637/jss.v033.i01>, Bair and Tibshirani (2004) <doi:10.1371/journal.pbio.0020108>, Ishwaran et al. (2008) <doi:10.1214/08-AOAS169>, Blanche et al. (2013) <doi:10.1002/sim.5958>, and Binder and Schumacher (2008) <doi:10.1186/1471-2105-9-14>.

Inference of protein complex states from quantitative proteomics data. The package takes information on known stable protein interactions (i.e. protein components of the same complex) and assesses how protein quantitative ratios change between different conditions. It reports protein pairs for which relative protein quantities to each other have been significantly altered in the tested condition.

It extends the functionality of logger package. Additional logging metadata can be configured to be collected. Logging messages are displayed on console and optionally they are sent to Azure Log Analytics workspace in real-time.

Utility functions to check data, variables and conditions for functions used in admiral and admiral extension packages. Additional utility helper functions to assist developers with maintaining documentation, testing and general upkeep of admiral and admiral extension packages.

Easy data analysis and quality checks which are commonly used in data science. It combines the tabular and graphical visualization for easier usability. This package also creates an R Notebook with detailed data exploration with one function call. The notebook can be made interactive.

Uses Auth0 API (see <https://auth0.com> for more information) to use a simple authentication system. It provides tools to log in and out a shiny application using social networks or a list of e-mails.

Browse through a continuously updated list of existing RStudio addins and install/uninstall their corresponding packages.

This package provides a collection of functions to construct A-optimal block designs for comparing test treatments with one or more control(s). Mainly A-optimal balanced treatment incomplete block designs, weighted A-optimal balanced treatment incomplete block designs, A-optimal group divisible treatment designs and A-optimal balanced bipartite block designs can be constructed using the package. The designs are constructed using algorithms based on linear integer programming. To the best of our knowledge, these facilities to construct A-optimal block designs for comparing test treatments with one or more controls are not available in the existing R packages. For more details on designs for tests versus control(s) comparisons, please see Hedayat, A. S. and Majumdar, D. (1984) <doi:10.1080/00401706.1984.10487989> A-Optimal Incomplete Block Designs for Control-Test Treatment Comparisons, Technometrics, 26, 363-370 and Mandal, B. N. , Gupta, V. K., Parsad, Rajender. (2017) <doi:10.1080/03610926.2015.1071394> Balanced treatment incomplete block designs through integer programming. Communications in Statistics - Theory and Methods 46(8), 3728-3737.

We extend existing gene enrichment tests to perform adverse event enrichment analysis. Unlike the continuous gene expression data, adverse event data are counts. Therefore, adverse event data has many zeros and ties. We propose two enrichment tests. One is a modified Fisher's exact test based on pre-selected significant adverse events, while the other is based on a modified Kolmogorov-Smirnov statistic. We add Covariate adjustment to improve the analysis."Adverse event enrichment tests using VAERS" Shuoran Li, Lili Zhao (2020) <doi:10.48550/arXiv.2007.02266>.

Helper functions for working with Regional Ocean Modeling System ROMS output. See <https://www.myroms.org/> for more information about ROMS'.

This package provides methods for high-throughput adaptive immune receptor repertoire sequencing (AIRR-Seq; Rep-Seq) analysis. In particular, immunoglobulin (Ig) sequence lineage reconstruction, lineage topology analysis, diversity profiling, amino acid property analysis and gene usage. Citations: Gupta and Vander Heiden, et al (2017) <doi:10.1093/bioinformatics/btv359>, Stern, Yaari and Vander Heiden, et al (2014) <doi:10.1126/scitranslmed.3008879>.

The maximum likelihood estimator (MLE) is a technology: under regularity conditions, any MLE is asymptotically normal with variance given by the inverse Fisher information. This package exploits that structure by defining an algebra over MLEs. Compose independent estimators into joint MLEs via block-diagonal covariance ('joint'), optimally combine repeated estimates via inverse-variance weighting ('combine'), propagate transformations via the delta method ('rmap'), and bridge to distribution algebra via conversion to normal or multivariate normal objects ('as_dist'). Supports asymptotic ('mle', mle_numerical') and bootstrap ('mle_boot') estimators with a unified interface for inference: confidence intervals, standard errors, AIC, Fisher information, and predictive intervals. For background on maximum likelihood estimation, see Casella and Berger (2002, ISBN:978-0534243128). For the delta method and variance estimation, see Lehmann and Casella (1998, ISBN:978-0387985022).

Pair of simple convenience functions to convert a vector of birth dates to age and age distributions. These functions may be helpful when related age and custom age distributions are desired given a vector of birth dates.

Adaptive and Robust Transfer Learning (ART) is a flexible framework for transfer learning that integrates information from auxiliary data sources to improve model performance on primary tasks. It is designed to be robust against negative transfer by including the non-transfer model in the candidate pool, ensuring stable performance even when auxiliary datasets are less informative. See the paper, Wang, Wu, and Ye (2023) <doi:10.1002/sta4.582>.

This package provides a collection of several pharmacovigilance signal detection methods based on adaptive lasso. Additional lasso-based and propensity score-based signal detection approaches are also supplied. See Courtois et al <doi:10.1186/s12874-021-01450-3>.

Interface to the Azure Machine Learning Software Development Kit ('SDK'). Data scientists can use the SDK to train, deploy, automate, and manage machine learning models on the Azure Machine Learning service. To learn more about Azure Machine Learning visit the website: <https://docs.microsoft.com/en-us/azure/machine-learning/service/overview-what-is-azure-ml>.