Provides utilities for cleaning survey data, computing weights, and performing descriptive statistical analysis. Methods follow Lohr (2019, ISBN:978-0367272454) "Sampling: Design and Analysis" and Lumley (2010) &lt;<a href="https://doi.org/10.1002%2F9780470580066" target="_top">doi:10.1002/9780470580066</a>&gt;.

Provides functions to compute the Rank-Based Stability Index (RSI) for genotype by environment interaction data, along with a genotype plus genotype-by-environment (GGE) style biplot visualization of stability.

Implements the Multi-view Aggregated Two-Sample (MATES) test, a powerful nonparametric method for testing equality of two multivariate distributions. The method constructs multiple graph-based statistics from various perspectives (views) including different distance metrics, graph types (nearest neighbor graphs, minimum spanning trees, and robust nearest neighbor graphs), and weighting schemes. These statistics are then aggregated through a quadratic form to achieve improved statistical power. The package provides both asymptotic closed-form inference and permutation-based testing procedures. For methodological details, see Cai and others (2026+) &lt;<a href="https://doi.org/10.48550%2FarXiv.2412.16684" target="_top">doi:10.48550/arXiv.2412.16684</a>&gt;.

The futurize() function transpiles calls to sequential map-reduce functions such as base::lapply(), purrr::map(), 'foreach::foreach() %do% { ... }' into concurrent alternatives, providing you with a simple, straightforward path to scalable parallel computing via the 'future' ecosystem &lt;<a href="https://doi.org/10.32614%2FRJ-2021-048" target="_top">doi:10.32614/RJ-2021-048</a>&gt;. By combining this function with R's native pipe operator, you have an convenient way for speeding up iterative computations with minimal refactoring, e.g. 'lapply(xs, fcn) |&gt; futurize()', 'purrr::map(xs, fcn) |&gt; futurize()', and 'foreach::foreach(x = xs) %do% { fcn(x) } |&gt; futurize()'. Other map-reduce packages that be "futurized" are 'BiocParallel', 'plyr', 'crossmap' packages. There is also support for growing set of domain-specific packages, including 'boot', 'glmnet', 'mgcv', 'lme4', and 'tm'.

Download 'Alphavantage financial data' &lt;<a href="https://www.alphavantage.co/documentation/" target="_top">https://www.alphavantage.co/documentation/</a>&gt; to reduced 'data.table' objects. Includes support functions to extract and simplify complex data returned from API calls.

https://cran.r-project.org/package=valytics