Frequently used generics and helper functions used in 'anvl' and its companion packages 'stablehlo' and 'pjrt'. Comes with S3 generics to query arrays (shape, data type, device) and to convert them to R arrays and raw vectors, as well as a class for data types.

Fits multivariate Gaussian-mixture proxies that are Kullback-Leibler optimal to user-supplied target densities on real Euclidean space. Three fitting regimes are unified under one verb: (i) closed-form moment matching for a single component, (ii) classical expectation-maximisation when independent samples are available, and (iii) importance-sampled expectation-maximisation that minimises the Kullback-Leibler divergence when the target can be evaluated point-wise but not (cheaply) sampled. Closed-form Gaussian-mixture operators (density, sampling, marginalisation, conditioning, divergence) round out the toolkit. The conditioning operator drives multiple imputation of data missing at random, covering the multimodal and heteroscedastic cases a single-Gaussian model cannot represent. Implements the regime hierarchy of van der Hoek and Elliott (2024) &lt;<a href="https://doi.org/10.1080%2F07362994.2024.2372605" target="_top">doi:10.1080/07362994.2024.2372605</a>&gt;.

Provides tools for generating simulated study data, creating matched participant pairs using optimal nonbipartite matching, randomizing participants within pairs into study groups, and assessing post-randomization balance using descriptive summary statistics and standardized mean differences. The matching methodology is based on Lu et al. (2011) &lt;<a href="https://doi.org/10.1198%2Ftast.2011.08294" target="_top">doi:10.1198/tast.2011.08294</a>&gt;.

Evaluation of synchronous deposition across different event records using a log-ratio approach, and syncing age-depth models based on the availability of isochrons. Following the methodology of Wils &amp; Ramisch (2026) &lt;<a href="https://doi.org/10.1038%2Fs41598-026-67943-7" target="_top">doi:10.1038/s41598-026-67943-7</a>&gt;. The synthetic example dataset used in the examples, tests and vignette is distributed separately in the companion data package 'SyncERdata'.

Tools for specifying, analyzing and simulating models of social influence network theory based on the Friedkin-Johnsen model, Friedkin and Johnsen (1990) &lt;<a href="https://doi.org/10.1080%2F0022250X.1990.9990069" target="_top">doi:10.1080/0022250X.1990.9990069</a>&gt;, which includes the consensus model of DeGroot (1974) &lt;<a href="https://doi.org/10.1080%2F01621459.1974.10480137" target="_top">doi:10.1080/01621459.1974.10480137</a>&gt; as a special case. Equilibrium opinions, total influence matrices and convergence diagnostics are computed in closed form, also for signed networks with antagonistic ties, Altafini (2013) &lt;<a href="https://doi.org/10.1109%2FTAC.2012.2224251" target="_top">doi:10.1109/TAC.2012.2224251</a>&gt;. Simulations allow influence weights and susceptibilities to depend on time and on the state of the system, and can couple latent opinions with manifest responses through logistic or threshold response functions, whose results are aggregated into collective outcomes by quota rules.

Designing and comparing modified chain sampling inspection plan (MChSP). This package implements ChSP-1, MChSP-1, Multiple Dependent State Sampling (MDS), and Modified Chain Sampling plans (MChSP). The plans use user-supplied failure probabilities and determine the minimum sample size subject to consumer's risk constraints. Functions are provided to compare the required sample sizes of the four plans and visualize their performance. The distribution-free formulation allows the methods to be used with different lifetime distributions. Luca (2018) &lt;<a href="https://doi.org/10.1080%2F02664763.2017.1375084" target="_top">doi:10.1080/02664763.2017.1375084</a>&gt;; Tripathi et al. (2021) &lt;<a href="https://doi.org/10.32604%2Fcsse.2021.015624" target="_top">doi:10.32604/csse.2021.015624</a>&gt;; Tripathi et al. (2023) &lt;<a href="https://doi.org/10.1007%2Fs41872-023-00215-9" target="_top">doi:10.1007/s41872-023-00215-9</a>&gt;; Rao et al. (2025) &lt;<a href="https://doi.org/10.1080%2F27684520.2025.2531810" target="_top">doi:10.1080/27684520.2025.2531810</a>&gt;; Tripathi and Saha (2023) &lt;<a href="https://doi.org/10.1007%2Fs13198-023-02221-7" target="_top">doi:10.1007/s13198-023-02221-7</a>&gt;.

Read released, versioned data from MetaLab (&lt;<a href="https://metalab.stanford.edu" target="_top">https://metalab.stanford.edu</a>&gt;), a database of community-augmented meta-analyses of language acquisition and cognitive development; validate candidate datasets against the MetaLab field specification; compute standardized effect sizes; and draw the standard MetaLab visualizations (scatter, violin, forest, and funnel plots) backed by multilevel random-effects models.

Calculate and extract remote sensing metrics for spatial analysis in the field of health. The package offers R users a quick and straightforward way to obtain areal or zonal statistics of key environmental indicators, covariates, and vector-borne disease data ideal for modeling infectious diseases within the framework of spatial epidemiology.

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

Turns a 'gt' table into a range of cells in an 'openxlsx2' workbook, keeping the heading, column spanners, row groups, stub, summary rows, footnotes and the styling set through 'gt'. Numbers stay numbers wherever a spreadsheet number format can reproduce what 'gt' shows. A second entry point does the same for a plain HTML table, so output from other table packages can be written to a worksheet as well; that path needs nothing beyond 'openxlsx2'.

Sizes and reads out A/B/n tests on conversion rates in a few function calls. Supports unequal traffic splits, multiple treatment arms with multiplicity correction, sample ratio mismatch checks, achieved minimum detectable effects, and plain-English summaries.

Sequential, anytime-valid comparison of algorithms on paired losses with a practical-equivalence margin. Confidence sequences for the mean paired difference, superiority/equivalence/continue decisions, cost accounting and auditable trajectories.

Performs Trial Sequential Analysis (TSA) for meta-analyses of time-to-event outcomes reported as hazard ratios. Implements the Schoenfeld required-events sample-size formula generalised for unequal allocation, the Diversity (D-squared) heterogeneity adjustment of Wetterslev et al. (2009), and O'Brien-Fleming-type alpha- and beta-spending trial sequential monitoring boundaries computed at the inverse-variance information based on observed study-level log-HR standard errors (not merely event counts), via a compiled (C++) recursive numerical integration engine ported from the R package 'RTSA' (Soerensen, Olsen, Lange and Gluud; an R implementation of the Trial Sequential Analysis software of the Copenhagen Trial Unit, &lt;<a href="https://ctu.dk/tools" target="_top">https://ctu.dk/tools</a>&gt;), with no fixed software-imposed limit on the number of looks (subject to available computational resources). Produces a cumulative Z-curve plot with efficacy, futility, and conventional significance boundaries. Methodology follows Miladinovic et al. (2013) &lt;<a href="https://doi.org/10.1016%2Fj.jclinepi.2012.11.007" target="_top">doi:10.1016/j.jclinepi.2012.11.007</a>&gt; and Wetterslev et al. (2009) &lt;<a href="https://doi.org/10.1186%2F1471-2288-9-86" target="_top">doi:10.1186/1471-2288-9-86</a>&gt;.