This package 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) <doi:10.1002/9780470580066>.

Perform spatial analysis on network. Implement several methods for spatial analysis on network: Network Kernel Density estimation, building of spatial matrices based on network distance ('listw objects from spdep package), K functions estimation for point pattern analysis on network, k nearest neighbours on network, reachable area calculation, and graph generation References: Okabe et al (2019) <doi:10.1080/13658810802475491>; Okabe et al (2012, ISBN:978-0470770818);Baddeley et al (2015, ISBN:9781482210200).

This package provides tools for Bayesian learning of spatiotemporal dynamical mechanistic models. Includes methods for parameter estimation, simulation, and inference using hierarchical and state-space modeling approaches, following Banerjee, Chen, Frankenburg and Zhou (2025) <https://jmlr.org/papers/v26/22-0896.html>.

An interactive framework for simulating blockchain protocols using a hybrid R-Shiny and Python architecture. The package provides tools to visualize peer-to-peer network maps, manage supply chain logistics on-chain, and execute cross-border settlements via smart contract logic. It leverages the reticulate package to perform standardized cryptographic operations, including SHA-256 hashing, Merkle Tree construction, and ECDSA (Elliptic Curve Digital Signature Algorithm) key generation. This tool is designed for pedagogical demonstration and rapid prototyping of distributed ledger requirements.

User-friendly functions which parse output of command line programs used to query Slurm. Morris A. Jette and Tim Wickberg (2023) <doi:10.1007/978-3-031-43943-8_1> describe Slurm in detail.

This package contains existing and novel methods for nonparametric analysis and Cox regression analysis with doubly truncated survival data, as described in Vazquez and Xie (2025) <doi:10.1007/s10985-025-09650-5>. Includes survival curves and hazard estimates through nonparametric maximum likelihood estimation, various tests for detecting group differences or non-ignorable sampling bias, and inverse probability weighted Cox regression with several options of nonparametric weights. Also implements diagnostics for key modeling assumptions such as quasi-independent truncation and the positivity assumption. Closed-form standard errors are available for all estimates, i.e. bootstrapping is not required.

Facilitates secret management by storing credentials in a dedicated file, keeping them out of your code base. The secrets are stored without encryption. This package is compatible with secrets stored by the SecretsProvider Python package <https://pypi.org/project/SecretsProvider/>.

This package provides a simple, light, and robust interface between R and the Scryfall card data API <https://scryfall.com/docs/api>.

Artificial selection through selective breeding is an efficient way to induce changes in traits of interest in experimental populations. This package (sra) provides a set of tools to analyse artificial-selection response datasets. The data typically feature for several generations the average value of a trait in a population, the variance of the trait, the population size and the average value of the parents that were chosen to breed. Sra implements two families of models aiming at describing the dynamics of the genetic architecture of the trait during the selection response. The first family relies on purely descriptive (phenomenological) models, based on an autoregressive framework. The second family provides different mechanistic models, accounting e.g. for inbreeding, mutations, genetic and environmental canalization, or epistasis. The parameters underlying the dynamics of the time series are estimated by maximum likelihood. The sra package thus provides (i) a wrapper for the R functions mle() and optim() aiming at fitting in a convenient way a predetermined set of models, and (ii) some functions to plot and analyze the output of the models.

This package provides a simple tool for numerical optimization on the unit sphere. This is achieved by combining the spherical coordinating system with L-BFGS-B optimization. This algorithm is implemented in Kolkiewicz, A., Rice, G., & Xie, Y. (2020) <doi:10.1016/j.jspi.2020.07.001>.

This package provides a set of functions to quantify and visualise social autocorrelation.

Simulation tools for planning Vitamin D studies. Individual vitamin D status profiles are simulated, modelling population heterogeneity in trial arms. Exposures to infectious agents are generated, with infection depending on vitamin D status.

Tests for equality of two survival functions based on integrated weighted differences of two Kaplan-Meier curves.

Reveals how data quality silently degrades during geographic transformations while variable labels remain unchanged. Demonstrates that transformation error is agnostic to both the variable (population, income, etc.) and the tool ('R', Python', etc.). Provides a reproducible audit framework for quantifying the shift from observed to imputed data at each transformation hop.

This package provides functions for computing geographically weighted regressions are provided, based on work by Chris Brunsdon, Martin Charlton and Stewart Fotheringham.

Generate synthetic time series from commonly used statistical models, including linear, nonlinear and chaotic systems. Applications to testing methods can be found in Jiang, Z., Sharma, A., & Johnson, F. (2019) <doi:10.1016/j.advwatres.2019.103430> and Jiang, Z., Sharma, A., & Johnson, F. (2020) <doi:10.1029/2019WR026962> associated with an open-source tool by Jiang, Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020) <doi:10.1016/j.envsoft.2020.104907>.

An user-friendly framework to preprocess raw item scores of questionnaires into factors or scores and standardize them. Standardization can be made either by their normalization in representative sample, or by import of premade scoring table.

Conduct various tests for evaluating implicit biases in word embeddings: Word Embedding Association Test (Caliskan et al., 2017), <doi:10.1126/science.aal4230>, Relative Norm Distance (Garg et al., 2018), <doi:10.1073/pnas.1720347115>, Mean Average Cosine Similarity (Mazini et al., 2019) <arXiv:1904.04047>, SemAxis (An et al., 2018) <arXiv:1806.05521>, Relative Negative Sentiment Bias (Sweeney & Najafian, 2019) <doi:10.18653/v1/P19-1162>, and Embedding Coherence Test (Dev & Phillips, 2019) <arXiv:1901.07656>.

Allows users to list data structures using path-based navigation. Provides intuitive methods for storing, accessing, and manipulating nested data through simple path strings. Key features include strict mode validation, path existence checking, recursive operations, and automatic parent-level creation. Designed for use cases requiring organized storage of complex nested data while maintaining simple access patterns. Particularly useful for configuration management, nested settings, and any application where data naturally forms a tree-like structure.

This package provides a modern, S7'-based foundation for survey analysis spanning both probability and non-probability samples. Probability sample designs include Taylor series linearization, replicate weights (BRR, Fay, jackknife, bootstrap), and two-phase estimation, following Lumley (2004) <doi:10.18637/jss.v009.i08>. Non-probability sample designs support bootstrap and jackknife variance estimation for opt-in panels and convenience samples. Provides a unified estimator interface for means, frequencies, totals, quantiles, ratios, correlations, regression, and t-tests, with weighted polychoric and polyserial correlation following Mannan (2025) <doi:10.2139/ssrn.6580480>. A metadata system preserves haven'-style variable labels, value labels, and question-preface attributes through all operations. Uses a tidyselect interface throughout.

Decision support tool for prioritizing sites for ecological surveys based on their potential to improve plans for conserving biodiversity (e.g. plans for establishing protected areas). Given a set of sites that could potentially be acquired for conservation management, it can be used to generate and evaluate plans for surveying additional sites. Specifically, plans for ecological surveys can be generated using various conventional approaches (e.g. maximizing expected species richness, geographic coverage, diversity of sampled environmental algorithms. After generating such survey plans, they can be evaluated using conditions) and maximizing value of information. Please note that several functions depend on the Gurobi optimization software (available from <https://www.gurobi.com>). Additionally, the JAGS software (available from <https://mcmc-jags.sourceforge.io/>) is required to fit hierarchical generalized linear models. For further details, see Hanson et al. (2023) <doi:10.1111/1365-2664.14309>.

This package provides a set of functions to build a scoring model from beginning to end, leading the user to follow an efficient and organized development process, reducing significantly the time spent on data exploration, variable selection, feature engineering, binning and model selection among other recurrent tasks. The package also incorporates monotonic and customized binning, scaling capabilities that transforms logistic coefficients into points for a better business understanding and calculates and visualizes classic performance metrics of a classification model.

This package provides a coordinate descent algorithm for computing the solution paths of the sparse and coupled sparse asymmetric least squares, including the (adaptive) elastic net and Lasso penalized SALES and COSALES regressions.

An implementation of semi-supervised regression methods including self-learning and co-training by committee based on Hady, M. F. A., Schwenker, F., & Palm, G. (2009) <doi:10.1007/978-3-642-04274-4_13>. Users can define which set of regressors to use as base models from the caret package, other packages, or custom functions.