Routines to write, simulate, and validate stock-flow consistent (SFC) models. The accounting structure of SFC models are described in Godley and Lavoie (2007, ISBN:978-1-137-08599-3). The algorithms implemented to solve the models (Gauss-Seidel and Broyden) are described in Kinsella and O'Shea (2010) <doi:10.2139/ssrn.1729205> and Peressini and Sullivan (1988, ISBN:0-387-96614-5).

This package provides a compact schema domain-specific language for inferring, editing, and validating R data structures with checkmate checks. Schemas can be serialized to and restored from JSON for storage and review. A generated standalone bundle supports vendoring the schema tools into other R packages.

Interface to the sTiles framework for tile-based sparse Cholesky factorization: log-determinants, selected inverse (marginal variances) and triangular solves, with symbolic reuse so that repeated factorization of matrices sharing one sparsity pattern pays the ordering cost only once, as in a hyperparameter sweep. The compiled glue in this package resolves its symbols at run time against the sTiles solver library ('libstiles'), which is a separate component distributed under its own terms and is not part of this package. Install it once with sTiles_install_library()', or point the package at a copy you already have with the STILES_LIB environment variable.

This package contains a suite of functions for survival analysis in health economics. These can be used to run survival models under a frequentist (based on maximum likelihood) or a Bayesian approach (both based on Integrated Nested Laplace Approximation or Hamiltonian Monte Carlo). To run the Bayesian models, the user needs to install additional modules (packages), i.e. survHEinla and survHEhmc'. These can be installed from <https://giabaio.r-universe.dev/> using install.packages("survHEhmc", repos = c("https://giabaio.r-universe.dev", "https://cloud.r-project.org")) and install.packages("survHEinla", repos = c("https://giabaio.r-universe.dev", "https://cloud.r-project.org")) respectively. survHEinla is based on the package INLA, which is available for download at <https://inla.r-inla-download.org/R/stable/>. The user can specify a set of parametric models using a common notation and select the preferred mode of inference. The results can also be post-processed to produce probabilistic sensitivity analysis and can be used to export the output to an Excel file (e.g. for a Markov model, as often done by modellers and practitioners). <doi:10.18637/jss.v095.i14>.

Runs the applicable tests for a comparison in one call and returns standardised result tables. One-sample, two-group, multi-group, factorial and categorical workflows report parametric, rank-based and robust results side by side with effect sizes, confidence intervals and multiplicity-adjusted p-values. Supervised fits, embeddings, clustering and simulators follow the same result contracts. Methods include those of Welch (1947) <doi:10.1093/biomet/34.1-2.28>, Wilcoxon (1945) <doi:10.2307/3001968>, Mann and Whitney (1947) <doi:10.1214/aoms/1177730491>, Kruskal and Wallis (1952) <doi:10.1080/01621459.1952.10483441>, Friedman (1937) <doi:10.1080/01621459.1937.10503522>, Tukey (1949) <doi:10.2307/3001913>, Dunn (1964) <doi:10.1080/00401706.1964.10490181>, Yuen (1974) <doi:10.1093/biomet/61.1.165>, Brunner and Munzel (2000) <doi:10.1002/(SICI)1521-4036(200001)42:1%3C17::AID-BIMJ17%3E3.0.CO;2-U>, Algina, Keselman and Penfield (2005) <doi:10.1037/1082-989X.10.3.317>, DeLong, DeLong and Clarke-Pearson (1988) <doi:10.2307/2531595>, Sun and Xu (2014) <doi:10.1109/LSP.2014.2337313>, Pencina, D'Agostino, D'Agostino and Vasan (2008) <doi:10.1002/sim.2929>, and Pencina, D'Agostino and Steyerberg (2011) <doi:10.1002/sim.4085>.

Density, distribution function, quantile function and random generation for the skewed generalized t distribution. This package also provides a function that can fit data to the skewed generalized t distribution using maximum likelihood estimation.

Get started with new projects by dropping a skeleton of a new project into a new or existing directory, initialise git repositories, and create reproducible environments with the renv package. The package allows for dynamically named files, folders, file content, as well as the functionality to drop individual template files into existing projects.

This package provides a rendering tool for parameterized SQL that also translates into different SQL dialects. These dialects include Microsoft SQL Server', Oracle', PostgreSql', Amazon RedShift', Apache Impala', IBM Netezza', Google BigQuery', Microsoft PDW', Snowflake', Azure Synapse Analytics Dedicated', Apache Spark', SQLite', and InterSystems IRIS'.

Wrapping and supplementing commonly used functions in the R ecosystem related to spatial data science, while serving as a basis for other packages maintained by Wenbo Lv.

Perform spatial temporal analysis of moving polygons; a longstanding analysis problem in Geographic Information Systems. Facilitates directional analysis, distance analysis, and some other simple functionality for examining spatial-temporal patterns of moving polygons.

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>.

It implements parametric formulas of soil water retention or conductivity curve. At the moment, only Van Genuchten (for soil water retention curve) and Mualem (for hydraulic conductivity) were implemented. See reference (<http://en.wikipedia.org/wiki/Water_retention_curve>).

User tools for working with The STOICH (Stoichiometric Traits of Organisms in their Chemical Habitats) Project database <https://snr-stoich.unl.edu/>. This package is designed to aid in data discovery, filtering, pairing water samples with organism samples, and merging data tables to assist users in preparing data for analyses. For additional examples see "Additional Examples" and the readme file at <https://github.com/STOICH-project/STOICH-utilities>.

This package provides functions that calculate appropriate sample sizes for one-sample t-tests, two-sample t-tests, and F-tests for microarray experiments based on desired power while controlling for false discovery rates. For all tests, the standard deviations (variances) among genes can be assumed fixed or random. This is also true for effect sizes among genes in one-sample and two sample experiments. Functions also output a chart of power versus sample size, a table of power at different sample sizes, and a table of critical test values at different sample sizes.

This package provides a fast, GPU-free 3D software renderer written in modern C++17 with native R bindings. Renders triangle meshes to publication-quality images entirely on the CPU, requiring no display server or graphics hardware. Features multi-light Blinn-Phong shading, screen-space ambient occlusion, anti-aliasing, depth fog, transparency, wireframe rendering, texture mapping, and procedural geometry generation. Supports standard mesh file formats with PNG and PPM output. Works on high-performance computing clusters, headless servers, containers, and continuous integration pipelines, making it suitable for scientific visualization across neuro-imaging, molecular structures, and general 3D graphics.

Interface to the Sensor Tower API <https://app.sensortower.com/api/docs/app_analysis> for mobile app analytics and market intelligence. Provides a small, consistent set of functions to retrieve app metadata, publisher information, download and revenue estimates, active user metrics, category rankings, aggregate game market denominators, and market trends. Four core verbs ('st_metrics', st_rankings', st_app'/'st_apps', st_filter') cover the common workflows with standardized parameters and tidyverse-friendly output. Supports both iOS and Android app ecosystems with unified data structures for cross-platform analysis.

Fast versions of seismic analysis functions that roll over a vector of values. See the RcppRoll package for alternative versions of basic statistical functions such as rolling mean, median, etc.

Evaluating probabilistic forecasts via proper scoring rules. scoring implements the beta, power, and pseudospherical families of proper scoring rules, along with ordered versions of the latter two families. Included among these families are popular rules like the Brier (quadratic) score, logarithmic score, and spherical score. For two-alternative forecasts, also includes functionality for plotting scores that one would obtain under specific scoring rules.

This package provides a pipeline that can process single or multiple Single Cell RNAseq samples primarily specializes in Clustering and Dimensionality Reduction. Meanwhile we use common cell type marker genes for T cells, B cells, Myeloid cells, Epithelial cells, and stromal cells (Fiboblast, Endothelial cells, Pericyte, Smooth muscle cells) to visualize the Seurat clusters, to facilitate labeling them by biological names. Once users named each cluster, they can evaluate the quality of them again and find the de novo marker genes also.

This package implements multi-study learning algorithms such as merging, the study-specific ensemble (trained-on-observed-studies ensemble) the study strap, the covariate-matched study strap, covariate-profile similarity weighting, and stacking weights. Embedded within the caret framework, this package allows for a wide range of single-study learners (e.g., neural networks, lasso, random forests). The package offers over 20 default similarity measures and allows for specification of custom similarity measures for covariate-profile similarity weighting and an accept/reject step. This implements methods described in Loewinger, Kishida, Patil, and Parmigiani. (2019) <doi:10.1101/856385>.

Allow sharing sensitive information, for example passwords, API keys, etc., in R packages, using public key cryptography.

Routines for a collection of screen-and-clean type variable selection procedures, including UPS and GS.

This package provides a robust solution employing the SRS (Simple Random Sampling), systematic and PPS (Probability Proportional to Size) sampling methods, ensuring a methodical and representative selection of data. Seamlessly allocate predetermined allocations to smaller levels.

Mappings for estimated one rep max from commonly used formulas. Convenience functions for turning mass/rep/set data into useful derived quantities.