This package provides access to granular sub-national income data from the MCC-PIK Database Of Sub-national Economic Output (DOSE). The package downloads and processes the data from its open repository on Zenodo (<https://zenodo.org/records/20035157>). Functions are provided to fetch data at multiple geographic levels, match coordinates to administrative regions, and access associated geometries.

The sasctl (sas control) package enables easy communication between the "SAS Viya" platform APIs <https://developer.sas.com> and the R runtime. It offers convenient wrappers to some most used endpoints.

Data visualization tours animates linear projection of multivariate data as its basis (ie. orientation) changes. The spinifex packages generates paths for manual tours by manipulating the contribution of a single variable at a time Cook & Buja (1997) <doi:10.1080/10618600.1997.10474754>. Other types of tours, such as grand (random walk) and guided (optimizing some objective function) are available in the tourr package Wickham et al. <doi:10.18637/jss.v040.i02>. spinifex builds on tourr and can render tours with gganimate and plotly graphics, and allows for exporting as an .html widget and as an .gif, respectively. This work is fully discussed in Spyrison & Cook (2020) <doi:10.32614/RJ-2020-027>.

Fitting dimension reduction methods to data lying on two-dimensional sphere. This package provides principal geodesic analysis, principal circle, principal curves proposed by Hauberg, and spherical principal curves. Moreover, it offers the method of locally defined principal geodesics which is underway. The detailed procedures are described in Lee, J., Kim, J.-H. and Oh, H.-S. (2021) <doi:10.1109/TPAMI.2020.3025327>. Also see Kim, J.-H., Lee, J. and Oh, H.-S. (2020) <arXiv:2003.02578>.

Set of tools to import, summarize, wrangle, and visualize data. These functions were originally written based on the needs of the various synthesis working groups that were supported by the National Center for Ecological Analysis and Synthesis (NCEAS). These tools are meant to be useful inside and outside of the context for which they were designed.

This is a compendium of C++ routines useful for Bayesian statistics. We steal other people's C++ code, repurpose it, and export it so developers of R packages can use it in their C++ code. We actually don't steal anything, or claim that Thomas Bayes did, but copy code that is compatible with our GPL 3 licence, fully acknowledging the authorship of the original code.

Mosaic diagram, scatterplot matrix, Andrews curves, parallel coordinate diagram, radar diagram, and Chernoff plots as a Shiny app, which allow the order of variables to be changed interactively. The apps are intended as teaching examples.

This package provides functions are provided for internal use by the spatial capture-recapture package secr (from version 5.4.0). The idea is to speed up the installation of secr', and possibly reduce its size. Initially the functions are those for area and transect search that use numerical integration code from RcppNumerical and RcppEigen'. The functions are not intended to be user-friendly and require considerable preprocessing of data.

This package provides a S3 resource is provided by Amazon Web Services S3 or a S3-compatible object store (such as Minio). The resource can be a tidy file to be downloaded from the object store, or a data lake (such as Delta Lake) Parquet file to be read by Apache Spark.

This package provides a non convex optimization package that optimizes any function under the criterion, combination of variables are on the surface of a unit sphere, as described in the paper : Das et al. (2019) <arXiv:1909.04024> .

Soil health assessment builds information to improve decision in soil management. It facilitates assessment of soil conditions for crop suitability [such as those given by FAO <https://www.fao.org/land-water/databases-and-software/crop-information/en/>], groundwater recharge, fertility, erosion, salinization [<doi:10.1002/ldr.4211>], carbon sequestration, irrigation potential, and status of soil resources.

Download and read datasets from the Swiss National Science Foundation (SNF, FNS, SNSF; <https://snf.ch>). The package is lightweight and without dependencies. Downloaded data can optionally be cached, to avoid repeated downloads of the same files. There are also utilities for comparing different versions of datasets, i.e. to report added, removed and changed entries.

This package provides tools to decompose (transformed) spatial connectivity matrices and perform supervised or unsupervised semiparametric spatial filtering in a regression framework. The package supports unsupervised spatial filtering in standard linear as well as some generalized linear regression models.

This package implements Bayesian inference in accelerated failure time (AFT) models for right-censored survival times assuming a log-logistic distribution. Details of the variational Bayes algorithms, with and without shared frailty, are described in Xian et al. (2024) <doi:10.1007/s11222-023-10365-6> and Xian et al. (2024) <doi:10.48550/arXiv.2408.00177>, respectively.

This package creates a wrapper for the SuiteSparse routines that execute the Takahashi equations. These equations compute the elements of the inverse of a sparse matrix at locations where the its Cholesky factor is structurally non-zero. The resulting matrix is known as a sparse inverse subset. Some helper functions are also implemented. Support for spam matrices is currently limited and will be implemented in the future. See Rue and Martino (2007) <doi:10.1016/j.jspi.2006.07.016> and Zammit-Mangion and Rougier (2018) <doi:10.1016/j.csda.2018.02.001> for the application of these equations to statistics.

This package provides Sensory and Consumer Data mapping and analysis <doi:10.14569/IJACSA.2017.081266>. The mapping visualization is made available from several features : options in dimension reduction methods and prediction models ranging from linear to non linear regressions. A smoothed version of the map performed using locally weighted regression algorithm is available. A selection process of map stability is provided. A shiny application is included. It presents an easy GUI for the implemented functions as well as a comparative tool of fit models using several criteria. Basic analysis such as characterization of products, panelists and sessions likewise consumer segmentation are also made available.

Transforms or simulates data with a target empirical covariance matrix supplied by the user. The method to obtain the data with the target empirical covariance matrix is described in Section 5.1 of Christidis, Van Aelst and Zamar (2019) <arXiv:1812.05678>.

This package performs a dual-parameter sensitivity analysis of treatment effect to unmeasured confounding in observational studies with either survival or competing risks outcomes. Huang, R., Xu, R. and Dulai, P.S.(2020) <doi:10.1002/sim.8672>.

Perform two-dimensional smoothing for spatial fields using FFT and the convolution theorem (see Gilleland 2013, <doi:10.5065/D61834G2>).

This package provides estimation of Bayesian vector autoregression (BVAR) models with steady-state priors via Stan', along with functions for unconditional and conditional forecasting, as well as impulse response analysis. For details on the steady-state BVAR model see Villani (2009) <doi:10.1002/jae.1065>.

Identify and understand clusters of points (typically representing the locations of places or events) stored in simple-features (SF) objects. This is useful for analysing, for example, hot-spots of crime events. The package emphasises producing results from point SF data in a single step using reasonable default values for all other arguments, to aid rapid data analysis by users who are starting out. Functions available include kernel density estimation (for details, see Yip (2020) <doi:10.22224/gistbok/2020.1.12>), analysis of spatial association (Getis and Ord (1992) <doi:10.1111/j.1538-4632.1992.tb00261.x>) and hot-spot classification (Chainey (2020) ISBN:158948584X).

Calculate and compare lower confidence bounds for binomial series system reliability. The R shiny application, launched by the function launch_app(), weaves together a workflow of customized simulations and delta coverage calculations to output recommended lower confidence bound methods.

This package provides a simple function that anonymises a list of variables in a consistent way: anonymised factors are not recycled and the same original levels receive the same anonymised factor even if located in different datasets.

Compute relative or absolute population trends across space and time using predictions from models fitted to ecological population abundance data, as described in Knape (2025) <doi:10.1016/j.ecolind.2025.113435>. The package supports models fitted by mgcv or brms', and draws from posterior predictive distributions.