This package provides a new take on the bar chart. Similar to a waffle style chart but instead of squares the layout resembles a brick wall.

This function converts mfpr, numeric, or character strings representing numbers to bigq format without loss of precision.

Utilizes methods of the PyMongo Python library to initialize, insert and query GeoJson data (see <https://github.com/mongodb/mongo-python-driver> for more information on PyMongo'). Furthermore, it allows the user to validate GeoJson objects and to use the console for MongoDB (bulk) commands. The reticulate package provides the R interface to Python modules, classes and functions.

Declares, retrieves, verifies, tracks and actively manages external data dependencies too large or too fast-moving to ship inside a package. Resources are identified by package, name and version, pinned to a Secure Hash Algorithm (SHA-256) checksum, and resolved through an explicit policy so that the same installed package always resolves the same bytes. A registry served from a remote host may be signed with Ed25519 and verified against a key the declaring package ships, so the declaration and the key that vouches for it arrive by different routes. Hashing follows National Institute of Standards and Technology (2015) "Secure Hash Standard" <doi:10.6028/NIST.FIPS.180-4>; signing follows Bernstein, Duif, Lange, Schwabe and Yang (2012) "High-Speed High-Security Signatures" <doi:10.1007/s13389-012-0027-1> and Josefsson and Liusvaara (2017) "Edwards-Curve Digital Signature Algorithm (EdDSA)" <doi:10.17487/RFC8032>. Designed for reproducible offline use and graceful behaviour during package checks.

Using Australian Bureau of Statistics indices, provides functions that convert historical, nominal statistics to real, contemporary values without worrying about date input quality, performance, or the ABS catalogue.

The social network literature features numerous methods for assigning value to paths as a function of their ties. gretel systemizes these approaches, casting them as instances of a generalized path value function indexed by a penalty parameter. The package also calculates probabilistic path value and identifies optimal paths in either value framework. Finally, proximity matrices can be generated in these frameworks that capture high-order connections overlooked in primitive adjacency sociomatrices. Novel methods are described in Buch (2019) <https://davidbuch.github.io/analyzing-networks-with-gretel.html>. More traditional methods are also implemented, as described in Yang, Knoke (2001) <doi:10.1016/S0378-8733(01)00043-0>.

This package implements the generalized integration model, which integrates individual-level data and summary statistics under a generalized linear model framework. It supports continuous and binary outcomes to be modeled by the linear and logistic regression models. For binary outcome, data can be sampled in prospective cohort studies or case-control studies. Described in Zhang et al. (2020)<doi:10.1093/biomet/asaa014>.

This package provides methods for automatic calculation of gene scores from gene count tables, including a Z-score method that requires a table of samples being scored and a count table with control samples; a geometric mean method that does not rely on control samples; and a principal component-based method that summarizes gene expression using user-selected principal components. The Z-score and geometric mean approaches are described in Kim et al. (2018) <doi:10.1089/jir.2017.0127>.

Create geographically referenced traffic data from the Google Maps JavaScript API <https://developers.google.com/maps/documentation/javascript/examples/layer-traffic>.

This package provides a ggplot2 extension that allows text to follow curved paths. Curved text makes it easier to directly label paths or neatly annotate in polar co-ordinates.

Simulation and analysis of graded response data with different types of estimators. Also, an interactive shiny application is provided with graphics for characteristic and information curves. Samejima (2018) <doi:10.1007/978-1-4757-2691-6_5>.

Uses similarity graphs to estimate change-points and their p-values. The default data-level interface constructs a k-MST with k=floor(sqrt(N)) and reports the max-type scan. The generalized, original, and weighted scans remain available explicitly.

This package implements the Expectation-Maximization (EM) algorithm of Dempster, Laird, and Rubin (1977) <doi:10.1111/j.2517-6161.1977.tb01600.x> for parameter estimation under progressive Type-II censored data (Balakrishnan and Aggarwala (2000) <doi:10.1007/978-1-4612-1186-0>) and computes Generalized Process Capability Indices (GPCIs). Uses the UniCensorEM package for EM estimation. Supports classical and generalized capability indices including Cpy (Maiti et al. (2010) <doi:10.1080/16843703.2010.11673233>), Cp, Cpk, Cpu, Cpl, Cpm, Cpmk, Spmk (Dey and Saha (2019) <doi:10.1007/s41872-019-00081-4>), CpTk (Saha et al. (2018) <doi:10.1080/21681015.2018.1437793>), Cpc, CNpmc (Alotaibi et al. (2022) <doi:10.1155/2022/3135264>), CNpmkc (Saha et al. (2024) <doi:10.1142/S021853932450013X>), and CNpk (Saha et al. (2022) <doi:10.1080/02664763.2021.1971632>). Computes point estimates, bias, mean squared error, risk, Heidelberger and Welch convergence diagnostics, convergence probability, and bootstrap confidence intervals at 90 percent, 95 percent, and 99 percent levels. Accommodates user-defined probability density or mass functions, cumulative distribution functions, and survival functions.

Implementation of global envelopes for a set of general d-dimensional vectors T in various applications. A 100(1-alpha)% global envelope is a band bounded by two vectors such that the probability that T falls outside this envelope in any of the d points is equal to alpha. Global means that the probability is controlled simultaneously for all the d elements of the vectors. The global envelopes can be used for graphical Monte Carlo and permutation tests where the test statistic is a multivariate vector or function (e.g. goodness-of-fit testing for point patterns and random sets, functional analysis of variance, functional general linear model, n-sample test of correspondence of distribution functions), for central regions of functional or multivariate data (e.g. outlier detection, functional boxplot) and for global confidence and prediction bands (e.g. confidence band in polynomial regression, Bayesian posterior prediction). See Myllymäki and MrkviÄ ka (2024) <doi:10.18637/jss.v111.i03>, Myllymäki et al. (2017) <doi:10.1111/rssb.12172>, MrkviÄ ka and Myllymäki (2023) <doi:10.1007/s11222-023-10275-7>, MrkviÄ ka et al. (2016) <doi:10.1016/j.spasta.2016.04.005>, MrkviÄ ka et al. (2017) <doi:10.1007/s11222-016-9683-9>, MrkviÄ ka et al. (2020) <doi:10.14736/kyb-2020-3-0432>, MrkviÄ ka et al. (2021) <doi:10.1007/s11009-019-09756-y>, Myllymäki et al. (2021) <doi:10.1016/j.spasta.2020.100436>, MrkviÄ ka et al. (2022) <doi:10.1002/sim.9236>, Dai et al. (2022) <doi:10.5772/intechopen.100124>, DvoŠák and MrkviÄ ka (2022) <doi:10.1007/s00180-021-01134-y>, MrkviÄ ka et al. (2023) <doi:10.48550/arXiv.2309.04746>, and Konstantinou et al. (2024) <doi: 10.1007/s00180-024-01569-z>.

This package provides tools to download global and European spatial data from the Eurostat GISCO (Geographic Information System of the Commission) data distribution <https://ec.europa.eu/eurostat/web/gisco>. The package provides helpers for country boundaries, Nomenclature of Territorial Units for Statistics ('NUTS') regions, administrative units, statistical units, transport networks, basic service locations and other GISCO datasets. This package is neither affiliated with nor endorsed by Eurostat'.

This package provides a ggplot2 extension that creates publication-quality forest and funnel plots from meta package objects or tidy data frames. Provides custom ggproto geometries for study-level confidence intervals with weight-proportional squares, summary effect diamonds, prediction intervals, null-effect reference lines, and funnel-plot contours. Supports subgroup analysis, back-transformation of summary measures, on-the-fly effect pooling, effect and weight table columns, and journal-specific layout presets (JAMA, BMJ, RevMan5).

Learns sparse large Directed Acyclic Graphs with a combination of a convex program and a tailored genetic algorithm.

An interactive document on the topic of goodness of fit analysis using rmarkdown and shiny packages. Runtime examples are provided in the package function as well as at <https://predanalyticssessions1.shinyapps.io/ChiSquareGOF/>.

Estimate natural mortality (M) throughout the life history for organisms, mainly fish and invertebrates, based on gnomonic interval approach proposed by Caddy (1996) <doi:10.1051/alr:1996023> and Martinez-Aguilar et al. (2005) <doi:10.1016/j.fishres.2004.04.008>. It includes estimation of duration of each gnomonic interval (life stage), the constant probability of death (G), and some basic plots.

Turns a single ggplot2'-style formula string into a small shiny application. Placeholder tokens in the formula become input widgets automatically; the package completes the expression with the current input values, renders the plot, shows the generated code, and supports uploaded datasets in different formats.

This package implements regression models for bounded continuous data in the open interval (0,1) using the five-parameter Generalized Kumaraswamy distribution. Supports modeling all distribution parameters (alpha, beta, gamma, delta, lambda) as functions of predictors through various link functions. Provides efficient maximum likelihood estimation via Template Model Builder ('TMB'), offering comprehensive diagnostics, model comparison tools, and simulation methods. Particularly useful for analyzing proportions, rates, indices, and other bounded response data with complex distributional features not adequately captured by simpler models. Methods are described in Lopes and Bonat (2026) <doi:10.21105/joss.08991>.

Aligns peak based on peak retention times and matches homologous peaks across samples. The underlying alignment procedure comprises three sequential steps. (1) Full alignment of samples by linear transformation of retention times to maximise similarity among homologous peaks (2) Partial alignment of peaks within a user-defined retention time window to cluster homologous peaks (3) Merging rows that are likely representing homologous substances (i.e. no sample shows peaks in both rows and the rows have similar retention time means). The algorithm is described in detail in Ottensmann et al., 2018 <doi:10.1371/journal.pone.0198311>.

This package provides a mechanism to plot a Google Map from R and overlay it with shapes and markers. Also provides access to Google Maps APIs, including places, directions, roads, distances, geocoding, elevation and timezone.

Embeds the full GeoLibre geographic information system in R Markdown', Quarto', Shiny', and the RStudio Viewer. Create maps from GeoJSON and sf objects, markers, heatmaps, and tabular coordinates; add Cloud Optimized GeoTIFF', XYZ', WMS', WMTS', WFS', PMTiles', vector tile, 3D Tiles', and video sources; classify choropleths, arrange layers, and add legends, colorbars, and split-map comparisons. Control the camera, export standalone HTML', and read and write .geolibre.json project files. The underlying application is described in Wu (2026) <doi:10.5281/zenodo.20785400>.