The Darwin Core data standard is widely used to share biodiversity information, most notably by the Global Biodiversity Information Facility and its partner nodes; but converting data to this standard can be tricky. galaxias is functionally similar to devtools', but with a focus on building Darwin Core Archives rather than R packages, enabling data to be shared and re-used with relative ease. For details see Wieczorek and colleagues (2012) <doi:10.1371/journal.pone.0029715>.

Create epicurves, epigantt charts, and diverging bar charts using ggplot2'. Prepare data for visualisation or other reporting for infectious disease surveillance and outbreak investigation (time series data). Includes tidy functions to solve date based transformations for common reporting tasks, like (A) seasonal date alignment for respiratory disease surveillance, (B) date-based case binning based on specified time intervals like isoweek, epiweek, month and more, (C) automated detection and marking of the new year based on the date/datetime axis of the ggplot2', (D) labelling of the last value of a time-series. An introduction on how to use epicurves can be found on the US CDC website (2012, <https://www.cdc.gov/training/quicklearns/epimode/index.html>).

An implementation of maximum simulated likelihood method for the estimation of multinomial logit models with random coefficients as presented by Sarrias and Daziano (2017) <doi:10.18637/jss.v079.i02>. Specifically, it allows estimating models with continuous heterogeneity such as the mixed multinomial logit and the generalized multinomial logit. It also allows estimating models with discrete heterogeneity such as the latent class and the mixed-mixed multinomial logit model.

This package implements the GAMbag, GAMrsm and GAMens ensemble classifiers for binary classification (De Bock et al., 2010) <doi:10.1016/j.csda.2009.12.013>. The ensembles implement Bagging (Breiman, 1996) <doi:10.1023/A:1010933404324>, the Random Subspace Method (Ho, 1998) <doi:10.1109/34.709601> , or both, and use Hastie and Tibshirani's (1990, ISBN:978-0412343902) generalized additive models (GAMs) as base classifiers. Once an ensemble classifier has been trained, it can be used for predictions on new data. A function for cross validation is also included.

Decision curve analysis is a method for evaluating and comparing prediction models that incorporates clinical consequences, requires only the data set on which the models are tested, and can be applied to models that have either continuous or dichotomous results. The ggscidca package adds coloured bars of discriminant relevance to the traditional decision curve. Improved practicality and aesthetics. This method was described by Balachandran VP (2015) <doi:10.1016/S1470-2045(14)71116-7>.

This package provides methods to analyse experimental agriculture data, from data synthesis to model selection and visualisation. The package is named after W.S. Gosset aka â Studentâ , a pioneer of modern statistics in small sample experimental design and analysis.

Flexible and robust estimation and inference of Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models with covariates ('X') based on the results by Francq and Thieu (2019) <doi:10.1017/S0266466617000512>. Coefficients can straightforwardly be set to zero by omission, and quasi maximum likelihood methods ensure estimates are generally consistent and inference valid, even when the standardised innovations are non-normal and/or dependent over time. See Sucarrat (2021) <doi:10.32614/RJ-2021-057> for an overview of the package.

For spatial data analysis; provides exploratory spatial analysis tools, spatial regression, spatial econometric, and disease mapping models, model diagnostics, and special methods for inference with small area survey data (e.g., the America Community Survey (ACS)) and censored population health monitoring data. Models are pre-specified using the Stan programming language, a platform for Bayesian inference using Markov chain Monte Carlo (MCMC). References: Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Donegan (2021) <doi:10.31219/osf.io/3ey65>; Donegan (2022) <doi:10.21105/joss.04716>; Donegan, Chun and Hughes (2020) <doi:10.1016/j.spasta.2020.100450>; Donegan, Chun and Griffith (2021) <doi:10.3390/ijerph18136856>; Morris et al. (2019) <doi:10.1016/j.sste.2019.100301>.

Genomic biology is not limited to the confines of the canonical B-forming DNA duplex, but includes over ten different types of other secondary structures that are collectively termed non-B DNA structures. Of these non-B DNA structures, the G-quadruplexes are highly stable four-stranded structures that are recognized by distinct subsets of nuclear factors. This package provide functions for predicting intramolecular G quadruplexes. In addition, functions for predicting other intramolecular nonB DNA structures are included.

Splits date and time of day components from continuous datetime objects, then plots them using grammar of graphics ('ggplot2'). Plots can also be decorated with solar cycle information (e.g., sunset, sunrise, etc.). This is useful for visualising data that are associated with the solar cycle.

This package provides a nonparametric empirical Bayes method for recovering gradients (or growth velocities) from observations of smooth functions (e.g., growth curves) at isolated time points.

Draw geospatial objects by clicks on the map. This packages can help data analyst who want to check their own geospatial hypothesis but has no ready-made geospatial objects.

Inspects ggplot objects by extracting their full declarative specification - layers, aesthetic mappings, scales, facets, coordinate systems, and labels - as tidy data frames. A second tier of functions enables structural comparison of two ggplot objects, supporting automated plot testing, auditing, and framework-agnostic grading workflows. Unlike ggcheck', which is designed exclusively for learnr'/'gradethis pipelines and returns ad-hoc objects, ggspec returns rectangular, pipeable output and does not require any grading framework as a dependency.

This package provides algorithms for detection of spatial patterns from oceanographic data using image processing methods based on Gradient Recognition.

Finite candidate-set approximate optimal designs for group testing and related experiments, using convex optimization and equivalence checks. Implements the information matrix and cost structure for the prevalence / sensitivity / specificity model used in Huang and colleagues (2020), as in Chi-Kuang Yeh, Weng Kee Wong, and Julie Zhou (<doi:10.48550/arXiv.2508.08445>).

Generalized LassO applied to knot selection in multivariate B-splinE Regression (GLOBER) implements a novel approach for estimating functions in a multivariate nonparametric regression model based on an adaptive knot selection for B-splines using the Generalized Lasso. For further details we refer the reader to the paper Savino, M. E. and Lévy-Leduc, C. (2023), <arXiv:2306.00686>.

This package provides functions for estimating a generalized partial linear model, a semiparametric variant of the generalized linear model (GLM) which replaces the linear predictor by the sum of a linear and a nonparametric function.

An intuitive interface to simulate superimposed (marked) point patterns with vectorized parameterization of random point pattern and distribution of marks. The author has retired from academic research. Accordingly, this package should not be considered a validated tool for use in peer-reviewed publications or as the basis for grant applications.

This package provides a fast, vectorized hashmap that is built on top of C++ std::unordered_map <https://en.cppreference.com/w/cpp/container/unordered_map.html>. The map can hold any R object as key / value as long as it is serializable and supports vectorized insertion, lookup, and deletion.

Facilitates the analysis and evaluation of hydrologic model output and time-series data with functions focused on comparison of modeled (simulated) and observed data, period-of-record statistics, and trends.

Perform Hi-C data differential analysis based on pixel-level differential analysis and a post hoc inference strategy to quantify signal in clusters of pixels. Clusters of pixels are obtained through a connectivity-constrained two-dimensional hierarchical clustering.

This package provides a multi-core R package that allows for the statistical modeling of multi-group multivariate mixed data using Gaussian graphical models. Combining the Gaussian copula framework with the fused graphical lasso penalty, the heteromixgm package can handle a wide variety of datasets found in various sciences. The package also includes an option to perform model selection using the AIC, BIC and EBIC information criteria, a function that plots partial correlation graphs based on the selected precision matrices, as well as simulate mixed heterogeneous data for exploratory or simulation purposes and one multi-group multivariate mixed agricultural dataset pertaining to maize yields. The package implements the methodological developments found in Hermes et al. (2024) <doi:10.1080/10618600.2023.2289545>.

This package implements assessment of benefit-risk balance using Bayesian Discrete Choice Experiment. For more details see the article by Mukhopadhyay et al. (2019) <DOI:10.1080/19466315.2018.1527248>.

This package creates self-contained widgets for interactive phylogenetic tree visualization. This package wraps the JavaScript heat-tree package using the htmlwidgets R package.