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 Global Biodiversity Information Facility ('GBIF', <https://www.gbif.org>) sources data from an international network of data providers, known as nodes'. Several of these nodes - the "living atlases" (<https://living-atlases.gbif.org>) - maintain their own web services using software originally developed by the Atlas of Living Australia ('ALA', <https://www.ala.org.au>). galah enables the R community to directly access data and resources hosted by GBIF and its partner nodes.

Generating, evaluating, and selecting initialization strategies for Gaussian Mixture Models (GMMs), along with functions to run the Expectation-Maximization (EM) algorithm. Initialization methods are compared using log-likelihood, and the best-fitting model can be selected using BIC. Methods build on initialization strategies for finite mixture models described in Michael and Melnykov (2016) <doi:10.1007/s11634-016-0264-8> and Biernacki et al. (2003) <doi:10.1016/S0167-9473(02)00163-9>, and on the EM algorithm of Dempster et al. (1977) <doi:10.1111/j.2517-6161.1977.tb01600.x>. Background on model-based clustering includes Fraley and Raftery (2002) <doi:10.1198/016214502760047131> and McLachlan and Peel (2000, ISBN:9780471006268).

Density, distribution function, quantile function and random generation for the Generalized Binomial Distribution. Functions to compute the Clopper-Pearson Confidence Interval and the required sample size. Enhanced model for burn-in studies, where failures are tackled by countermeasures.

This package provides a collection of tools to create, use and maintain modularized model code written in the modeling language GAMS (<https://www.gams.com/>). Out-of-the-box GAMS does not come with support for modularized model code. This package provides the tools necessary to convert a standard GAMS model to a modularized one by introducing a modularized code structure together with a naming convention which emulates local environments. In addition, this package provides tools to monitor the compliance of the model code with modular coding guidelines.

This package provides a collection of datasets and simplified functions for an introductory (geo)statistics module at University College London. Provides functionality for compositional, directional and spatial data, including ternary diagrams, Wulff and Schmidt stereonets, and ordinary kriging interpolation. Implements logistic and (additive and centred) logratio transformations. Computes vector averages and concentration parameters for the von-Mises distribution. Includes a collection of natural and synthetic fractals, and a simulator for deterministic chaos using a magnetic pendulum example. The main purpose of these functions is pedagogical. Researchers can find more complete alternatives for these tools in other packages such as compositions', robCompositions', sp', gstat and RFOC'. All the functions are written in plain R, with no compiled code and a minimal number of dependencies. Theoretical background and worked examples are available at <https://tinyurl.com/UCLgeostats/>.

Gaussian process regression models, a.k.a. Kriging models, are applied to global multi-objective optimization of black-box functions. Multi-objective Expected Improvement and Step-wise Uncertainty Reduction sequential infill criteria are available. A quantification of uncertainty on Pareto fronts is provided using conditional simulations.

Basic functions for plotting 2D and 3D views of a sphere, by default the Earth with its major coastline, and additional lines and points.

This package provides functions for obtaining the probability of detection, for grab samples selection by using two different methods such as systematic or random based on two-state Markov chain model. For detection probability calculation, we used results from Bhat, U. and Lal, R. (1988) <doi:10.2307/1427041>.

Standardise the width in ggplot2 geoms to appear visually consistent across plots with different numbers of categories, panel dimensions, and orientations.

Simulation, estimation and testing for geopolitical volatility (GEOVOL) based on the global common volatility model of Engle and Campos-Martins (2023) <doi:10.1016/j.jfineco.2022.09.009>. GEOVOL is modelled as a latent multiplicative volatility factor with heterogeneous factor loadings. Estimation is carried out as a maximization-maximization procedure, where GEOVOL and the GEOVOL loadings are estimated iteratively until convergence.

This package provides a ggplot2 extension that enables visualization of IP (Internet Protocol) addresses and networks. The address space is mapped onto the Cartesian coordinate system using a space-filling curve. Offers full support for both IPv4 and IPv6 (Internet Protocol versions 4 and 6) address spaces.

This package provides a ggplot2 extension providing an integrative framework for composable visualization, enabling the creation of complex multi-plot layouts such as insets, circular arrangements, and multi-panel compositions. Built on the grammar of graphics, it offers tools to align, stack, and nest plots, simplifying the construction of richly annotated figures for high-dimensional data contextsâ such as genomics, transcriptomics, and microbiome studiesâ by making it easy to link related plots, overlay clustering results, or highlight shared patterns.

Robust multiple or multivariate linear regression, nonparametric regression on orthogonal components, classical or robust partial least squares models as described in Bilodeau, Lafaye De Micheaux and Mahdi (2015) <doi:10.18637/jss.v065.i01>.

Techniques from a particular branch of spatial statistics,termed geographically-weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. GWmodel includes functions to calibrate: GW summary statistics (Brunsdon et al., 2002)<doi: 10.1016/s0198-9715(01)00009-6>, GW principal components analysis (Harris et al., 2011)<doi: 10.1080/13658816.2011.554838>, GW discriminant analysis (Brunsdon et al., 2007)<doi: 10.1111/j.1538-4632.2007.00709.x> and various forms of GW regression (Brunsdon et al., 1996)<doi: 10.1111/j.1538-4632.1996.tb00936.x>; some of which are provided in basic and robust (outlier resistant) forms.

The correlations and linkage disequilibrium between tests can vary as a function of minor allele frequency thresholds used to filter variants, and also varies with different choices of test statistic for region-based tests. Appropriate genome-wide significance thresholds can be estimated empirically through permutation on only a small proportion of the whole genome.

This package provides a ggplot2 extension that provides tools for automatically creating scales to focus on subgroups of the data plotted without losing other information.

Moon charts are like pie charts except that the proportions are shown as crescent or gibbous portions of a circle, like the lit and unlit portions of the moon. As such, they work best with only one or two groups. gggibbous extends ggplot2 to allow for plotting multiple moon charts in a single panel and does not require a square coordinate system.

Utilities to cost and evaluate Australian tax policy, including fast projections of personal income tax collections, high-performance tax and transfer calculators, and an interface to common indices from the Australian Bureau of Statistics. Written to support Grattan Institute's Australian Perspectives program, and related projects. Access to the Australian Taxation Office's sample files of personal income tax returns is assumed.

It provides a custom ggplot2 geom to add day/night patterns to plots. It visually distinguishes daytime and nighttime periods. It is useful for visualizing data that spans multiple days and for highlighting diurnal patterns.

The purpose is to account for the random displacements (jittering) of true survey household cluster center coordinates in geostatistical analyses of Demographic and Health Surveys program (DHS) data. Adjustment for jittering can be implemented either in the spatial random effect, or in the raster/distance based covariates, or in both. Detailed information about the methods behind the package functionality can be found in our two papers. Umut Altay, John Paige, Andrea Riebler, Geir-Arne Fuglstad (2024) <doi:10.32614/RJ-2024-027>. Umut Altay, John Paige, Andrea Riebler, Geir-Arne Fuglstad (2023) <doi:10.1177/1471082X231219847>.

This package provides the standard operations for signal processing on graphs: graph Fourier transform, spectral graph wavelet transform, visualization tools. It also implements a data driven method for graph signal denoising/regression, for details see De Loynes, Navarro, Olivier (2019) <arxiv:1906.01882>. The package also provides an interface to the SuiteSparse Matrix Collection, <https://sparse.tamu.edu/>, a large and widely used set of sparse matrix benchmarks collected from a wide range of applications.

Estimation and display of various types of population attributable fraction and impact fractions. As well as the usual calculations of attributable fractions and impact fractions, functions are provided for attributable fraction nomograms and fan plots, continuous exposures, for pathway specific population attributable fractions, and for joint, average and sequential population attributable fractions.

The GenSVM classifier is a generalized multiclass support vector machine (SVM). This classifier aims to find decision boundaries that separate the classes with as wide a margin as possible. In GenSVM, the loss function is very flexible in the way that misclassifications are penalized. This allows the user to tune the classifier to the dataset at hand and potentially obtain higher classification accuracy than alternative multiclass SVMs. Moreover, this flexibility means that GenSVM has a number of other multiclass SVMs as special cases. One of the other advantages of GenSVM is that it is trained in the primal space, allowing the use of warm starts during optimization. This means that for common tasks such as cross validation or repeated model fitting, GenSVM can be trained very quickly. Based on: G.J.J. van den Burg and P.J.F. Groenen (2018) <https://www.jmlr.org/papers/v17/14-526.html>.