This package provides an easy to use interface to the Google Pub/Sub REST API <https://cloud.google.com/pubsub/docs/reference/rest>.

This package provides a tool to sensitivity analysis using SOBOL (Sobol, 1993) and AMA (Dell'Oca et al. 2017 <doi:10.5194/hess-21-6219-2017>) indices. It allows to identify the most sensitive parameter or parameters of a model.

We implement two main functions. The first function uses a given grouped and/or right-censored grouping scheme and empirical data to infer parameters, and implements chi-square goodness-of-fit tests. The second function searches for the global optimal grouping scheme of grouped and/or right-censored count responses in surveys.

This package contains the Gene ontology terms and skeleton for the reduced GO directed acyclic graph (DAG) for the organisms Rat and Mouse. The methods are explicitly discussed in the following article : Manjang et al (2020) <doi:10.1038/s41598-020-73326-3>.

Represents generalized geometric ellipsoids with the "(U,D)" representation. It allows degenerate and/or unbounded ellipsoids, together with methods for linear and duality transformations, and for plotting. Thus ellipsoids are naturally extended to include lines, hyperplanes, points, cylinders, etc. This permits exploration of a variety to statistical issues that can be visualized using ellipsoids as discussed by Friendly, Fox & Monette (2013), Elliptical Insights: Understanding Statistical Methods Through Elliptical Geometry <doi:10.1214/12-STS402>.

Data sets used in the book Marra and Radice (2025, ISBN:9781032973111) "Copula Additive Distributional Regression Using R", for illustrating the fitting of various joint (and univariate) regression models, with several types of covariate effects, in the presence of equations errors association.

This package provides functions to compute various germination indices such as germinability, median germination time, mean germination time, mean germination rate, speed of germination, Timson's index, germination value, coefficient of uniformity of germination, uncertainty of germination process, synchrony of germination etc. from germination count data. Includes functions for fitting cumulative seed germination curves using four-parameter hill function and computation of associated parameters. See the vignette for more, including full list of citations for the methods implemented.

This package provides a collection of functions to set up Google Public Data Explorer <https://www.google.com/publicdata/> data visualization tool with your own data, building automatically the corresponding DataSet Publishing Language file, or DSPL (XML), metadata file jointly with the CSV files. All zip-up and ready to be published in Public Data Explorer'.

This package provides functions to explore datasets from the Global Biodiversity Information Facility (GBIF - <https://www.gbif.org/>) using a Shiny interface.

The standard linear regression theory whether frequentist or Bayesian is based on an assumed (revealed?) truth (John Tukey) attitude to models. This is reflected in the language of statistical inference which involves a concept of truth, for example confidence intervals, hypothesis testing and consistency. The motivation behind this package was to remove the word true from the theory and practice of linear regression and to replace it by approximation. The approximations considered are the least squares approximations. An approximation is called valid if it contains no irrelevant covariates. This is operationalized using the concept of a Gaussian P-value which is the probability that pure Gaussian noise is better in term of least squares than the covariate. The precise definition given in the paper "An Approximation Based Theory of Linear Regression". Only four simple equations are required. Moreover the Gaussian P-values can be simply derived from standard F P-values. Furthermore they are exact and valid whatever the data in contrast F P-values are only valid for specially designed simulations. A valid approximation is one where all the Gaussian P-values are less than a threshold p0 specified by the statistician, in this package with the default value 0.01. This approximations approach is not only much simpler it is overwhelmingly better than the standard model based approach. The will be demonstrated using high dimensional regression and vector autoregression real data sets. The goal is to find valid approximations. The search function is f1st which is a greedy forward selection procedure which results in either just one or no approximations which may however not be valid. If the size is less than than a threshold with default value 21 then an all subset procedure is called which returns the best valid subset. A good default start is f1st(y,x,kmn=15) The best function for returning multiple approximations is f3st which repeatedly calls f1st. For more information see the papers: L. Davies and L. Duembgen, "Covariate Selection Based on a Model-free Approach to Linear Regression with Exact Probabilities", <doi:10.48550/arXiv.2202.01553>, L. Davies, "An Approximation Based Theory of Linear Regression", 2024, <doi:10.48550/arXiv.2402.09858>.

Spatio-temporal causal inference based on point process data. You provide the raw data of locations and timings of treatment and outcome events, specify counterfactual scenarios, and the package estimates causal effects over specified spatial and temporal windows. See Papadogeorgou, et al. (2022) <doi:10.1111/rssb.12548> and Mukaigawara, et al. (2024) <doi:10.31219/osf.io/5kc6f>.

The genridge package introduces generalizations of the standard univariate ridge trace plot used in ridge regression and related methods. These graphical methods show both bias (actually, shrinkage) and precision, by plotting the covariance ellipsoids of the estimated coefficients, rather than just the estimates themselves. 2D and 3D plotting methods are provided, both in the space of the predictor variables and in the transformed space of the PCA/SVD of the predictors.

Data-driven approach for arriving at person-specific time series models. The method first identifies which relations replicate across the majority of individuals to detect signal from noise. These group-level relations are then used as a foundation for starting the search for person-specific (or individual-level) relations. See Gates & Molenaar (2012) <doi:10.1016/j.neuroimage.2012.06.026>.

Fits unimodal and multimodal gambin distributions to species-abundance distributions from ecological data, as in in Matthews et al. (2014) <DOI:10.1111/ecog.00861>. gambin is short for gamma-binomial'. The main function is fit_abundances(), which estimates the alpha parameter(s) of the gambin distribution using maximum likelihood. Functions are also provided to generate the gambin distribution and for calculating likelihood statistics.

This package provides a suite of custom R Markdown formats and templates for authoring web pages styled with the GOV.UK Design System.

This package provides a way to log ggplot component calls, which can be useful for debugging and understanding how ggplot objects are created. The logged calls can be printed, saved, and re-executed to reproduce the original ggplot object.

Computes the solution path for generalized lasso problems. Important use cases are the fused lasso over an arbitrary graph, and trend fitting of any given polynomial order. Specialized implementations for the latter two subproblems are given to improve stability and speed. See Taylor Arnold and Ryan Tibshirani (2016) <doi:10.1080/10618600.2015.1008638>.

Fits generalized linear models using the same model specification as glm in the stats package, but with a modified default fitting method that provides greater stability for models that may fail to converge using glm.

Discretize multivariate continuous data using a grid to capture the joint distribution that preserves clusters in original data. It can handle both labeled or unlabeled data. Both published methods (Wang et al 2020) <doi:10.1145/3388440.3412415> and new methods are included. Joint grid discretization can prepare data for model-free inference of association, function, or causality.

This package provides classes for GeoJSON to make working with GeoJSON easier. Includes S3 classes for GeoJSON classes with brief summary output, and a few methods such as extracting and adding bounding boxes, properties, and coordinate reference systems; working with newline delimited GeoJSON'; and serializing to/from Geobuf binary GeoJSON format.

Calculate, plot and animate the configuration of Jupiter's four largest satellites (known as Galilean satellites) for a given date and time (UTC - Coordinated Universal Time). The galsat() function returns numerical values of the satellitesâ positions. x â the apparent rectangular coordinate of the satellite with respect to the center of Jupiterâ s disk in the equatorial plane in the units of Jupiterâ s equatorial radius; X is positive toward the west, y â the apparent rectangular coordinate of the satellite with respect to the center of Jupiterâ s disk from the equatorial plane in the units of Jupiterâ s equatorial radius; Y is positive toward the north. For more details see Meeus (1988, ISBN 0-943396-22-0) "Astronomical Formulae for Calculators". The galsat_animate() function creates an animation of the Galilean satellites positions. You provide the starting time, duration, the time step between frames, and the pause between frames. The function delta_t() returns the value of delta-T in units of seconds.

Generalized Linear Mixed Model (GLMM) for Binary Randomized Response Data. Includes Cauchit, Compl. Log-Log, Logistic, and Probit link functions for Bernoulli Distributed RR data. RR Designs: Warner, Forced Response, Unrelated Question, Kuk, Crosswise, and Triangular. Reference: Fox, J-P, Veen, D. and Klotzke, K. (2018). Generalized Linear Mixed Models for Randomized Responses. Methodology. <doi:10.1027/1614-2241/a000153>.

Set of functions to create datasets using a correlation matrix.

This package provides tools implementing an automated version of the graphic double integration technique (GDI) for volume implementation, and some other related utilities for paleontological image-analysis. GDI was first employed by Jerison (1973) <ISBN:9780323141086> and Hurlburt (1999) <doi:10.1080/02724634.1999.10011145> and is primarily used for volume or mass estimation of (extinct) animals. The package gdi aims to make this technique as convenient and versatile as possible. The core functions of gdi provide utilities for automatically measuring diameters from digital silhouettes provided as image files and calculating volume via graphic double integration with simple elliptical, superelliptical (following Motani 2001 <doi:10.1666/0094-8373(2001)027%3C0735:EBMFST%3E2.0.CO;2>) or complex cross-sectional geometries (see also Zhao 2024 <doi:10.7717/peerj.17479>). Additionally, the package provides functions for estimating the center of mass position (COM), the moment of inertia (I) for 3D shapes and the second moment of area (Ix, Iy, Iz) of 2D cross-sections, as well as for the visualization of results.