This package provides a new diagram for the verification of vector variables (wind, current, etc) generated by multiple models against a set of observations is presented in this package. It has been designed as a generalization of the Taylor diagram to two dimensional quantities. It is based on the analysis of the two-dimensional structure of the mean squared error matrix between model and observations. The matrix is divided into the part corresponding to the relative rotation and the bias of the empirical orthogonal functions of the data. The full set of diagnostics produced by the analysis of the errors between model and observational vector datasets comprises the errors in the means, the analysis of the total variance of both datasets, the rotation matrix corresponding to the principal components in observation and model, the angle of rotation of model-derived empirical orthogonal functions respect to the ones from observations, the standard deviation of model and observations, the root mean squared error between both datasets and the squared two-dimensional correlation coefficient. See the output of function UVError() in this package.

Perform the balanced (Scott and Knott, 1974) and unbalanced <doi:10.1590/1984-70332017v17n1a1> Scott & Knott algorithm.

Spatial transcriptomics iterative hierarchical clustering ('stIHC'), is a method for identifying spatial gene co-expression modules, defined as groups of genes with shared spatial expression patterns. The method is applicable across spatial transcriptomics technologies with differing spatial resolution, and provides a framework for investigating the spatial organisation of gene expression in tissues. For further details, see Higgins C., Li J.J., Carey M. <doi:10.1002/qub2.70011>.

This package provides a unifying framework for managing and deploying shiny applications that consist of modules, where an "app" is a tab-based workflow that guides a user step-by-step through an analysis. The shinymgr app builder "stitches" shiny modules together so that outputs from one module serve as inputs to the next, creating an analysis pipeline that is easy to implement and maintain. Users of shinymgr apps can save analyses as an RDS file that fully reproduces the analytic steps and can be ingested into an R Markdown report for rapid reporting. In short, developers use the shinymgr framework to write modules and seamlessly combine them into shiny apps, and users of these apps can execute reproducible analyses that can be incorporated into reports for rapid dissemination.

Calculating home ranges and movements of animals in complex stream environments is often challenging, and standard home range estimators do not apply. This package provides a series of tools for assessing movements in a stream network, such as calculating the total length of stream used, distances between points, and movement patterns over time. See Vignette for additional details. This package was originally released on GitHub under the name SNM'. SNMA was developed for analyses in McKnight et al. (2025) <doi:10.3354/esr01442> which contains additional examples and information.

This package provides a programmatic interface to many species occurrence data sources, including Global Biodiversity Information Facility ('GBIF'), iNaturalist', eBird', Integrated Digitized Biocollections ('iDigBio'), VertNet', Ocean Biogeographic Information System ('OBIS'), and Atlas of Living Australia ('ALA'). Includes functionality for retrieving species occurrence data, and combining those data.

Acquire hourly meteorological data from stations located all over the world. There is a wealth of data available, with historic weather data accessible from nearly 30,000 stations. The available data is automatically downloaded from a data repository and processed into a tibble for the exact range of years requested. A relative humidity approximation is provided using the August-Roche-Magnus formula, which was adapted from Alduchov and Eskridge (1996) <doi:10.1175%2F1520-0450%281996%29035%3C0601%3AIMFAOS%3E2.0.CO%3B2>.

The developed function is designed for the generation of spatial grids based on user-specified longitude and latitude coordinates. The function first validates the input longitude and latitude values, ensuring they fall within the appropriate geographic ranges. It then creates a polygon from the coordinates and determines the appropriate Universal Transverse Mercator zone based on the provided hemisphere and longitude values. Subsequently, transforming the input Shapefile to the Universal Transverse Mercator projection when necessary. Finally, a spatial grid is generated with the specified interval and saved as a Shapefile. For method details see, Brus,D.J.(2022).<DOI:10.1201/9781003258940>. The function takes into account crucial parameters such as the hemisphere (north or south), desired grid interval, and the output Shapefile path. The developed function is an efficient tool, simplifying the process of empty spatial grid generation for applications such as, geo-statistical analysis, digital soil mapping product generation, etc. Whether for environmental studies, urban planning, or any other geo-spatial analysis, this package caters to the diverse needs of users working with spatial data, enhancing the accessibility and ease of spatial data processing and visualization.

This package implements atom-based regression models (ABRM) for analyzing spatially misaligned data. Provides functions for simulating misaligned spatial data, preparing NIMBLE model inputs, running MCMC diagnostics, and providing results. All main functions return S3 objects with print(), summary(), and plot() methods for intuitive result exploration. Methods originally described in Mugglin et al. (2000) <doi:10.1080/01621459.2000.10474279>, further investigated in Trevisani & Gelfand (2013), and applied in Nethery et al. (2023) <doi:10.1101/2023.01.10.23284410>.

This package provides an interface to shiny inputs used for filtering vectors, data.frames, and other objects. S7'-based implementation allows for seamless extensibility.

Takes as input a stable oxygen isotope (d18O) profile measured in growth direction (D) through a shell + uncertainties in both variables (d18O_err & D_err). It then models the seasonality in the d18O record by fitting a combination of a growth and temperature sine wave to year-length chunks of the data (see Judd et al., (2018) <doi:10.1016/j.palaeo.2017.09.034>). This modeling is carried out along a sliding window through the data and yields estimates of the day of the year (Julian Day) and local growth rate for each data point. Uncertainties in both modeling routine and the data itself are propagated and pooled to obtain a confidence envelope around the age of each data point in the shell. The end result is a shell chronology consisting of estimated ages of shell formation relative to the annual cycle with their uncertainties. All formulae in the package serve this purpose, but the user can customize the model (e.g. number of days in a year and the mineralogy of the shell carbonate) through input parameters.

This package implements a parameter expanded variational Bayes algorithm for linear regression models with high-dimensional variable selection. The methodology utilizes spike-and-slab priors to perform simultaneous estimation and selection. Details can be found in Olejua et al. (2024) <doi:10.21203/rs.3.rs-7208847/v1>.

This package provides functions for calculating species richness for rarefaction and extrapolation, primarily non-parametric species richness such as jackknife, Chao1, and ACE. Also available are functions for plotting species richness and extrapolation curves, and computing standard diversity and entropy indices.

R interface to Apache Spark, a fast and general engine for big data processing, see <https://spark.apache.org/>. This package supports connecting to local and remote Apache Spark clusters, provides a dplyr compatible back-end, and provides an interface to Spark's built-in machine learning algorithms.

Simulation of event histories with possibly non-linear baseline hazard rate functions, non-linear (time-varying) covariate effect functions, and dependencies on the past of the history. Random generation of event histories is performed using inversion sampling on the cumulative all-cause hazard rate functions.

An extension to the individual claim simulator called SynthETIC (on CRAN), to simulate the evolution of case estimates of incurred losses through the lifetime of an insurance claim. The transactional simulation output now comprises key dates, and both claim payments and revisions of estimated incurred losses. An initial set of test parameters, designed to mirror the experience of a real insurance portfolio, were set up and applied by default to generate a realistic test data set of incurred histories (see vignette). However, the distributional assumptions used to generate this data set can be easily modified by users to match their experiences. Reference: Avanzi B, Taylor G, Wang M (2021) "SPLICE: A Synthetic Paid Loss and Incurred Cost Experience Simulator" <arXiv:2109.04058>.

This package provides historical datasets related to John Snow's 1854 cholera outbreak study in London. Includes data on cholera cases, water pump locations, and the street layout, enabling analysis and visualisation of the outbreak.

This package implements the methodological developments found in Hermes (2025) <doi:10.48550/arXiv.2503.02786>, and allows for the statistical modeling of data consisting of multiple users that provide an ordinal rating for one or multiple items.

The Patient Rule Induction Method (PRIM) is typically used for "bump hunting" data mining to identify regions with abnormally high concentrations of data with large or small values. This package extends this methodology so that it can be applied to binary classification problems and used for prediction.

This package provides functions for computing split regularized estimators defined in Christidis, Lakshmanan, Smucler and Zamar (2019) <doi:10.48550/arXiv.1712.03561>. The approach fits linear regression models that split the set of covariates into groups. The optimal split of the variables into groups and the regularized estimation of the regression coefficients are performed by minimizing an objective function that encourages sparsity within each group and diversity among them. The estimated coefficients are then pooled together to form the final fit.

Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter models with shrinkage priors, both dynamic and static. Details on the algorithms used are provided in Bitto and Frühwirth-Schnatter (2019) <doi:10.1016/j.jeconom.2018.11.006> and Cadonna et al. (2020) <doi:10.3390/econometrics8020020> and Knaus and Frühwirth-Schnatter (2023) <doi:10.48550/arXiv.2312.10487>. For details on the package, please see Knaus et al. (2021) <doi:10.18637/jss.v100.i13>. For the multivariate extension, see the shrinkTVPVAR package.

Download files hosted on AWS S3 (Amazon Web Services Simple Storage Service; <https://aws.amazon.com/s3/>) to a local directory based on their URI. Avoid downloading files that are already present locally. Allow for customization of where to store downloaded files.

Implementation of the modified skew discrete Laplace (SDL) regression model. The package provides a set of functions for a complete analysis of integer-valued data, where the dependent variable is assumed to follow a modified SDL distribution. This regression model is useful for the analysis of integer-valued data and experimental studies in which paired discrete observations are collected.

Propose an area-level, non-parametric regression estimator based on Nadaraya-Watson kernel on small area mean. Adopt a two-stage estimation approach proposed by Prasad and Rao (1990). Mean Squared Error (MSE) estimators are not readily available, so resampling method that called bootstrap is applied. This package are based on the model proposed in Two stage non-parametric approach for small area estimation by Pushpal Mukhopadhyay and Tapabrata Maiti(2004) <http://www.asasrms.org/Proceedings/y2004/files/Jsm2004-000737.pdf>.