Responsive and modern HTML card essentials for shiny applications and dashboards. This novel card component in Bootstrap provides a flexible and extensible content container with multiple variants and options for building robust R based apps e.g for graph build or machine learning projects. The features rely on a combination of JQuery <https://jquery.com> and CSS styles to improve the card functionality.

This package contains a function called dmur() which accepts four parameters like possible values, probabilities of the values, selling cost and preparation cost. The dmur() function generates various numeric decision parameters like MEMV (Maximum (optimum) expected monitory value), best choice, EPPI (Expected profit with perfect information), EVPI (Expected value of the perfect information), EOL (Expected opportunity loss), which facilitate effective decision-making.

Simple Principal Components Analysis (PCA) and (Multiple) Correspondence Analysis (CA) based on the Singular Value Decomposition (SVD). This package provides S4 classes and methods to compute, extract, summarize and visualize results of multivariate data analysis. It also includes methods for partial bootstrap validation described in Greenacre (1984, ISBN: 978-0-12-299050-2) and Lebart et al. (2006, ISBN: 978-2-10-049616-7).

This package provides a genetic algorithm for finding variable subsets in high dimensional data with high prediction performance. The genetic algorithm can use ordinary least squares (OLS) regression models or partial least squares (PLS) regression models to evaluate the prediction power of variable subsets. By supporting different cross-validation schemes, the user can fine-tune the tradeoff between speed and quality of the solution.

This package provides density, distribution and random generation functions for the Linear Ballistic Accumulation (LBA) model, a widely used choice response time model in cognitive psychology. The package supports model specifications, parameter estimation, and likelihood computation, facilitating simulation and statistical inference for LBA-based experiments. For details on the LBA model, see Brown and Heathcote (2008) <doi:10.1016/j.cogpsych.2007.12.002>.

Proxy forward modelling for sediment archived climate proxies such as Mg/Ca, d18O or Alkenones. The user provides a hypothesised "true" past climate, such as output from a climate model, and details of the sedimentation rate and sampling scheme of a sediment core. Sedproxy returns simulated proxy records. Implements the methods described in Dolman and Laepple (2018) <doi:10.5194/cp-14-1851-2018>.

This package provides a comprehensive suite of portfolio spanning tests for asset pricing, such as Huberman and Kandel (1987) <doi:10.1111/j.1540-6261.1987.tb03917.x>, Gibbons et al. (1989) <doi:10.2307/1913625>, Kempf and Memmel (2006) <doi:10.1007/BF03396737>, Pesaran and Yamagata (2024) <doi:10.1093/jjfinec/nbad002>, and Gungor and Luger (2016) <doi:10.1080/07350015.2015.1019510>.

This package provides a tool to rectangle a nested list, that is to convert it into a tibble. This is done automatically or according to a given specification. A common use case is for nested lists coming from parsing JSON files or the JSON response of REST APIs. It is supported by the vctrs package and therefore offers a wide support of vector types.

This package provides a collection of functions for automatically creating Stan code for transition diagnostic classification models (TDCMs) as they are defined by Madison and Bradshaw (2018) <DOI:10.1007/s11336-018-9638-5>. This package supports automating the creation of Stan code for TDCMs, fungible TDCMs (i.e., TDCMs with item parameters constrained to be equal across all items), and multi-threaded TDCMs.

Fits hierarchical models of animal abundance and occurrence to data collected using survey methods such as point counts, site occupancy sampling, distance sampling, removal sampling, and double observer sampling. Parameters governing the state and observation processes can be modeled as functions of covariates. References: Kellner et al. (2023) <doi:10.1111/2041-210X.14123>, Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.

This package provides an R port of the library Clipper. It performs polygon clipping operations (intersection, union, set minus, set difference) for polygonal regions of arbitrary complexity, including holes. It computes offset polygons (spatial buffer zones, morphological dilations, Minkowski dilations) for polygonal regions and polygonal lines. It computes the Minkowski Sum of general polygons. There is a function for removing self-intersections from polygon data.

This package provides tools for the variable selection from random forests using both backwards variable elimination (for the selection of small sets of non-redundant variables) and selection based on the importance spectrum (somewhat similar to scree plots; for the selection of large, potentially highly-correlated variables). The main applications are in high-dimensional data (e.g., microarray data, and other genomics and proteomics applications).

Interacting with binary files can be difficult because R's types are a subset of what is generally supported by C'. This package provides a suite of functions for reading and writing binary data (with files, connections, and raw vectors) using C type descriptions. These functions convert data between C types and R types while checking for values outside the type limits, NA values, etc.

Nonparametric kernel density estimation, bandwidth selection, and other utilities for analyzing directional data. Implements the estimator in Bai, Rao and Zhao (1987) <doi:10.1016/0047-259X(88)90113-3>, the cross-validation bandwidth selectors in Hall, Watson and Cabrera (1987) <doi:10.1093/biomet/74.4.751> and the plug-in bandwidth selectors in Garcà a-Portugués (2013) <doi:10.1214/13-ejs821>.

This package provides functions and data supporting the Eco-Stats text (Warton, 2022, Springer), and solutions to exercises. Functions include tools for using simulation envelopes in diagnostic plots, and a function for diagnostic plots of multivariate linear models. Datasets mentioned in the package are included here (where not available elsewhere) and there is a vignette for each chapter of the text with solutions to exercises.

R binds GeoSpark <http://geospark.datasyslab.org/> extending sparklyr <https://spark.rstudio.com/> R package to make distributed geocomputing easier. Sf is a package that provides [simple features] <https://en.wikipedia.org/wiki/Simple_Features> access for R and which is a leading geospatial data processing tool. Geospark R package bring the same simple features access like sf but running on Spark distributed system.

Uses simple Bayesian conjugate prior update rules to calculate the win probability of each option, value remaining in the test, and percent lift over the baseline for various marketing objectives. References: Fink, Daniel (1997) "A Compendium of Conjugate Priors" <https://www.johndcook.com/CompendiumOfConjugatePriors.pdf>. Stucchio, Chris (2015) "Bayesian A/B Testing at VWO" <https://vwo.com/downloads/VWO_SmartStats_technical_whitepaper.pdf>.

This package provides a correlation-based batch process for fast, accurate imputation for high dimensional missing data problems via chained random forests. See Waggoner (2023) <doi:10.1007/s00180-023-01325-9> for more on hdImpute', Stekhoven and Bühlmann (2012) <doi:10.1093/bioinformatics/btr597> for more on missForest', and Mayer (2022) <https://github.com/mayer79/missRanger> for more on missRanger'.

This R package implements methods for estimation and inference under Incomplete Block Designs and Balanced Incomplete Block Designs within a design-based finite-population framework. Based on Koo and Pashley (2024) <arXiv:2405.19312>, it includes block-level estimators and extends to unit-level effects using Horvitz-Thompson and Hájek estimators. The package also provides asymptotic confidence intervals to support valid statistical inference.

The K-sample omnibus non-proportional hazards (KONP) tests are powerful non-parametric tests for comparing K (>=2) hazard functions based on right-censored data (Gorfine, Schlesinger and Hsu, 2020, <doi:10.1177/0962280220907355>). These tests are consistent against any differences between the hazard functions of the groups. The KONP tests are often more powerful than other existing tests, especially under non-proportional hazard functions.

LineUp is an interactive technique designed to create, visualize and explore rankings of items based on a set of heterogeneous attributes. This is a htmlwidget wrapper around the JavaScript library LineUp.js'. It is designed to be used in R Shiny apps and R Markddown files. Due to an outdated webkit version of RStudio it won't work in the integrated viewer.

Data class for increased interoperability working with spatial-temporal data together with corresponding functions and methods (conversions, basic calculations and basic data manipulation). The class distinguishes between spatial, temporal and other dimensions to facilitate the development and interoperability of tools build for it. Additional features are name-based addressing of data and internal consistency checks (e.g. checking for the right data order in calculations).

Generates functional Magnetic Resonance Imaging (fMRI) time series or 4D data. Some high-level functions are created for fast data generation with only a few arguments and a diversity of functions to define activation and noise. For more advanced users it is possible to use the low-level functions and manipulate the arguments. See Welvaert et al. (2011) <doi:10.18637/jss.v044.i10>.

Framework is devoted to mining numerical association rules through the utilization of nature-inspired algorithms for optimization. Drawing inspiration from the NiaARM Python and the NiaARM Julia packages, this repository introduces the capability to perform numerical association rule mining in the R programming language. Fister Jr., Iglesias, Galvez, Del Ser, Osaba and Fister (2018) <doi:10.1007/978-3-030-03493-1_9>.