An R implementation of the Reinert text clustering method. For more details about the algorithm see the included vignettes or Reinert (1990) <doi:10.1177/075910639002600103>.

Using this package, it is possible to call a BUGS model, summarize inferences and convergence in a table and graph, and save the simulations in arrays for easy access in R.

Drift-Diffusion Model (DDM) has been widely used to model binary decision-making tasks, and many research studies the relationship between DDM parameters and other characteristics of the subject. This package uses RStan to perform generalized liner regression analysis over DDM parameters via a single Bayesian Hierarchical model. Compared to estimating DDM parameters followed by a separate regression model, RegDDM reduces bias and improves statistical power.

This package implements diversification analyses using the phylogenetic birth-death-shift model. It leverages belief propagation techniques to calculate branch-specific diversification rates, see Kopperud & Hoehna (2025) <doi:10.1093/sysbio/syaf041>.

This package provides a lightweight, table-driven engine for executing insurance rating plans, including coverage-specific rating specifications, entity aggregation, and trace output for auditing. Given policy data, an ordered rating specification, and rating factor tables, it returns rated policies and, optionally, a step-by-step trace of the calculation.

This package creates reports from Trello, a collaborative, project organization and list-making application. <https://trello.com/> Reports are created by comparing individual Trello board cards from two different points in time and documenting any changes made to the cards.

This framework aims to provide classes and methods for manipulating and processing of raster time series data (e.g. a time series of satellite images).

We implement full-ranked, rank-penalized, and adaptive nuclear norm penalized estimation methods using multivariate mixture models proposed by Kang, Chen, and Yao (2022+).

Converting ascii text into (floating-point) numeric values is a very common problem. The fast_float header-only C++ library by Daniel Lemire does it very well and very fast at up to or over to 1 gigabyte per second as described in more detail in <doi:10.1002/spe.2984>. fast_float is licensed under the Apache 2.0 license and provided here for use by other R packages via a simple LinkingTo: statement.

Plot regression surfaces and marginal effects in three dimensions. The plots are plotly objects and can be customized using functions and arguments from the plotly package.

This package performs penalized quantile regression with LASSO, elastic net, SCAD and MCP penalty functions including group penalties. In addition, offers a group penalty that provides consistent variable selection across quantiles. Provides a function that automatically generates lambdas and evaluates different models with cross validation or BIC, including a large p version of BIC. Below URL provides a link to article in the R Journal.

Implementation of an alternating direction method of multipliers algorithm for fitting a linear model with tree-based lasso regularization, which is proposed in Algorithm 1 of Yan and Bien (2020) <doi:10.1080/01621459.2020.1796677>. The package allows efficient model fitting on the entire 2-dimensional regularization path for large datasets. The complete set of functions also makes the entire process of tuning regularization parameters and visualizing results hassle-free.

This package implements the fast iterative shrinkage-thresholding algorithm (FISTA) algorithm to fit a Gamma distribution with an elastic net penalty as described in Chen, Arakvin and Martin (2018) <doi:10.48550/arXiv.1804.07780>. An implementation for the case of the exponential distribution is also available, with details available in Chen and Martin (2018) <doi:10.2139/ssrn.3085672>.

Predicts morphological parameters of rorquals (e.g. body mass, flipper length, maximum engulfment capacity) from body length using allometric equations from Kahane-Rapport and Goldbogen (2018) <doi:10.1002/jmor.20846>.

R interface to CPLEX solvers for linear, quadratic, and (linear and quadratic) mixed integer programs. Support for quadratically constrained programming is available. See the file "INSTALL" for details on how to install the Rcplex package in Linux/Unix-like and Windows systems. Support for sparse matrices is provided by an S3-style class "simple_triplet_matrix" from package slam and by objects from the Matrix package class hierarchy.

Optimal linear combination predictive signatures for maximizing the area between two Receiver Operating Characteristic (ROC) curves (treatment vs. control).

This package provides a port of the C++ routine for applying the marching cubes algorithm written by Thomas Lewiner et al. (2012) <doi:10.1080/10867651.2003.10487582> into an R package. The package supplies the contour3d() function, which takes a 3-dimensional array of voxel data and calculates the vertices, vertex normals, and faces for a 3d mesh representing the contour(s) at a given level.

This package performs Wavelet Lifting Transforms focusing on signal denoising and functional data analysis (FDA). Implements a hybrid architecture with a zero-allocation C++ core for high-performance processing. Features include unified offline (batch) denoising, causal (real-time) filtering using a ring buffer engine, and adaptive recursive thresholding.

An implementation of simulated maximum likelihood method for the estimation of Binary (Probit and Logit), Ordered (Probit and Logit) and Poisson models with random parameters for cross-sectional and longitudinal data as presented in Sarrias (2016) <doi:10.18637/jss.v074.i10>.

This package provides functions for studying realized genetic relatedness between people. Users will be able to simulate inheritance patterns given pedigree structures, generate SNP marker data given inheritance patterns, and estimate realized relatedness between pairs of individuals using SNP marker data. See Wang (2017) <doi:10.1534/genetics.116.197004>. This work was supported by National Institutes of Health grants R37 GM-046255.

This package implements the network clustering algorithm described in Newman (2006) <doi:10.1103/PhysRevE.74.036104>. The complete iterative algorithm comprises of two steps. In the first step, the network is expressed in terms of its leading eigenvalue and eigenvector and recursively partition into two communities. Partitioning occurs if the maximum positive eigenvalue is greater than the tolerance (10e-5) for the current partition, and if it results in a positive contribution to the Modularity. Given an initial separation using the leading eigen step, rSpectral then continues to maximise for the change in Modularity using a fine-tuning step - or variate thereof. The first stage here is to find the node which, when moved from one community to another, gives the maximum change in Modularity. This nodeâ s community is then fixed and we repeat the process until all nodes have been moved. The whole process is repeated from this new state until the change in the Modularity, between the new and old state, is less than the predefined tolerance. A slight variant of the fine-tuning step, which can improve speed of the calculation, is also provided. Instead of moving each node into each community in turn, we only consider moves of neighbouring nodes, found in different communities, to the community of the current node of interest. The two steps process is repeatedly applied to each new community found, subdivided each community into two new communities, until we are unable to find any division that results in a positive change in Modularity.

Enhances the R Optimization Infrastructure (ROI) package by registering the CPLEX commercial solver. It allows for solving mixed integer quadratically constrained programming (MIQPQC) problems as well as all variants/combinations of LP, QP, QCP, IP.

Simulate samples from populations with known covariate distributions, generate response variables according to common linear and generalized linear model families, draw from sampling distributions of regression estimates, and perform visual inference on diagnostics from model fits.

This is a Google Forms and Google Classroom API Wrapper for R for managing Google Classrooms from R. The documentation for these APIs is here <https://developers.google.com/forms/api/guides> .