Robust kernel center matrix, robust kernel cross-covariance operator for kernel unsupervised methods, kernel canonical correlation analysis, influence function of identifying significant outliers or atypical objects from multimodal datasets. Alam, M. A, Fukumizu, K., Wang Y.-P. (2018) <doi:10.1016/j.neucom.2018.04.008>. Alam, M. A, Calhoun, C. D., Wang Y.-P. (2018) <doi:10.1016/j.csda.2018.03.013>.

This package provides a collection of functions to simulate luminescence production in dosimetric materials using Monte Carlo methods. Implemented are models for delocalised transitions (e.g., Chen and McKeever (1997) <doi:10.1142/2781>), localised transitions (e.g., Pagonis et al. (2019) <doi:10.1016/j.jlumin.2018.11.024>) and tunnelling transitions (Jain et al. (2012) <doi:10.1088/0953-8984/24/38/385402> and Pagonis et al. (2019) <doi:10.1016/j.jlumin.2018.11.024>). Supported stimulation methods are thermal luminescence (TL), continuous-wave optically stimulated luminescence (CW-OSL), linearly-modulated optically stimulated luminescence (LM-OSL), linearly-modulated infrared stimulated luminescence (LM-IRSL), and isothermal luminescence (ITL or ISO-TL).

This package provides a collection of R functions for use with Stock Synthesis, a fisheries stock assessment modeling platform written in ADMB by Dr. Richard D. Methot at the NOAA Northwest Fisheries Science Center. The functions include tools for summarizing and plotting results, manipulating files, visualizing model parameterizations, and various other common stock assessment tasks. This version of r4ss is compatible with Stock Synthesis versions 3.24 through 3.30 (specifically version 3.30.19.01, from April 2022).

An implementation of the Heroicons icon library for shiny applications and other R web-based projects. You can search, render, and customize icons without CSS or JavaScript dependencies.

Floating Percentile Model with additional functions for optimizing inputs and evaluating outputs and assumptions.

This package contains several useful navigation helper functions, including easily building folder paths, quick viewing dataframes in Excel', creating date vectors and changing the console prompt to reflect time.

This package provides a RUT (Rol Unico Tributario) is an unique and personal identification number implemented in Chile to identify citizens and taxpayers. Rutifier allows to validate if a RUT exist or not and change between the different formats a RUT can have.

Generates polygon straight skeletons and 3D models. Provides functions to create and visualize interior polygon offsets, 3D beveled polygons, and 3D roof models.

This package provides an implementation of Regularized LS-TreeBoost & LAD-TreeBoost algorithm for Regulatory Network inference from any type of expression data (Microarray/RNA-seq etc).

BM25 is a ranking function used by search engines to rank matching documents according to their relevance to a user's search query. This package provides a light wrapper around the BM25 rust crate for Okapi BM25 text search. For more information, see Robertson et al. (1994) <https://trec.nist.gov/pubs/trec3/t3_proceedings.html>.

This package provides a set of tools for creation, manipulation, and modeling of tensors with arbitrary number of modes. A tensor in the context of data analysis is a multidimensional array. rTensor does this by providing a S4 class Tensor that wraps around the base array class. rTensor provides common tensor operations as methods, including matrix unfolding, summing/averaging across modes, calculating the Frobenius norm, and taking the inner product between two tensors. Familiar array operations are overloaded, such as index subsetting via [ and element-wise operations. rTensor also implements various tensor decomposition, including CP, GLRAM, MPCA, PVD, and Tucker. For tensors with 3 modes, rTensor also implements transpose, t-product, and t-SVD, as defined in Kilmer et al. (2013). Some auxiliary functions include the Khatri-Rao product, Kronecker product, and the Hadamard product for a list of matrices.

R tools to measure and compare inequality, welfare and poverty using the EU statistics on income and living conditions surveys.

This package provides a rotatogram is a method of displaying an association which is axis non-dominant. This is achieved in two ways: First, the method of estimating the slope and intercept uses the least-products method rather than more typical least squared error for the "dependent" variable. The least products method has no "dependent" variable and is scale independent. Second, the plot is rotated such that the resulting regression line is vertical, reducing the suggestion that the vertical axis is the dominant one. The slope can be read relative to either axis equally.

This package provides a collection of methods for estimating the basic reproduction number (R0) of infectious diseases. Features a web application to interface with the estimators. Uses the models from: Fisman et al. (2013) <DOI:10.1371/journal.pone.0083622>, Bettencourt and Ribeiro (2008) <DOI:10.1371/journal.pone.0002185>, and White and Pagano (2008) <DOI:10.1002/sim.3136>. Includes datasets for Canadian national and provincial COVID-19 case counts provided by Berry et al. (2021) <DOI:10.1038/s41597-021-00955-2>.

Interface to JDemetra+ 3.x (<https://github.com/jdemetra>) time series analysis software. It provides a variety of methods for temporal disaggregation & interpolation, benchmarking, reconciliation and calendarization. It incorporates statistical methods described in the latest European Statistical System (ESS) guidelines on temporal disaggregation, benchmarking, and reconciliation (2018 edition). The package implements highly efficient algorithms for fast and reliable computation.

An implementation of a stochastic heuristic method for performing multidimensional function optimization. The method is inspired in the Cross-Entropy Method. It does not relies on derivatives, neither imposes particularly strong requirements into the function to be optimized. Additionally, it takes profit from multi-core processing to enable optimization of time-consuming functions.

Simple methods to generate attractive random colors. The random colors are from a wrapper of randomColor.js <https://github.com/davidmerfield/randomColor>. In addition, it also generates optimally distinct colors based on k-means (inspired by IWantHue <https://github.com/medialab/iwanthue>).

Utilities for accessing RePEc (Research Papers in Economics) through a RESTful API. You can request a code and get detailed information at the following page: <https://ideas.repec.org/api.html>.

Rcpp reimplementation of the the Bayesian non-parametric Dirichlet Process Regression model for penalized regression first published in Zeng and Zhou (2017) <doi:10.1038/s41467-017-00470-2>. A full Bayesian version is implemented with Gibbs sampling, as well as a faster but less accurate variational Bayes approximation.

This package provides methods for randomization inference in group-randomized trials. Specifically, it can be used to analyze the treatment effect of stratified data with multiple clusters in each stratum with treatment given on cluster level. User may also input as many covariates as they want to fit the data. Methods are described by Dylan S Small et al., (2012) <doi:10.1198/016214507000000897>.

Resampling Stats (http://www.resample.com) is an add-in for running randomization tests in Excel worksheets. The workflow is (1) to define a statistic of interest that can be calculated from a data table, (2) to randomize rows ad/or columns of a data table to simulate a null hypothesis and (3) and to score the value of the statistic from many randomizations. The relative frequency distribution of the statistic in the simulations is then used to infer the probability of the observed value be generated by the null process (probability of Type I error). This package intends to translate this logic for R for teaching purposes. Keeping the original workflow is favored over performance.

Iterative least cost path and minimum spanning tree methods for projecting forest road networks. The methods connect a set of target points to an existing road network using igraph <https://igraph.org> to identify least cost routes. The cost of constructing a road segment between adjacent pixels is determined by a user supplied weight raster and a weight function; options include the average of adjacent weight raster values, and a function of the elevation differences between adjacent cells that penalizes steep grades. These road network projection methods are intended for integration into R workflows and modelling frameworks used for forecasting forest change, and can be applied over multiple time-steps without rebuilding a graph at each time-step.

An easy way to analyze international large-scale assessments and surveys in education or any other dataset that includes replicated weights (Balanced Repeated Replication (BRR) weights, Jackknife replicate weights,...) while also allowing for analysis with multiply imputed variables (plausible values). It supports the estimation of univariate statistics (e.g. mean, variance, standard deviation, quantiles), frequencies, correlation, linear regression and any other model already implemented in R that takes a data frame and weights as parameters. It also includes options to prepare the results for publication, following the table formatting standards of the Organization for Economic Cooperation and Development (OECD).

Enhances the R Optimization Infrastructure ('ROI') package with the quadratic solver OSQP'. More information about OSQP can be found at <https://osqp.org>.