Extract the implied risk neutral density from options using various methods.

This package contains a collection of helper functions to use with rbi', the R interface to LibBi', described in Murray et al. (2015) <doi:10.18637/jss.v067.i10>. It contains functions to adapt the proposal distribution and number of particles in particle Markov-Chain Monte Carlo, as well as calculating the Deviance Information Criterion (DIC) and converting between times in LibBi results and R time/dates.

Implementation of the MaxRank normalization method, which enables standardization of Rank Abundance Distributions (RADs) to a specified number of ranks. Rank abundance distributions are widely used in biology and ecology to describe species abundances, and are mathematically equivalent to complementary cumulative distribution functions (CCDFs) used in physics, linguistics, sociology, and other fields. The method is described in Saeedghalati et al. (2017) <doi:10.1371/journal.pcbi.1005362>.

This package implements a high performance C++ parser for ActiGraph GT3X'/'GT3X+ data format (with extension .gt3x') for accelerometer samples. Activity samples can be easily read into a matrix or data.frame. This allows for storing the raw accelerometer samples in the original binary format to reserve space.

Dump source code, documentation and vignettes of an R package into a single file. Supports installed packages, tar.gz archives, and package source directories. If the package is not installed, only its source is automatically downloaded from CRAN for processing. The output is a single plain text file or a character vector, which is useful to ingest complete package documentation and source into a large language model (LLM) or pass it further to other tools, such as ragnar <https://github.com/tidyverse/ragnar> to create a Retrieval-Augmented Generation (RAG) workflow.

This package provides a tool to conquer the difficulties to convert various region names and administration division codes of Chinese regions. The current version enables seamlessly converting Chinese regions formal names, common-used names, and codes between each other at the city level from 1986 to 2019.

This package provides the datasets in the book "Methods of Multivariate Analysis (3rd)", such as Table 6.27 Blood Pressure Data, for statistical analysis,especially MANOVA. The dataset names correspond to their numbering in the third edition of the book, such as table6.27. Based on the book by Rencher and Christensen (2012, ISBN:9780470178966).

Reads data files acquired by Bruker Daltonics matrix-assisted laser desorption/ionization-time-of-flight mass spectrometer of the *flex series.

This package provides useful tools which supplement the use of Simulx software and R connectors ('Monolix Suite'). Simulx is an easy, efficient and flexible application for clinical trial simulations. You need Simulx software to be installed in order to use RsSimulx package. Among others tasks, RsSimulx provides the same functions as package mlxR does with a compatibility with Simulx software.

The goal of rlowdb is to provide a lightweight, file-based JSON database. Inspired by LowDB in JavaScript', it generates an intuitive interface for storing, retrieving, updating, and querying structured data without requiring a full-fledged database system. Ideal for prototyping, small-scale applications, and lightweight data management needs.

The main purpose of this package is to streamline the generation of exams that include random elements in exercises. Exercises can be defined in a table, based on text and figures, and may contain gaps to be filled with provided options. Exam documents can be generated in various formats. It allows us to generate a version for conducting the assessment and another version that facilitates correction, linked through a code.

This package implements a comprehensive suite of statistical tests for evaluating the accuracy of forecasting models against a benchmark. The package is grounded in the reality check framework of White (2000) <doi:10.1111/1468-0262.00152>, extended by Hansen (2005) <doi:10.1198/073500105000000063> for Superior Predictive Ability (SPA), Giacomini & White (2006) <doi:10.1111/j.1468-0262.2006.00718.x> for Conditional Predictive Ability (CPA), and Corradi & Swanson (2006) <doi:10.1016/j.jeconom.2005.07.026> for predictive density evaluation via the Kullback'-'Leibler Information Criterion ('KLIC') and ZP Quantile Loss test, the Continuous Ranked Probability Score ('CRPS') ('Gneiting & Raftery', 2007) <doi:10.1198/016214506000001437>, coverage tests ('Kupiec', 1995) <doi:10.3905/jod.1995.407942>, HAC covariance estimation ('Newey & West, 1987) <doi:10.2307/1913610>, and Moving Block Bootstrap resampling ('Kunsch', 1989) <doi:10.1214/aos/1176347265>.

Estimates Pareto-optimal solution for personnel selection with 3 objectives using Normal Boundary Intersection (NBI) algorithm introduced by Das and Dennis (1998) <doi:10.1137/S1052623496307510>. Takes predictor intercorrelations and predictor-objective relations as input and generates a series of solutions containing predictor weights as output. Accepts between 3 and 10 selection predictors. Maximum 2 objectives could be adverse impact objectives. Partially modeled after De Corte (2006) TROFSS Fortran program <https://users.ugent.be/~wdecorte/trofss.pdf> and updated from ParetoR package described in Song et al. (2017) <doi:10.1037/apl0000240>. For details, see Study 3 of Zhang et al. (2023).

Computes a novel variable importance for random forests: Impurity reduction importance scores for out-of-bag (OOB) data complementing the existing inbag Gini importance, see also <doi: 10.1080/03610926.2020.1764042>. The Gini impurities for inbag and OOB data are combined in three different ways, after which the information gain is computed at each split. This gain is aggregated for each split variable in a tree and averaged across trees.

This package provides a port of Ruby Warrior. Teaches R programming in a fun and interactive way.

Computes revisitation metrics for trajectory data, such as the number of revisitations for each location as well as the time spent for that visit and the time since the previous visit. Also includes functions to plot data.

Allows the user to view an image in full screen when clicking on it in RMarkdown documents and shiny applications. The package relies on the JavaScript library intense-images'. See <https://tholman.com/intense-images/> for more information.

This package provides a framework for the measurement and partitioning of the (similarity-sensitive) biodiversity of a metacommunity and its constituent subcommunities. Richard Reeve, et al. (2016) <arXiv:1404.6520v3>.

This package provides a toolkit for Commodities analytics', risk management and trading professionals. Includes functions for API calls to <https://www.zema.global/platforms/zema-marketplace>, <https://developer.genscape.com/>, and <https://www.bankofcanada.ca/valet/docs>.

Perform wavelet analysis (orthogonal,translation invariant, tensorial, 1-2-3d transforms, thresholding, block thresholding, linear,...) with applications to data compression or denoising/regression. The core of the code is a port of MATLAB Wavelab toolbox written by D. Donoho, A. Maleki and M. Shahram (<https://statweb.stanford.edu/~wavelab/>).

This package provides functions to compute Shannon entropy, Renyi entropy, Tsallis entropy, and related extropy measures for discrete probability distributions. Includes joint and conditional entropy, KL divergence, Jensen-Shannon divergence, cross-entropy, normalized entropy, and Renyi extropy (including the conditional and maximum forms). All measures use the natural logarithm (nats). Useful for information theory, statistics, and machine learning applications.

This package provides a common framework for calculating distance matrices.

Parser generator for R using combinatory parsers. It is inspired by combinatory parsers developed in Haskell.

This package provides a robust alternative to the traditional principal component estimator is proposed within the framework of factor models, known as Robust Exponential Factor Analysis, specifically designed for the modeling of high-dimensional datasets with heavy-tailed distributions. The algorithm estimates the latent factors and the loading by minimizing the exponential squared loss function. To determine the appropriate number of factors, we propose a modified rank minimization technique, which has been shown to significantly enhance finite-sample performance. For more detail of Robust Exponential Factor Analysis, please refer to Hu et al. (2026) <doi:10.1016/j.jmva.2025.105567>.