Diversification is one of the most important concepts in portfolio management. This framework offers scholars, practitioners and policymakers a useful toolbox to measure diversification. Specifically, this framework provides recent diversification measures from the recent literature. These diversification measures are based on the works of Rudin and Morgan (2006) <doi:10.3905/jpm.2006.611807>, Choueifaty and Coignard (2008) <doi:10.3905/JPM.2008.35.1.40>, Vermorken et al. (2012) <doi:10.3905/jpm.2012.39.1.067>, Flores et al. (2017) <doi:10.3905/jpm.2017.43.4.112>, Calvet et al. (2007) <doi:10.1086/524204>, and Candelon, Fuerst and Hasse (2020).

Data and miscellanea to support the book "Introduction to Data analysis with R for Forensic Scientists." This book was written by James Curran and published by CRC Press in 2010 (ISBN: 978-1-4200-8826-7).

Allows you to define rules which can be used to verify a given dataset. The package acts as a thin wrapper around more powerful data packages such as dplyr', data.table', arrow', and DBI ('SQL'), which do the heavy lifting.

Direction analysis is a set of tools designed to identify combinatorial effects of multiple treatments/conditions on pathways and kinases profiled by microarray, RNA-seq, proteomics, or phosphoproteomics data. See Yang P et al (2014) <doi:10.1093/bioinformatics/btt616>; and Yang P et al. (2016) <doi:10.1002/pmic.201600068>.

This package performs Bayesian posterior inference for deep Gaussian processes following Sauer, Gramacy, and Higdon (2023, <doi:10.48550/arXiv.2012.08015>). See Sauer (2023, <http://hdl.handle.net/10919/114845>) for comprehensive methodological details and <https://bitbucket.org/gramacylab/deepgp-ex/> for a variety of coding examples. Models are trained through MCMC including elliptical slice sampling of latent Gaussian layers and Metropolis-Hastings sampling of kernel hyperparameters. Gradient-enhancement and gradient predictions are offered following Booth (2025, <doi:10.48550/arXiv.2512.18066>). Vecchia approximation for faster computation is implemented following Sauer, Cooper, and Gramacy (2023, <doi:10.48550/arXiv.2204.02904>). Optional monotonic warpings are implemented following Barnett et al. (2025, <doi:10.48550/arXiv.2408.01540>). Downstream tasks include sequential design through active learning Cohn/integrated mean squared error (ALC/IMSE; Sauer, Gramacy, and Higdon, 2023), optimization through expected improvement (EI; Gramacy, Sauer, and Wycoff, 2022, <doi:10.48550/arXiv.2112.07457>), and contour location through entropy (Booth, Renganathan, and Gramacy, 2025, <doi:10.48550/arXiv.2308.04420>). Models extend up to three layers deep; a one layer model is equivalent to typical Gaussian process regression. Incorporates OpenMP and SNOW parallelization and utilizes C/C++ under the hood.

Double constrained correspondence analysis (dc-CA) analyzes (multi-)trait (multi-)environment ecological data by using the vegan package and native R code. Throughout the two step algorithm of ter Braak et al. (2018) is used. This algorithm combines and extends community- (sample-) and species-level analyses, i.e. the usual community weighted means (CWM)-based regression analysis and the species-level analysis of species-niche centroids (SNC)-based regression analysis. The two steps use canonical correspondence analysis to regress the abundance data on to the traits and (weighted) redundancy analysis to regress the CWM of the orthonormalized traits on to the environmental predictors. The function dc_CA() has an option to divide the abundance data of a site by the site total, giving equal site weights. This division has the advantage that the multivariate analysis corresponds with an unweighted (multi-trait) community-level analysis, instead of being weighted. The first step of the algorithm uses vegan::cca(). The second step uses wrda() but vegan::rda() if the site weights are equal. This version has a predict() function. For details see ter Braak et al. 2018 <doi:10.1007/s10651-017-0395-x>. and ter Braak & van Rossum 2025 <doi:10.1016/j.ecoinf.2025.103143>.

Divide taxonomic occurrence data into geographic regions of fair comparison, with three customisable methods to standardise area and extent. Calculate common biodiversity and range-size metrics on subsampled data. Background theory and practical considerations for the methods are described in Antell and others (2024) <doi:10.1017/pab.2023.36>.

Empirical Bayes methods for learning prior distributions from data. An unknown prior distribution (g) has yielded (unobservable) parameters, each of which produces a data point from a parametric exponential family (f). The goal is to estimate the unknown prior ("g-modeling") by deconvolution and Empirical Bayes methods. Details and examples are in the paper by Narasimhan and Efron (2020, <doi:10.18637/jss.v094.i11>).

This package provides a wide collection of univariate discrete data sets from various applied domains related to distribution theory. The functions allow quick, easy, and efficient access to 100 univariate discrete data sets. The data are related to different applied domains, including medical, reliability analysis, engineering, manufacturing, occupational safety, geological sciences, terrorism, psychology, agriculture, environmental sciences, road traffic accidents, demography, actuarial science, law, and justice. The documentation, along with associated references for further details and uses, is presented.

Simple functions to deflate nominal Brazilian Reais using several popular price indexes downloaded from the Brazilian Institute for Applied Economic Research.

Utility functions to be used to analyse datasets obtained from seed germination/emergence assays. Fits several types of seed germination/emergence models, including those reported in Onofri et al. (2018) "Hydrothermal-time-to-event models for seed germination", European Journal of Agronomy, 101, 129-139 <doi:10.1016/j.eja.2018.08.011>. Contains several datasets for practicing.

This package performs Bayesian model averaging for capture-recapture. This includes code to stratify records, check the strata for suitable overlap to be used for capture-recapture, and some functions to plot the estimated population size.

Companion to the book "An Introduction to Clustering with R" by P. Giordani, M.B. Ferraro and F. Martella (Springer, Singapore, 2020). The datasets are used in some case studies throughout the text.

This package provides a suite of loon related packages providing data analytic tools for Direct Interactive Visual Exploration in R ('diveR'). These tools work with and complement those of the tidyverse suite, extending the grammar of ggplot2 to become a grammar of interactive graphics. The suite provides many visual tools designed for moderately (100s of variables) high dimensional data analysis, through zenplots and novel tools in loon', and extends the ggplot2 grammar to provide parallel coordinates, Andrews plots, and arbitrary glyphs through ggmulti'. The diveR package gathers together and installs all these related packages in a single step.

This package implements methods for calculating disproportionate impact: the percentage point gap, proportionality index, and the 80% index. California Community Colleges Chancellor's Office (2017). Percentage Point Gap Method. <https://www.cccco.edu/-/media/CCCCO-Website/About-Us/Divisions/Digital-Innovation-and-Infrastructure/Research/Files/PercentagePointGapMethod2017.ashx>. California Community Colleges Chancellor's Office (2014). Guidelines for Measuring Disproportionate Impact in Equity Plans. <https://www.cccco.edu/-/media/CCCCO-Website/Files/DII/guidelines-for-measuring-disproportionate-impact-in-equity-plans-tfa-ada.pdf>.

Generate point data for representing people within spatial data. This collects a suite of tools for creating simple dot density maps. Several functions from different spatial packages are standardized to take the same arguments so that they can be easily substituted for each other.

This package provides a simple interface to build designs using the package DeclareDesign'. In one line of code, users can specify the parameters of individual designs and diagnose their properties. The designers can also be used to compare performance of a given design across a range of combinations of parameters, such as effect size, sample size, and assignment probabilities.

We consider a multiple testing procedure used in many modern applications which is the q-value method proposed by Storey and Tibshirani (2003), <doi:10.1073/pnas.1530509100>. The q-value method is based on the false discovery rate (FDR), hence versions of the q-value method can be defined depending on which estimator of the proportion of true null hypotheses, p0, is plugged in the FDR estimator. We implement the q-value method based on two classical pi0 estimators, and furthermore, we propose and implement three versions of the q-value method for homogeneous discrete uniform P-values based on pi0 estimators which take into account the discrete distribution of the P-values.

Add a "Did You Mean" feature to the R interactive. With this package, error messages for misspelled input of variable names or package names suggest what you really want to do in addition to notification of the mistake.

Detection of runs of homozygosity and of heterozygosity in diploid genomes using two methods: sliding windows (Purcell et al (2007) <doi:10.1086/519795>) and consecutive runs (Marras et al (2015) <doi:10.1111/age.12259>).

This package performs Diffusion Non-Additive (DNA) model proposed by Heo, Boutelet, and Sung (2025+) <doi:10.48550/arXiv.2506.08328> for multi-fidelity computer experiments with tuning parameters. The DNA model captures nonlinear dependencies across fidelity levels using Gaussian process priors and is particularly effective when simulations at different fidelity levels are nonlinearly correlated. The DNA model targets not only interpolation across given fidelity levels but also extrapolation to smaller tuning parameters including the exact solution corresponding to a zero-valued tuning parameter, leveraging a nonseparable covariance kernel structure that models interactions between the tuning parameter and input variables. Closed-form expressions for the predictive mean and variance enable efficient inference and uncertainty quantification. Hyperparameters in the model are estimated via maximum likelihood estimation.

An R interface to the Free Dictionary API <https://dictionaryapi.dev/>, <https://github.com/meetDeveloper/freeDictionaryAPI>. Retrieve dictionary definitions for English words, as well as additional information including phonetics, part of speech, origins, audio pronunciation, example usage, synonyms and antonyms, returned in tidy format for ease of use.

Item focussed recursive partitioning for simultaneous selection of items and variables that induce Differential Item Functioning (DIF) in dichotomous or polytomous items.

Estimation of Difference-in-Differences (DiD) estimators from de Chaisemartin et al. (2025) <doi:10.48550/arXiv.2405.04465> in Heterogeneous Adoption Designs with Quasi Untreated Groups.