Biodiversity is a multifaceted concept covering different levels of organization from genes to ecosystems. iNEXT.3D extends iNEXT to include three dimensions (3D) of biodiversity, i.e., taxonomic diversity (TD), phylogenetic diversity (PD) and functional diversity (FD). This package provides functions to compute standardized 3D diversity estimates with a common sample size or sample coverage. A unified framework based on Hill numbers and their generalizations (Hill-Chao numbers) are used to quantify 3D. All 3D estimates are in the same units of species/lineage equivalents and can be meaningfully compared. The package features size- and coverage-based rarefaction and extrapolation sampling curves to facilitate rigorous comparison of 3D diversity across individual assemblages. Asymptotic 3D diversity estimates are also provided. See Chao et al. (2021) <doi:10.1111/2041-210X.13682> for more details.

An R implementation of Matthew Thomas's Python library inteq'. First, this solves Fredholm integral equations of the first kind ($f(s) = \int_a^b K(s, y) g(y) dy$) using methods described by Twomey (1963) <doi:10.1145/321150.321157>. Second, this solves Volterra integral equations of the first kind ($f(s) = \int_0^s K(s,y) g(t) dt$) using methods from Betto and Thomas (2021) <doi:10.48550/arXiv.2106.08496>. Third, this solves Voltera integral equations of the second kind ($g(s) = f(s) + \int_a^s K(s,y) g(y) dy$) using methods from Linz (1969) <doi:10.1137/0706034>.

Reads the output of the PerkinElmer InForm software <http://www.perkinelmer.com/product/inform-cell-analysis-one-seat-cls135781>. In addition to cell-density count, it can derive statistics of intercellular spatial distance for each cell-type.

Genome-wide gene insertion and deletion rates can be modelled in a maximum likelihood framework with the additional flexibility of modelling potential missing data using the models included within. These models simultaneously estimate insertion and deletion (indel) rates of gene families and proportions of "missing" data for (multiple) taxa of interest. The likelihood framework is utilized for parameter estimation. A phylogenetic tree of the taxa and gene presence/absence patterns (with data ordered by the tips of the tree) are required. See Dang et al. (2016) <doi:10.1534/genetics.116.191973> for more details.

R interface to access the Vocabularies REST API of the ICES (International Council for the Exploration of the Sea) Vocabularies database <https://vocab.ices.dk/services/>.

Identify Cancer Dysfunctional Sub-pathway by integrating gene expression, DNA methylation and copy number variation, and pathway topological information. 1)We firstly calculate the gene risk scores by integrating three kinds of data: DNA methylation, copy number variation, and gene expression. 2)Secondly, we perform a greedy search algorithm to identify the key dysfunctional sub-pathways within the pathways for which the discriminative scores were locally maximal. 3)Finally, the permutation test was used to calculate statistical significance level for these key dysfunctional sub-pathways.

When you want to install R package or download file from GitHub, but you can't access GitHub, this package helps you install R packages or download file from GitHub via the proxy website <https://gh-proxy.com/> or <https://ghfast.top/>, which is in real-time sync with GitHub.

One function to read files. One function to write files. One function to direct plots to screen or file. Automatic file format inference and directory structure creation.

This package provides access to core inflation functions. Four different core inflation functions are provided. The well known trimmed means, exclusion and double weighing methods, alongside the new Triple Filter method introduced in Ferreira et al. (2016) <https://goo.gl/UYLhcj>.

Computes individual causes of death and population cause-specific mortality fractions using the InSilicoVA algorithm from McCormick et al. (2016) <DOI:10.1080/01621459.2016.1152191>. It uses data derived from verbal autopsy (VA) interviews, in a format similar to the input of the widely used InterVA method. This package provides general model fitting and customization for InSilicoVA algorithm and basic graphical visualization of the output.

Calculates various intraclass correlation coefficients used to quantify inter-rater and intra-rater reliability. The assumption here is that the raters produced quantitative ratings. Most of the statistical procedures implemented in this package are described in details in Gwet, K.L. (2014, ISBN:978-0970806284): "Handbook of Inter-Rater Reliability," 4th edition, Advanced Analytics, LLC.

Builds statistical control charts with exact limits for univariate and multivariate cases.

We provide the collection of data-sets used in the book An Introduction to Statistical Learning with Applications in R, Second Edition'. These include many data-sets that we used in the first edition (some with minor changes), and some new datasets.

Quick indexation of any type of vector or of any combination of those. Indexation turns a vector into an integer vector going from 1 to the number of unique elements. Indexes are important building blocks for many algorithms. The method is described at <https://github.com/lrberge/indexthis/>.

This package provides a suite of functions to use with regression models, including summaries, residual plots, and factor comparisons. Used as part of the Model Fitting module of iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions.

Interesting igraph datasets from Melanie Walsh's sample social network datasets repository <https://github.com/melaniewalsh/sample-social-network-datasets>.

Instrumental variable estimation for linear models by two-stage least-squares (2SLS) regression or by robust-regression via M-estimation (2SM) or MM-estimation (2SMM). The main ivreg() model-fitting function is designed to provide a workflow as similar as possible to standard lm() regression. A wide range of methods is provided for fitted ivreg model objects, including extensive functionality for computing and graphing regression diagnostics in addition to other standard model tools.

Calculate false ring proportions from data frames of intra annual density fluctuations.

Implementation of the methodology proposed in Data-driven design of targeted gene panels for estimating immunotherapy biomarkers', Bradley and Cannings (2021) <arXiv:2102.04296>. This package allows the user to fit generative models of mutation from an annotated mutation dataset, and then further to produce tunable linear estimators of exome-wide biomarkers. It also contains functions to simulate mutation annotated format (MAF) data, as well as to analyse the output and performance of models.

Leveraging information-theoretic measures like mutual information and v-measure to quantify spatial associations between patterns (Nowosad and Stepinski (2018) <doi:10.1080/13658816.2018.1511794>; Bai, H. et al. (2023) <doi:10.1080/24694452.2023.2223700>).

Fast and multi-threaded implementation of isolation forest (Liu, Ting, Zhou (2008) <doi:10.1109/ICDM.2008.17>), extended isolation forest (Hariri, Kind, Brunner (2018) <doi:10.48550/arXiv.1811.02141>), SCiForest (Liu, Ting, Zhou (2010) <doi:10.1007/978-3-642-15883-4_18>), fair-cut forest (Cortes (2021) <doi:10.48550/arXiv.2110.13402>), robust random-cut forest (Guha, Mishra, Roy, Schrijvers (2016) <http://proceedings.mlr.press/v48/guha16.html>), and customizable variations of them, for isolation-based outlier detection, clustered outlier detection, distance or similarity approximation (Cortes (2019) <doi:10.48550/arXiv.1910.12362>), isolation kernel calculation (Ting, Zhu, Zhou (2018) <doi:10.1145/3219819.3219990>), and imputation of missing values (Cortes (2019) <doi:10.48550/arXiv.1911.06646>), based on random or guided decision tree splitting, and providing different metrics for scoring anomalies based on isolation depth or density (Cortes (2021) <doi:10.48550/arXiv.2111.11639>). Provides simple heuristics for fitting the model to categorical columns and handling missing data, and offers options for varying between random and guided splits, and for using different splitting criteria.

An implementation of corrected sandwich variance (CSV) estimation method for making inference of marginal hazard ratios (HR) in inverse probability weighted (IPW) Cox model without and with clustered data, proposed by Shu, Young, Toh, and Wang (2019) in their paper under revision for Biometrics. Both conventional inverse probability weights and stabilized weights are implemented. Logistic regression model is assumed for propensity score model.

Instrumental variable (IV) estimators for homogeneous and heterogeneous treatment effects with efficient machine learning instruments. The estimators are based on double/debiased machine learning allowing for nonlinear and potentially high-dimensional control variables. Details can be found in Scheidegger, Guo and Bühlmann (2025) "Inference for heterogeneous treatment effects with efficient instruments and machine learning" <doi:10.48550/arXiv.2503.03530>.

R dependency injection framework. Dependency injection allows a program design to follow the dependency inversion principle. The user delegates to external code (the injector) the responsibility of providing its dependencies. This separates the responsibilities of use and construction.