This package provides a comprehensive analysis tool for metabolomics data. It consists a variety of functional modules, including several new modules: a pre-processing module for normalization and imputation, an exploratory data analysis module for dimension reduction and source of variation analysis, a classification module with the new deep-learning method and other machine-learning methods, a prognosis module with cox-PH and neural-network based Cox-nnet methods, and pathway analysis module to visualize the pathway and interpret metabolite-pathway relationships. References: H. Paul Benton <http://www.metabolomics-forum.com/index.php?topic=281.0> Jeff Xia <https://github.com/cangfengzhe/Metabo/blob/master/MetaboAnalyst/website/name_match.R> Travers Ching, Xun Zhu, Lana X. Garmire (2018) <doi:10.1371/journal.pcbi.1006076>.

Bayesian population size estimation using non parametric latent-class models.

Improve your text analysis with languagelayer <https://languagelayer.com>, a powerful language detection API.

Robust test(s) for model diagnostics in regression. The current version contains a robust test for functional specification (linearity). The test is based on the robust bounded-influence test by Heritier and Ronchetti (1994) <doi:10.1080/01621459.1994.10476822>.

This package provides a unified analytical workflow that bridges conventional binary and multinomial logistic regression with singly-ordered (SONSCA) and doubly-ordered (DONSCA) nonsymmetric correspondence analysis. Log-odds ratios (LORs) from logistic regression are re-expressed as cosine theta estimates and closeness-of-concordance measures (CCMs) -- including Yule's Q, Yule's Y, and r_meta -- on the familiar [-1, +1] scale introduced by Kim and Grochowalski (2019) <doi:10.3758/s13428-018-1161-1>. Bootstrap confidence intervals for cosine theta are provided throughout. The package is intended to help clinical and medical researchers interpret association strength from logistic regression in an intuitive, correlation-like metric, and to connect conventional regression results with geometric correspondence analysis visualisations.

Interact with LaminDB'. LaminDB is an open-source data framework for biology. This package allows you to query and download data from LaminDB instances.

This package implements non-parametric tests from Higgins (2004, ISBN:0534387756), including tests for one sample, two samples, k samples, paired comparisons, blocked designs, trends and association. Built with Rcpp for efficiency and R6 for flexible, object-oriented design, the package provides a unified framework for performing or creating custom permutation tests.

Statistical tests widely utilized in biostatistics, public policy, and law. Along with the well-known tests for equality of means and variances, randomness, and measures of relative variability, the package contains new robust tests of symmetry, omnibus and directional tests of normality, and their graphical counterparts such as robust QQ plot, robust trend tests for variances, etc. All implemented tests and methods are illustrated by simulations and real-life examples from legal statistics, economics, and biostatistics.

This package performs analysis of Differential Item Functioning (DIF) for dichotomous and polytomous items using an iterative hybrid of ordinal logistic regression and item response theory (IRT) according to Choi, Gibbons, and Crane (2011) <doi:10.18637/jss.v039.i08>.

Identification of equilibrium locations in location games (Hotelling (1929) <doi:10.2307/2224214>). In these games, two competing actors place customer-serving units in two locations simultaneously. Customers make the decision to visit the location that is closest to them. The functions in this package include Prim algorithm (Prim (1957) <doi:10.1002/j.1538-7305.1957.tb01515.x>) to find the minimum spanning tree connecting all network vertices, an implementation of Dijkstra algorithm (Dijkstra (1959) <doi:10.1007/BF01386390>) to find the shortest distance and path between any two vertices, a self-developed algorithm using elimination of purely dominated strategies to find the equilibrium, and several plotting functions.

This package provides a comprehensive framework for linear regression modeling and associated statistical analysis. The package implements methods for correlation analysis, including computation of correlation matrices with corresponding significance levels and visualization via correlation heatmaps. It supports estimation of multiple linear regression models, along with automated model selection through backward elimination procedures based on statistical significance criteria. In addition, the package offers a suite of diagnostic tools to assess key assumptions of linear regression, including multicollinearity using variance inflation factors, heteroscedasticity using the Goldfeld-Quandt test, and normality of residuals using the Shapiro-Wilk test. These functionalities, as described in Draper and Smith (1998) <doi:10.1002/9781118625590>, are designed to facilitate robust model building, evaluation, and interpretation in applied statistical and data analytical contexts.

Fit and simulate latent position and cluster models for statistical networks. See Krivitsky and Handcock (2008) <doi:10.18637/jss.v024.i05> and Krivitsky, Handcock, Raftery, and Hoff (2009) <doi:10.1016/j.socnet.2009.04.001>.

This package performs approximate GP regression for large computer experiments and spatial datasets. The approximation is based on finding small local designs for prediction (independently) at particular inputs. OpenMP and SNOW parallelization are supported for prediction over a vast out-of-sample testing set; GPU acceleration is also supported for an important subroutine. OpenMP and GPU features may require special compilation. An interface to lower-level (full) GP inference and prediction is provided. Wrapper routines for blackbox optimization under mixed equality and inequality constraints via an augmented Lagrangian scheme, and for large scale computer model calibration, are also provided. For details and tutorial, see Gramacy (2016 <doi:10.18637/jss.v072.i01>.

Analyze graph/network data using L1 centrality and prestige. Functions for deriving global, local, and group L1 centrality/prestige are provided. Routines for visual inspection of a graph/network are also provided. Details are in Kang and Oh (2026a) <doi:10.1080/01621459.2025.2520467>, Kang and Oh (2026b) <doi:10.1080/00031305.2025.2563730>, and Kang (2025) <doi:10.23170/snu.000000188358.11032.0001856>.

The goal of LCMSQA is to make it easy to check the quality of liquid chromatograph/mass spectrometry (LC/MS) experiments using a shiny application. This package provides interactive data visualizations for quality control (QC) samples, including total ion current chromatogram (TIC), base peak chromatogram (BPC), mass spectrum, extracted ion chromatogram (XIC), and feature detection results from internal standards or known metabolites.

Imports a data frame containing a single time resolved laser ablation mass spectrometry analysis of a foraminifera (or other carbonate shell), then detects when the laser has burnt through the foraminifera test as a function of change in signal over time.

This package implements a local likelihood estimator for the dependence parameter in bivariate conditional copula models. Copula family and local likelihood bandwidth parameters are selected by leave-one-out cross-validation. The models are implemented in TMB', meaning that the local score function is efficiently calculated via automated differentiation (AD), such that quasi-Newton algorithms may be used for parameter estimation.

This package provides functions implementing multivariate state space models for purposes of time series analysis and forecasting. The focus of the package is on multivariate models, such as Vector Exponential Smoothing, Vector ETS (Error-Trend-Seasonal model) etc. It currently includes Vector Exponential Smoothing (VES, de Silva et al., 2010, <doi:10.1177/1471082X0901000401>), Vector ETS (Svetunkov et al., 2023, <doi:10.1016/j.ejor.2022.04.040>) and simulation function for VES.

The LIC criterion is to determine the most informative subsets so that the subset can retain most of the information contained in the complete data. The philosophy of the package is described in Guo G. (2022) <doi:10.1080/02664763.2022.2053949>.

Some simple objects and functions to do statistics using linear models and a Bayesian framework.

An extendable toolkit for interactive data visualization and exploration.

Assess the proportion of treatment effect explained by a longitudinal surrogate marker as described in Agniel D and Parast L (2021) <doi:10.1111/biom.13310>; and estimate the treatment effect on a longitudinal surrogate marker as described in Wang et al. (2025) <doi:10.1093/biomtc/ujaf104>. A tutorial for this package can be found at <https://www.laylaparast.com/longsurr>.

The leaflet JavaScript library provides many plugins some of which are available in the core leaflet package, but there are many more. It is not possible to support them all in the core leaflet package. This package serves as an add-on to the leaflet package by providing extra functionality via leaflet plugins.

In the fashion of node.js <https://nodejs.org/>, requires a file, sourcing into the current environment only the variables explicitly specified in the module.exports or exports list variable. If the file was already sourced, the result of the earlier sourcing is returned to the caller.