This package provides a plain Rcpp wrapper for MeCab that can segment Chinese, Japanese, and Korean text into tokens. The main goal of this package is to provide an alternative to tidytext using morphological analysis.

This package provides utility functions for, and drawing on, the data.table package. The package also collates useful miscellaneous functions extending base R not available elsewhere. The name is a portmanteau of utils and the author.

Uses the Jaccard similarity index to account for population structure in sequencing studies. This method was specifically designed to detect population stratification based on rare variants, hence it will be especially useful in rare variant analysis.

This package provides tools to use API such as e-Stat (<https://www.e-stat.go.jp/>), the portal site for Japanese government statistics, and RESAS (Regional Economy and Society Analyzing System, <https://resas.go.jp>).

This package provides a new practical method to evaluate whether relationships between two sets of high-dimensional variables are different or not across two conditions. Song, H. and Wu, M.C. (2023) <arXiv:2307.15268>.

This package provides a set of functions, classes and methods for performing ABC and ABC/XYZ analyses, identifying overperforming, underperforming and constantly performing items, and plotting, analyzing as well as predicting the temporal development of items.

Co-Expression Network Analysis by adopting network embedding technique. Song W.-M., Zhang B. (2015) Multiscale Embedded Gene Co-expression Network Analysis. PLoS Comput Biol 11(11): e1004574. <doi: 10.1371/journal.pcbi.1004574>.

Package computes popular and widely used multicollinearity diagnostic measures \doi10.17576/jsm-2019-4809-26 and \doi10.32614/RJ-2016-062. Package also indicates which regressors may be the reason of collinearity among regressors.

User-friendly Shiny apps for designing and evaluating phase I cancer clinical trials, with the aim to estimate the maximum tolerated dose (MTD) of a novel drug, using a Bayesian decision procedure based on logistic regression.

This package provides a fast, flexible machine learning library, written in C++, that aims to provide fast, extensible implementations of cutting-edge machine learning algorithms. See also Curtin et al. (2023) <doi:10.21105/joss.05026>.

Obtain and evaluate various optimal designs for the 3, 4, and 5-parameter logistic models. The optimal designs are obtained based on the numerical algorithm in Hyun, Wong, Yang (2018) <doi:10.18637/jss.v083.i05>.

Simulation of continuous, correlated high-dimensional data with time to event or binary response, and parallelized functions for Lasso, Ridge, and Elastic Net penalized regression with repeated starts and two-dimensional tuning of the Elastic Net.

This package provides tools to show and draw image pixels using HTML widgets and Shiny applications. It can be used to visualize the MNIST dataset for handwritten digit recognition or to create new image recognition datasets.

Implementation of assumption-lean and data-adaptive post-prediction inference (POPInf), for valid and efficient statistical inference based on data predicted by machine learning. See Miao, Miao, Wu, Zhao, and Lu (2023) <arXiv:2311.14220>.

This package provides a simple interface to developing complex data pipelines which can be executed in a single call. sewage makes it easy to test, debug, and share data pipelines through it's interface and visualizations.

This package provides an easy framework for Monte Carlo simulation in structural equation modeling, which can be used for various purposes, such as such as model fit evaluation, power analysis, or missing data handling and planning.

This package provides methods for fitting semi-parametric mean and variance models, with normal or censored data. Extended to allow a regression in the location, scale and shape parameters, and further for multiple regression in each.

Miscellaneous functions for data analysis, portfolio management, graphics, data manipulation, statistical investigation, including descriptive statistics, creating leading and lagging variables, portfolio return analysis, time series difference and percentage change calculation, stacking data for higher efficient analysis.

The open sourced data management software Integrated Rule-Oriented Data System ('iRODS') offers solutions for the whole data life cycle (<https://irods.org/>). The loosely constructed and highly configurable architecture of iRODS frees the user from strict formatting constraints and single-vendor solutions. This package provides an interface to the iRODS HTTP API, allowing you to manage your data and metadata in iRODS with R. Storage of annotated files and R objects in iRODS ensures findability, accessibility, interoperability, and reusability of data.

Seamless extraction of river networks from digital elevation models data. The package allows analysis of digital elevation models that can be either externally provided or downloaded from open source repositories (thus interfacing with the elevatr package). Extraction is performed via the D8 flow direction algorithm of TauDEM (Terrain Analysis Using Digital Elevation Models), thus interfacing with the traudem package. Resulting river networks are compatible with functions from the OCNet package. See Carraro (2023) <doi:10.5194/hess-27-3733-2023> for a presentation of the package.

This package provides an R wrapper of OpenAI API endpoints (see https://platform.openai.com/docs/introduction for details). This package covers Models, Completions, Chat, Edits, Images, Embeddings, Audio, Files, Fine-tunes, Moderations, and legacy Engines endpoints.

This package provides a toolset for Geometric Morphometrics and mesh processing. This includes (among other stuff) mesh deformations based on reference points, permutation tests, detection of outliers, processing of sliding semi-landmarks and semi-automated surface landmark placement.

This is a package for maximum likelihood estimation of random utility discrete choice models. The software is described in Croissant (2020) <doi:10.18637/jss.v095.i11> and the underlying methods in Train (2009) <doi:10.1017/CBO9780511805271>.

This package provides tools to estimate parameters of accumulated damage (load duration) models based on failure time data under a Bayesian framework, using Approximate Bayesian Computation (ABC), and to assess long-term reliability under stochastic load profiles.