External jars required for package RMOA. RMOA is a framework to build data stream models on top of MOA (Massive Online Analysis - <https://moa.cms.waikato.ac.nz/>). The jar files are put in this R package, the modelling logic can be found in the RMOA package.

The mixed integer programming library MIPLIB (see <http://miplib.zib.de/>) is commonly used to compare the performance of mixed integer optimization solvers. This package provides functions to access MIPLIB from the R Optimization Infrastructure ('ROI'). More information about MIPLIB can be found in the paper by Koch et al. available at <http://mpc.zib.de/index.php/MPC/article/viewFile/56/28>. The README.md file illustrates how to use this package.

This package contains functions to interface with variables and variable details sheets, including recoding variables and converting them to PMML.

The Reproducible Open Coding Kit ('ROCK', and this package, rock') was developed to facilitate reproducible and open coding, specifically geared towards qualitative research methods. It was developed to be both human- and machine-readable, in the spirit of MarkDown and YAML'. The idea is that this makes it relatively easy to write other functions and packages to process ROCK files. The rock package contains functions for basic coding and analysis, such as collecting and showing coded fragments and prettifying sources, as well as a number of advanced analyses such as the Qualitative Network Approach and Qualitative/Unified Exploration of State Transitions. The ROCK and this rock package are described in the ROCK book (ZörgÅ & Peters, 2022; <https://rockbook.org>), in ZörgÅ & Peters (2024) <doi:10.1080/21642850.2022.2119144> and Peters, ZörgÅ and van der Maas (2022) <doi:10.31234/osf.io/cvf52>, and more information and tutorials are available at <https://rock.science>.

The R Analytic Tool To Learn Easily (Rattle) provides a collection of utilities functions for the data scientist. This package (v5.6.0) supports the companion graphical interface with the aim to provide a simple and intuitive introduction to R for data science, allowing a user to quickly load data from a CSV file transform and explore the data, and to build and evaluate models. A key aspect of the GUI is that all R commands are logged and commented through the log tab. This can be saved as a standalone R script file and as an aid for the user to learn R or to copy-and-paste directly into R itself. If you want to use the older Rattle implementing the GUI in RGtk2 (which is no longer available from CRAN) then please install the Rattle package v5.5.1. See rattle.togaware.com for instructions on installing the modern Rattle graphical user interface.

Feasible multivariate GARCH models including DCC, GO-GARCH and Copula-GARCH.

Fits linear models to repeated ordinal scores using GEE methodology.

When creating a package, authors may sometimes struggle with coming up with easy and straightforward function names, and at the same time hoping that other packages do not already have the same function names. In trying to meet this goal, sometimes, function names are not descriptive enough and may confuse the potential users. The purpose of this package is to serve as a package function short form generator and also provide shorthand names for other functions. Having this package will entice authors to create long function names without the fear of users not wanting to use their packages because of the long names. In a way, everyone wins - the authors can use long descriptive function names, and the users can use this package to make short functions names while still using the package in question.

Rcpp reimplementation of the the Bayesian non-parametric Dirichlet Process Regression model for penalized regression first published in Zeng and Zhou (2017) <doi:10.1038/s41467-017-00470-2>. A full Bayesian version is implemented with Gibbs sampling, as well as a faster but less accurate variational Bayes approximation.

An Eigen'-based computationally efficient C++ implementation for fitting various kriging models to data. This research is supported by U.S. National Science Foundation grant DMS-2310637.

Generate basic charts either by custom applications, or from a small script launched from the system console, or within the R console. Two ASCII text files are necessary: (1) The graph parameters file, which name is passed to the function rplotengine()'. The user can specify the titles, choose the type of the graph, graph output formats (e.g. png, eps), proportion of the X-axis and Y-axis, position of the legend, whether to show or not a grid at the background, etc. (2) The data to be plotted, which name is specified as a parameter ('data_filename') in the previous file. This data file has a tabulated format, with a single character (e.g. tab) between each column. Optionally, the file could include data columns for showing confidence intervals.

This package provides tools to perform random forest consensus clustering of different data types. The package is designed to accept a list of matrices from different assays, typically from high-throughput molecular profiling so that class discovery may be jointly performed. For references, please see Tao Shi & Steve Horvath (2006) <doi:10.1198/106186006X94072> & Monti et al (2003) <doi:10.1023/A:1023949509487> .

Implementation of the Integrated Simple Weighted Sum Product Method (WISP), a multiple criteria sorting method create by Dragisa Stanujkic (2021) <doi:10.1109/TEM.2021.3075783>.

This package performs one-sample t-test based on robustified statistics using median/MAD (TA) and Hodges-Lehmann/Shamos (TB). For more details, see Park and Wang (2018)<arXiv:1807.02215>. This work was partially supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (No. NRF-2017R1A2B4004169).

The aim of this package is to manipulate relational data models in R. It provides functions to create, modify and export data models in json format. It also allows importing models created with MySQL Workbench (<https://www.mysql.com/products/workbench/>). These functions are accessible through a graphical user interface made with shiny'. Constraints such as types, keys, uniqueness and mandatory fields are automatically checked and corrected when editing a model. Finally, real data can be confronted to a model to check their compatibility.

We implement a test of the rational expectations hypothesis based on the marginal distributions of realizations and subjective beliefs from D'Haultfoeuille, Gaillac, and Maurel (2018) <doi:10.3386/w25274>. This test can be used in cases where realizations and subjective beliefs are observed in two different datasets that cannot be matched, or when they are observed in the same dataset. The package also computes the estimator of the minimal deviations from rational expectations than can be rationalized by the data.

Yandex Translate (https://translate.yandex.com/) is a statistical machine translation system. The system translates separate words, complete texts, and webpages. This package can be used to detect language from text and to translate it to supported target language. For more info: https://tech.yandex.com/translate/doc/dg/concepts/About-docpage/ .

This is an extension of the regression-based causal mediation analysis first proposed by Valeri and VanderWeele (2013) <doi:10.1037/a0031034> and Valeri and VanderWeele (2015) <doi:10.1097/EDE.0000000000000253>). It supports including effect measure modification by covariates(treatment-covariate and mediator-covariate product terms in mediator and outcome regression models) as proposed by Li et al (2023) <doi:10.1097/EDE.0000000000001643>. It also accommodates the original SAS macro and PROC CAUSALMED procedure in SAS when there is no effect measure modification. Linear and logistic models are supported for the mediator model. Linear, logistic, loglinear, Poisson, negative binomial, Cox, and accelerated failure time (exponential and Weibull) models are supported for the outcome model.

This package provides access to global river gauge data from a variety of national-level river agencies. The package interfaces with the national-level agency websites to provide access to river gauge locations, river discharge, and river stage. Currently, the package is available for the following countries: Australia, Brazil, Canada, Chile, France, Japan, South Africa, the United Kingdom, and the United States.

Summarize model output using a robust effect size index. The index is introduced in Vandekar, Tao, & Blume (2020, <doi:10.1007/s11336-020-09698-2>). Software paper available at <doi:10.18637/jss.v112.i03>.

This package implements a series of robust Kalman filtering approaches. It implements the additive outlier robust filters of Ruckdeschel et al. (2014) <arXiv:1204.3358> and Agamennoni et al. (2018) <doi:10.1109/ICRA.2011.5979605>, the innovative outlier robust filter of Ruckdeschel et al. (2014) <arXiv:1204.3358>, as well as the innovative and additive outlier robust filter of Fisch et al. (2020) <arXiv:2007.03238>.

Implementation of Nelson rules for control charts in R'. The Rspc implements some Statistical Process Control methods, namely Levey-Jennings type of I (individuals) chart, Shewhart C (count) chart and Nelson rules (as described in Montgomery, D. C. (2013) Introduction to statistical quality control. Hoboken, NJ: Wiley.). Typical workflow is taking the time series, specify the control limits, and list of Nelson rules you want to evaluate. There are several options how to modify the rules (one sided limits, numerical parameters of rules, etc.). Package is also capable of calculating the control limits from the data (so far only for i-chart and c-chart are implemented).

This package implements the "Stemming Algorithm for the Portuguese Language" <DOI:10.1109/SPIRE.2001.10024>.

Analysis of corneal data obtained from a Placido disk corneal topographer with calculation of irregularity indices. This package performs analyses of corneal data obtained from a Placido disk corneal topographer, with the calculation of the Placido irregularity indices and the posterior analysis. The package is intended to be easy to use by a practitioner, providing a simple interface and yielding easily interpretable results. A corneal topographer is an ophthalmic clinical device that obtains measurements in the cornea (the anterior part of the eye). A Placido disk corneal topographer makes use of the Placido disk [Rowsey et al. (1981)]<doi:10.1001/archopht.1981.03930011093022>, which produce a circular pattern of measurement nodes. The raw information measured by such a topographer is used by practitioners to analyze curvatures, to study optical aberrations, or to diagnose specific conditions of the eye (e.g. keratoconus, an important corneal disease). The rPACI package allows the calculation of the corneal irregularity indices described in [Castro-Luna et al. (2020)]<doi:10.1016%2Fj.clae.2019.12.006>, [Ramos-Lopez et al. (2013)]<doi:10.1097%2FOPX.0b013e3182843f2a>, and [Ramos-Lopez et al. (2011)]<doi:10.1097/opx.0b013e3182279ff8>. It provides a simple interface to read corneal topography data files as exported by a typical Placido disk topographer, to compute the irregularity indices mentioned before, and to display summary plots that are easy to interpret for a clinician.