This package provides a fast calculation of the Blyth-Still-Casella confidence interval. The implementation follows the StatXact 9 manual (Cytel 2010) and "Refining Binomial Confidence Intervals" by George Casella (1986) <doi:10.2307/3314658>.

We provide a toolbox to fit and simulate a univariate or multivariate damped random walk process that is also known as an Ornstein-Uhlenbeck process or a continuous-time autoregressive model of the first order, i.e., CAR(1) or CARMA(1, 0). This process is suitable for analyzing univariate or multivariate time series data with irregularly-spaced observation times and heteroscedastic measurement errors. When it comes to the multivariate case, the number of data points (measurements/observations) available at each observation time does not need to be the same, and the length of each time series can vary. The number of time series data sets that can be modeled simultaneously is limited to ten in this version of the package. We use Kalman-filtering to evaluate the resulting likelihood function, which leads to a scalable and efficient computation in finding maximum likelihood estimates of the model parameters or in drawing their posterior samples. Please pay attention to loading the data if this package is used for astronomical data analyses; see the details in the manual. Also see Hu and Tak (2020) <arXiv:2005.08049>.

Interface to libKriging C++ library <https://github.com/libKriging> that should provide most standard Kriging / Gaussian process regression features (like in DiceKriging', kergp or RobustGaSP packages). libKriging relies on Armadillo linear algebra library (Apache 2 license) by Conrad Sanderson, lbfgsb_cpp is a C++ port around by Pascal Have of lbfgsb library (BSD-3 license) by Ciyou Zhu, Richard Byrd, Jorge Nocedal and Jose Luis Morales used for hyperparameters optimization.

An interface to the Integrated Taxonomic Information System ('ITIS') (<https://www.itis.gov>). Includes functions to work with the ITIS REST API methods (<https://www.itis.gov/ws_description.html>), as well as the Solr web service (<https://www.itis.gov/solr_documentation.html>).

This package provides a tool to conquer the difficulties to convert various region names and administration division codes of Chinese regions. The current version enables seamlessly converting Chinese regions formal names, common-used names, and codes between each other at the city level from 1986 to 2019.

The goal of the readelan is to provide a simple way to read data and metadata in files created with the annotation software ELAN <https://archive.mpi.nl/tla/elan> into R as data frames.

The Rearrangement Correlation Coefficient is an adjusted version of Pearson's correlation coefficient that accurately measures monotonic dependence relationships, including both linear and nonlinear associations. This method addresses the underestimation problem of classical correlation coefficients in nonlinear monotonic scenarios through improved statistical bounds derived from rearrangement inequalities. For more details, see Ai (2024) <doi:10.52202/079017-1180>.

Flexible rounding functions for use in error detection. They were outsourced from the scrutiny package.

The expander functions rely on the mathematics developed for the Hessian-definiteness invariance theorem for linear projection transformations of variables, described in authors paper, to generate the full, high-dimensional gradient and Hessian from the lower-dimensional derivative objects. This greatly relieves the computational burden of generating the regression-function derivatives, which in turn can be fed into any optimization routine that utilizes such derivatives. The theorem guarantees that Hessian definiteness is preserved, meaning that reasoning about this property can be performed in the low-dimensional space of the base distribution. This is often a much easier task than its equivalent in the full, high-dimensional space. Definiteness of Hessian can be useful in selecting optimization/sampling algorithms such as Newton-Raphson optimization or its sampling equivalent, the Stochastic Newton Sampler. Finally, in addition to being a computational tool, the regression expansion framework is of conceptual value by offering new opportunities to generate novel regression problems.

This package provides a wrapper for Jagger, a morphological analyzer proposed in Yoshinaga (2023) <arXiv:2305.19045>. Jagger uses patterns derived from morphological dictionaries and training data sets and applies them from the beginning of the input. This simultaneous and deterministic process enables it to effectively perform tokenization, POS tagging, and lemmatization.

Search R files for not installed packages and run install.packages.

Collection of tools to calculate portfolio performance metrics. Portfolio performance is a key measure for investors. These metrics are important to analyse how effectively their money has been invested. This package uses portfolio theories to give investor tools to evaluate their portfolio performance. For more information see, Markowitz, H.M. (1952), <doi:10.2307/2975974>. Analysis of Investments & Management of Portfolios [2012, ISBN:978-8131518748].

Modified Poisson, logistic and least-squares regression analyses for binary outcomes of Zou (2004) <doi:10.1093/aje/kwh090>, Noma (2026)<doi:10.1016/j.spl.2026.110698>, and Cheung (2007) <doi:10.1093/aje/kwm223> have been standard multivariate analysis methods to estimate risk ratio and risk difference in clinical and epidemiological studies. This R package involves an easy-to-handle function to implement these analyses by simple commands. Missing data analysis tools (multiple imputation) are also involved. In addition, recent studies have shown the ordinary robust variance estimator possibly has serious bias under small or moderate sample size situations for these methods. This package also provides computational tools to calculate alternative accurate confidence intervals.

Fits non-linear regression models on dependant data with Generalised Least Square (GLS) based Random Forest (RF-GLS) detailed in Saha, Basu and Datta (2021) <doi:10.1080/01621459.2021.1950003>.

Regression methods to quantify the relation between two measurement methods are provided by this package. The focus is on a Bayesian Deming regressions family. With a Bayesian method the Deming regression can be run in a traditional fashion or can be run in a robust way just decreasing the degree of freedom d.f. of the sampling distribution. With d.f. = 1 an extremely robust Cauchy distribution can be sampled. Moreover, models for dealing with heteroscedastic data are also provided. For reference see G. Pioda (2024) <https://piodag.github.io/bd1/>.

Interface to SWI'-'Prolog', <https://www.swi-prolog.org/>. This package is normally not loaded directly, please refer to package rolog instead. The purpose of this package is to provide the Prolog runtime on systems that do not have a software installation of SWI'-'Prolog'.

Clinical care data from 130 U.S. hospitals in the years 1999-2008 adapted from the study Strack et al. (2014) <doi:10.1155/2014/781670>. Each row describes an "encounter" with a patient with diabetes, including variables on demographics, medications, patient history, diagnostics, payment, and readmission.

This package provides a set of functions for receiver operating characteristic (ROC) curve estimation and area under the curve (AUC) calculation. All functions are designed to work with aggregated data; nevertheless, they can also handle raw samples. In ROCket', we distinguish two types of ROC curve representations: 1) parametric curves - the true positive rate (TPR) and the false positive rate (FPR) are functions of a parameter (the score), 2) functions - TPR is a function of FPR. There are several ROC curve estimation methods available. An introduction to the mathematical background of the implemented methods (and much more) can be found in de Zea Bermudez, Gonçalves, Oliveira & Subtil (2014) and Cai & Pepe (2004).

Set of functions that enable you to use the FUSION commands (Program available in: <http://forsys.sefs.uw.edu/fusion/fusionlatest.html>).

Hydrologic modelling system is an object oriented tool for simulation and analysis of hydrologic events. The package proposes functions and methods for construction, simulation, visualization, and calibration of a hydrologic model.

This package provides tools for grading the coding style and documentation of R scripts. This is the R component of Roger the Omni Grader, an automated grading system for computer programming projects based on Unix shell scripts; see <https://gitlab.com/roger-project>. The package also provides an R interface to the shell scripts. Inspired by the lintr package.

This package provides methods to calculate approximate regional consistency probabilities using Method 1 and Method 2 proposed by the Japanese Ministry of Health, Labor and Welfare (2007) <https://www.pmda.go.jp/files/000153265.pdf>. These methods are useful for assessing regional consistency in multi-regional clinical trials. The package can calculate unconditional, joint, and conditional regional consistency probabilities. For technical details, please see Homma (2024) <doi:10.1002/pst.2358>.

Enables binary package installations on Linux distributions. Provides access to RStudio public repositories at <https://packagemanager.posit.co>, and transparent management of system requirements without administrative privileges. Currently supported distributions are CentOS / RHEL', and several RHEL derivatives ('Rocky Linux', AlmaLinux', Oracle Linux', and Amazon Linux'), openSUSE / SLES', Debian', and Ubuntu LTS.

Drift-Diffusion Model (DDM) has been widely used to model binary decision-making tasks, and many research studies the relationship between DDM parameters and other characteristics of the subject. This package uses RStan to perform generalized liner regression analysis over DDM parameters via a single Bayesian Hierarchical model. Compared to estimating DDM parameters followed by a separate regression model, RegDDM reduces bias and improves statistical power.