The AcceptReject package, available for the R programming language on the Comprehensive R Archive Network (CRAN), versioned and maintained on GitHub, offers a simple and efficient solution for generating pseudo-random observations of discrete or continuous random variables using the acceptance-rejection method. This method provides a viable alternative for generating pseudo-random observations in univariate distributions when the inverse of the cumulative distribution function is not in closed form or when suitable transformations involving random variables that we know how to generate are unknown, thereby facilitating the generation of observations for the variable of interest. The package is designed to be simple, intuitive, and efficient, allowing for the rapid generation of observations and supporting multicore parallelism on Unix-based operating systems. Some components are written using C++, and the package maximizes the acceptance probability of the generated observations, resulting in even more efficient execution. The package also allows users to explore the generated pseudo-random observations by comparing them with the theoretical probability mass function or probability density function and to inspect the underlying probability density functions that can be used in the method for generating observations of continuous random variables. This article explores the package in detail, discussing its functionalities, benefits, and practical applications, and provides various benchmarks in several scenarios.

The m-out-of-n bootstrap is a possible workaround to compute confidence intervals for bootstrap inconsistent estimators, because it works under weaker conditions than the n-out-of-n bootstrap. It has the disadvantage, however, that it requires knowledge of an appropriate scaling factor tau(n) and that the coverage probability for finite n depends on the choice of m. This article presents an R package moonboot which implements the computation of m-out-of-n bootstrap confidence intervals and provides functions for estimating the parameters tau(n) and m. By means of Monte Carlo simulations, we evaluate the different methods and compare them for different estimators.

Bayesian optimal interval based on both efficacy and toxicity outcomes (BOIN-ET) design is a model-assisted oncology phase I/II trial design, aiming to establish an optimal biological dose accounting for efficacy and toxicity in the framework of dose-finding. Some extensions of BOIN-ET design are also available to allow for time-to-event efficacy and toxicity outcomes based on cumulative and pending data (time-to-event BOIN-ET: TITE-BOIN-ET), ordinal graded efficacy and toxicity outcomes (generalized BOIN-ET: gBOIN-ET), and their combination (TITE-gBOIN-ET). The R package boinet implements the BOIN-ET design family. The package supports the conduct of simulation studies to assess operating characteristics of BOIN-ET, TITE-BOIN-ET, gBOIN-ET, and TITE-gBOIN-ET designs, where users can choose design parameters in flexible and straightforward ways depending on their own application. We demonstrate its capability and effectiveness using several simulation studies.

This paper presents an estimation and simulation method in the R package yuima for a linear regression model driven by a Student-t Lévy process with constant scale and arbitrary degrees of freedom. This process finds applications in several fields, for example finance, physics, biology, etc. The first challenge involves simulating sample paths at high-frequency levels, as only unit-time increments are Student-t distributed. In yuima, we solve this problem by means of the inverse Fourier transform for simulating the increments of a Student-t Lévy defined on an interval with any length. The second challenge is the joint estimation of trend, scale, and degrees of freedom, a problem not previously explored in the literature. In yuima, we develop a two-step estimation procedure that efficiently deals with this issue. Numerical examples are given in order to explain methods and classes used in the yuima package.

Item response theory (IRT) models the relationship between respondents' latent traits and their responses to specific items. One key aspect of IRT is the assumption about the distribution of the latent variables, which can influence parameter estimation accuracy. While a normal distribution has been conventionally assumed, this may not always be appropriate. When the assumption of normality is violated, latent distribution estimation (LDE) can enhance parameter estimation accuracy by accommodating non-normal characteristics. Despite there being several methods proposed for LDE in IRT, there is a lack of software designed to handle their implementations. This paper introduces IRTest, a software program developed for IRT analysis that incorporates LDE procedures. It outlines the statistical foundation of LDE, details the functionalities of IRTest, and provides examples of IRT analyses to demonstrate the software's applications.

The package MultiStatM presents a vectorial approach to multivariate moments and cumulants. Functions provided concern algorithms to build set partitions and commutator matrices, multivariate d-Hermite polynomials; theoretical vector moments and vector cumulants of multivariate distributions and their conversion formulae. Applications discussed concern multivariate measures of skewness and kurtosis; asymptotic covariances for d-variate Hermite polynomials and multivariate moments and cumulants; Gram-Charlier approximations.

Classic Canonical Correlation Analysis (CCA) is a popular statistical method that allows for the analysis of the associations between two sets of variables. However, currently it cannot be applied following the published methodological documentation to data sets collected using complex survey design (CSD), which includes factors, such as replicate weights, clusters, and strata, that are critical for the accurate calculation of the statistical significance of any correlation. To close this gap, we have developed the Survey CC algorithm and implemented it in an R package. We describe the theoretical foundations of our algorithm and provide a detailed report of the options of the function that performs it. Moreover, the application of our newly developed method to several national survey data sets shows the differences in conclusions that can be reached if the CSD elements are not taken into consideration during the calculation of the statistical significance of the canonical correlations.

Chemical data is a cornerstone in the fields of chemistry, pharmacology, bioinformatics, and environmental science. The PubChemR package provides a comprehensive R interface to the PubChem database, which is one of the largest and most complete repositories of chemical data. This package simplifies the process of querying and retrieving chemical information, including compound structures, properties, biological activities, and more, directly from within R. By leveraging PubChemR, users can programmatically access a wealth of chemical data, which is essential for research and analysis in the chemical sciences. The package supports various functionalities such as searching by chemical identifiers, downloading chemical structures, and retrieving bioassay results, among others. PubChemR is designed to be user-friendly, providing a intuitive experience for R users ranging from academic researchers to practitioners across various scientific disciplines. This paper presents the capabilities of PubChemR, demonstrates its use through practical examples, and discusses its potential impact on chemical data analysis.