Extra Features for 'reactable' Package

Enhanced functionality for 'reactable' in 'shiny' applications, offering interactive and dynamic data table capabilities with ease. With 'reactable.extras', easily integrate a range of functions and components to enrich your 'shiny' apps and facilitate user-friendly data exploration.

Acceptance-Rejection Method for Generating Pseudo-Random Observations

Provides a function that implements the acceptance-rejection method in an optimized manner to generate pseudo-random observations for discrete or continuous random variables. Proposed by von Neumann J. (1951), < https://mcnp.lanl.gov/pdf_files/>, the function is optimized to work in parallel on Unix-based operating systems and performs well on Windows systems. The acceptance-rejection method implemented optimizes the probability of generating observations from the desired random variable, by simply providing the probability function or probability density function, in the discrete and continuous cases, respectively. Implementation is based on references CASELLA, George at al. (2004) < https://www.jstor.org/stable/4356322>, NEAL, Radford M. (2003) < https://www.jstor.org/stable/3448413> and Bishop, Christopher M. (2006, ISBN: 978-0387310732).

Cox MultiBlock Survival

This software package provides Cox survival analysis for high-dimensional and multiblock datasets. It encompasses a suite of functions dedicated from the classical Cox regression to newest analysis, including Cox proportional hazards model, Stepwise Cox regression, and Elastic-Net Cox regression, Sparse Partial Least Squares Cox regression (sPLS-COX) incorporating three distinct strategies, and two Multiblock-PLS Cox regression (MB-sPLS-COX) methods. This tool is designed to adeptly handle high-dimensional data, and provides tools for cross-validation, plot generation, and additional resources for interpreting results. While references are available within the corresponding functions, key literature is mentioned below. Terry M Therneau (2024) < https://CRAN.R-project.org/package=survival>, Noah Simon et al. (2011) , Philippe Bastien et al. (2005) , Philippe Bastien (2008) , Philippe Bastien et al. (2014) , Kassu Mehari Beyene and Anouar El Ghouch (2020) , Florian Rohart et al. (2017) .

Regression Methods for Interval-Valued Variables

Contains some important regression methods for interval-valued variables. For each method, it is available the fitted values, residuals and some goodness-of-fit measures.

IUCN Redlisting Tools

Includes algorithms to facilitate the assessment of extinction risk of species according to the IUCN (International Union for Conservation of Nature, see < https://iucn.org/> for more information) red list criteria.

Data Sets for 'specmine'

Provides the data sets used to exemplify 'specmine'. These data sets were formerly distributed with 'specmine', but they exceed current CRAN policy for package size.

Metabolomics and Spectral Data Analysis and Mining

Provides methods for metabolomics and spectral data analysis, including data import, preprocessing, visualization, univariate and multivariate analysis, machine learning, feature selection, and pathway analysis. The package supports analytical workflows for different data types used in metabolomics and spectroscopy. Some optional functionality uses the suggested packages 'cyjShiny' and 'specmine.datasets'. The package 'specmine.datasets' is maintained separately at < https://github.com/PedroFontao/specmine.datasets>.

Simulate Pedagogical Statistical Data

Univariate and multivariate normal data simulation. They also supply a brief summary of the analysis for each experiment/design: - Independent samples. - One-way and two-way Anova. - Paired samples (T-Test & Regression). - Repeated measures (Anova & Multiple Regression). - Clinical Assay.

Probabilistic Streaming Data Sketches

Provides an interface to the 'Apache DataSketches' (< https://datasketches.apache.org/>) library of streaming algorithms for approximate analytics on data too large to hold or process exactly. Sketches are compact, mergeable summaries built in a single pass over a stream that answer queries such as approximate distinct counts, quantiles and ranks, frequent items and point-frequency estimates, weighted sampling, and set membership with mathematically proven error bounds. Implements Karnin-Lang-Liberty (KLL), Relative Error Quantiles (REQ), t-Digest, HyperLogLog (HLL), Compressed Probabilistic Counting (CPC), Theta, Frequent Items, Count-Min, Array of Doubles, Variance Optimal (VarOpt), Exact and Bounded Probabilistic Proportional-to-Size (EBPPS), and Bloom filter sketches, with native serialization for interoperability with other 'Apache DataSketches' implementations.

Heteroskedasticity-Consistent Inference for Linear Models

Computes heteroskedasticity-consistent covariance matrix estimators for ordinary least squares regression models. The published HC0 through HC5m estimators implemented in the package follow White (1980) , Hinkley (1977) , MacKinnon and White (1985) , Cribari-Neto (2004) , Cribari-Neto and da Silva (2011) , Cribari-Neto et al. (2007) , and Li et al. (2016) . The package also includes HCbeta, a new estimator proposed by the package authors. It provides normal Wald tests, confidence intervals, diagnostics, and S3 output for applied inference.