Fits smoothing spline regression models using scalable algorithms designed for large samples. Seven marginal spline types are supported: linear, cubic, different cubic, cubic periodic, cubic thin-plate, ordinal, and nominal. Random effects and parametric effects are also supported. Response can be Gaussian or non-Gaussian: Binomial, Poisson, Gamma, Inverse Gaussian, or Negative Binomial.

Simulation, estimation and forecasting of first-order Beta-Skew-t-EGARCH models with leverage (one-component, two-component, skewed versions).

Compare dissolution profiles with confidence interval of similarity factor f2 using bootstrap methodology as described in the literature, such as Efron and Tibshirani (1993, ISBN:9780412042317), Davison and Hinkley (1997, ISBN:9780521573917), and Shah et al. (1998) <doi:10.1023/A:1011976615750>. The package can also be used to simulate dissolution profiles based on mathematical modelling and multivariate normal distribution.

As heavy-tailed error distribution and outliers in the response variable widely exist, models which are robust to data contamination are highly demanded. Here, we develop a novel robust Bayesian variable selection method with elastic net penalty. In particular, the spike-and-slab priors have been incorporated to impose sparsity. An efficient Gibbs sampler has been developed to facilitate computation.The core modules of the package have been developed in C++ and R.

Semi-supervised and unsupervised Bayesian mixture models that simultaneously infer the cluster/class structure and a batch correction. Densities available are the multivariate normal and the multivariate t. The model sampler is implemented in C++. This package is aimed at analysis of low-dimensional data generated across several batches. See Coleman et al. (2022) <doi:10.1101/2022.01.14.476352> for details of the model.

This package provides a Bayesian data modeling scheme that performs four interconnected tasks: (i) characterizes the uncertainty of the elicited parametric prior; (ii) provides exploratory diagnostic for checking prior-data conflict; (iii) computes the final statistical prior density estimate; and (iv) executes macro- and micro-inference. Primary reference is Mukhopadhyay, S. and Fletcher, D. 2018 paper "Generalized Empirical Bayes via Frequentist Goodness of Fit" (<https://www.nature.com/articles/s41598-018-28130-5 >).

This package provides spatial data for mapping Brunei, including boundaries for districts, mukims, and kampongs, as well as locations of key infrastructure such as masjids, hospitals, clinics, and schools. The package supports researchers, analysts, and developers working with Bruneiâ s geographic and demographic data, offering a quick and accessible foundation for creating maps and conducting spatial studies.

R/C++ implementation of the model proposed by Primiceri ("Time Varying Structural Vector Autoregressions and Monetary Policy", Review of Economic Studies, 2005), with functionality for computing posterior predictive distributions and impulse responses.

Presence-Only data is best modelled with a Point Process Model. The work of Moreira and Gamerman (2022) <doi:10.1214/21-AOAS1569> provides a way to use exact Bayesian inference to model this type of data, which is implemented in this package.

This package provides a GUI with which users can construct and interact with biplots.

Working with reproducible reports or any other similar projects often require to run the script that builds the output file in a specified way. buildr can help you organize, modify and comfortably run those scripts. The package provides a set of functions that interactively guides you through the process and that are available as RStudio Addin, meaning you can set up the keyboard shortcuts, enabling you to choose and run the desired build script with one keystroke anywhere anytime.

High performance principal component analysis routines that operate directly on bigmemory::big.matrix objects. The package avoids materialising large matrices in memory by streaming data through BLAS and LAPACK kernels and provides helpers to derive scores, loadings, correlations, and contribution diagnostics, including utilities that stream results into bigmemory'-backed matrices for file-based workflows. Additional interfaces expose scalable singular value decomposition, robust PCA, and robust SVD algorithms so that users can explore large matrices while tempering the influence of outliers. Scalable principal component analysis is also implemented, Elgamal, Yabandeh, Aboulnaga, Mustafa, and Hefeeda (2015) <doi:10.1145/2723372.2751520>.

This package provides a collection of models for bivariate alternating recurrent event data analysis. Includes non-parametric and semi-parametric methods.

Collection of tools to work with European basketball data. Functions available are related to friendly web scraping, data management and visualization. Data were obtained from <https://www.euroleaguebasketball.net/euroleague/>, <https://www.euroleaguebasketball.net/eurocup/> and <https://www.acb.com/>, following the instructions of their respectives robots.txt files, when available. Box score data are available for the three leagues. Play-by-play and spatial shooting data are also available for the Spanish league. Methods for analysis include a population pyramid, 2D plots, circular plots of players percentiles, plots of players monthly/yearly stats, team heatmaps, team shooting plots, team four factors plots, cross-tables with the results of regular season games, maps of nationalities, combinations of lineups, possessions-related variables, timeouts, performance by periods, personal fouls, offensive rebounds and different types of shooting charts. Please see Vinue (2020) <doi:10.1089/big.2018.0124> and Vinue (2024) <doi:10.1089/big.2023.0177>.

Executes BASIC programs from the 1970s, for historical and educational purposes. This enables famous examples of early machine learning, artificial intelligence, natural language processing, cellular automata, and so on, to be run in their original form.

Bagged OutlierTrees is an explainable unsupervised outlier detection method based on an ensemble implementation of the existing OutlierTree procedure (Cortes, 2020). This implementation takes advantage of bootstrap aggregating (bagging) to improve robustness by reducing the possible masking effect and subsequent high variance (similarly to Isolation Forest), hence the name "Bagged OutlierTrees". To learn more about the base procedure OutlierTree (Cortes, 2020), please refer to <arXiv:2001.00636>.

This package provides a C++ library for Bayesian modeling, with an emphasis on Markov chain Monte Carlo. Although boom contains a few R utilities (mainly plotting functions), its primary purpose is to install the BOOM C++ library on your system so that other packages can link against it.

This package provides functions to implement a Hwang(2021) <doi:10.2139/ssrn.3866876> estimator, which bounds an omitted variable bias using auxiliary data.

Tests the parallel regression assumption wit the brant test by Brant (1990) <doi: 10.2307/2532457> for ordinal logit models generated with the function polr() from the package MASS'.

Algorithms developed for binned data analysis, gene expression data analysis and measurement error models for ordinal data analysis.

This package provides tools for downloading historical financial data from the www.belex.rs.

Instructor-developed tools for Analytics and Quantitative Methods (AQM) courses at Babson College. Included are compact descriptive statistics for data frames and lists, expanded reporting and graphics for linear regressions, and formatted reports for best subsets analyses.

Implementing the Block Coordinate Ascent with One-Step Generalized Rosen (BCA1SG) algorithm on the semiparametric models for panel count data, interval-censored survival data, and degradation data. A comprehensive description of the BCA1SG algorithm can be found in Wang et al. (2020) <https://github.com/yudongstat/BCA1SG/blob/master/BCA1SG.pdf>. For details of the semiparametric models for panel count data, interval-censored survival data, and degradation data, please see Wellner and Zhang (2007) <doi:10.1214/009053607000000181>, Huang and Wellner (1997) <ISBN:978-0-387-94992-5>, and Wang and Xu (2010) <doi:10.1198/TECH.2009.08197>, respectively.

Make Bootstrap 4 Shiny dashboards. Use the full power of AdminLTE3', a dashboard template built on top of Bootstrap 4 <https://github.com/ColorlibHQ/AdminLTE>.