Tool for quantitative research in scientometrics and bibliometrics. It implements the comprehensive workflow for science mapping analysis proposed in Aria M. and Cuccurullo C. (2017) <doi:10.1016/j.joi.2017.08.007>. bibliometrix provides various routines for importing bibliographic data from SCOPUS', Clarivate Analytics Web of Science (<https://www.webofknowledge.com/>), Digital Science Dimensions (<https://www.dimensions.ai/>), OpenAlex (<https://openalex.org/>), Cochrane Library (<https://www.cochranelibrary.com/>), Lens (<https://lens.org>), and PubMed (<https://pubmed.ncbi.nlm.nih.gov/>) databases, performing bibliometric analysis and building networks for co-citation, coupling, scientific collaboration and co-word analysis.

Query information and generate badge for using in README and GitHub Pages.

State-of-the art algorithms for learning discrete Bayesian network classifiers from data, including a number of those described in Bielza & Larranaga (2014) <doi:10.1145/2576868>, with functions for prediction, model evaluation and inspection.

This package provides an approach which is based on the methodology of the Burden of Communicable Diseases in Europe (BCoDE) and can be used for large and small samples such as individual countries. The Burden of Healthcare-Associated Infections (BHAI) is estimated in disability-adjusted life years, number of infections as well as number of deaths per year. Results can be visualized with various plotting functions and exported into tables.

Analyze and plot the abundance of different RNA biotypes present in a count matrix, this evaluation can be useful if you want to test different strategies of normalization or analyze a particular biotype in a differential gene expression analysis.

Jointly models the multivariate longitudinal responses and multiple covariates and time using gradient boosting approach.

Generates nonparametric bootstrap confidence intervals (Efron and Tibshirani, 1993: <doi:10.1201/9780429246593>) for standardized regression coefficients (beta) and other effect sizes, including multiple correlation, semipartial correlations, improvement in R-squared, squared partial correlations, and differences in standardized regression coefficients, for models fitted by lm().

Build and use B-splines for interpolation and regression. In case of regression, equality constraints as well as monotonicity and/or positivity of B-spline weights can be imposed. Moreover, knot positions can be on regular grid or be part of optimized parameters too (in addition to the spline weights). For this end, bspline is able to calculate Jacobian of basis vectors as function of knot positions. User is provided with functions calculating spline values at arbitrary points. These functions can be differentiated and integrated to obtain B-splines calculating derivatives/integrals at any point. B-splines of this package can simultaneously operate on a series of curves sharing the same set of knots. bspline is written with concern about computing performance that's why the basis and Jacobian calculation is implemented in C++. The rest is implemented in R but without notable impact on computing speed.

This package provides a two-step Bayesian approach for mode inference following Cross, Hoogerheide, Labonne and van Dijk (2024) <doi:10.1016/j.econlet.2024.111579>). First, a mixture distribution is fitted on the data using a sparse finite mixture (SFM) Markov chain Monte Carlo (MCMC) algorithm. The number of mixture components does not have to be known; the size of the mixture is estimated endogenously through the SFM approach. Second, the modes of the estimated mixture at each MCMC draw are retrieved using algorithms specifically tailored for mode detection. These estimates are then used to construct posterior probabilities for the number of modes, their locations and uncertainties, providing a powerful tool for mode inference.

Estimates survival and mortality with covariates from census or capture-recapture/recovery data in a Bayesian framework when many individuals are of unknown age. It includes tools for data checking, model diagnostics and outputs such as life-tables and plots, as described in Colchero, Jones, and Rebke (2012) <doi:10.1111/j.2041-210X.2012.00186.x> and Colchero et al. (2021) <doi:10.1038/s41467-021-23894-3>.

Calculates several entropy metrics for spatial data inspired by Boltzmann's entropy formula. It includes metrics introduced by Cushman for landscape mosaics (Cushman (2015) <doi:10.1007/s10980-015-0305-2>), and landscape gradients and point patterns (Cushman (2021) <doi:10.3390/e23121616>); by Zhao and Zhang for landscape mosaics (Zhao and Zhang (2019) <doi:10.1007/s10980-019-00876-x>); and by Gao et al. for landscape gradients (Gao et al. (2018) <doi:10.1111/tgis.12315>; Gao and Li (2019) <doi:10.1007/s10980-019-00854-3>).

Read and process brand.yml YAML files. brand.yml is a simple, portable YAML file that codifies your company's brand guidelines into a format that can be used by Quarto', Shiny and R tooling to create branded outputs. Maintain unified, branded theming for web applications to printed reports to dashboards and presentations with a consistent look and feel.

This package provides a Bayesian model averaging approach to causal effect estimation based on the BCEE algorithm. Currently supports binary or continuous exposures and outcomes. For more details, see Talbot et al. (2015) <doi:10.1515/jci-2014-0035> Talbot and Beaudoin (2022) <doi:10.1515/jci-2021-0023>.

BabyTime is an application for tracking infant and toddler care activities like sleeping, eating, etc. This package will take the outputted .zip files and parse it into a usable list object with cleaned data. It handles malformed and incomplete data gracefully and is designed to parse one directory at a time.

This package provides tools for optimal and approximate state estimation as well as network inference of Partially-Observed Boolean Dynamical Systems.

Calculate the operating characteristics of the Bayesian Optimal Interval with Back Filling Design for dose escalation in early-phase oncology trials.

This package implements the First Fit Decreasing algorithm to achieve one dimensional heuristic bin packing. Runtime is of order O(n log(n)) where n is the number of items to pack. See "The Art of Computer Programming Vol. 1" by Donald E. Knuth (1997, ISBN: 0201896834) for more details.

This package provides a family of novel beta mixture models (BMMs) has been developed by Majumdar et al. (2022) <doi:10.48550/arXiv.2211.01938> to appositely model the beta-valued cytosine-guanine dinucleotide (CpG) sites, to objectively identify methylation state thresholds and to identify the differentially methylated CpG (DMC) sites using a model-based clustering approach. The family of beta mixture models employs different parameter constraints applicable to different study settings. The EM algorithm is used for parameter estimation, with a novel approximation during the M-step providing tractability and ensuring computational feasibility.

This package provides a set of functions for doing analysis of A/B split test data and web metrics in general.

This package provides probability computation, data generation, and model estimation for fully-visible Boltzmann machines. It follows the methods described in Nguyen and Wood (2016a) <doi:10.1162/NECO_a_00813> and Nguyen and Wood (2016b) <doi:10.1109/TNNLS.2015.2425898>.

Bayesian approaches for analyzing multivariate data in ecology. Estimation is performed using Markov Chain Monte Carlo (MCMC) methods via Three. JAGS types of models may be fitted: 1) With explanatory variables only, boral fits independent column Generalized Linear Models (GLMs) to each column of the response matrix; 2) With latent variables only, boral fits a purely latent variable model for model-based unconstrained ordination; 3) With explanatory and latent variables, boral fits correlated column GLMs with latent variables to account for any residual correlation between the columns of the response matrix.

Bayesian approach to multidimensional scaling. The package consists of implementations of the methods of Oh and Raftery (2001) <doi:10.1198/016214501753208690>.

Fits finite mixture models of univariate Gaussian distributions using JAGS within a Bayesian framework.

Build decision trees and random forests for classification and regression. The implementation strikes a balance between minimizing computing efforts and maximizing the expected predictive accuracy, thus scales well to large data sets. Multi-threading is available through OpenMP <https://gcc.gnu.org/wiki/openmp>.