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r-btergm
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Temporal Exponential Random Graph Models (TERGM) estimated by maximum pseudolikelihood with bootstrapped confidence intervals or Markov Chain Monte Carlo maximum likelihood. Goodness of fit assessment for ERGMs, TERGMs, and SAOMs. Micro-level interpretation of ERGMs and TERGMs. The methods are described in Leifeld, Cranmer and Desmarais (2018), JStatSoft <doi:10.18637/jss.v083.i06>.
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2025-04-22 |
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r-btyd
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Functions for data preparation, parameter estimation, scoring, and plotting for the BG/BB (Fader, Hardie, and Shang 2010 <doi:10.1287/mksc.1100.0580>), BG/NBD (Fader, Hardie, and Lee 2005 <doi:10.1287/mksc.1040.0098>) and Pareto/NBD and Gamma/Gamma (Fader, Hardie, and Lee 2005 <doi:10.1509/jmkr.2005.42.4.415>) models.
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2025-04-22 |
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r-bst
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Functional gradient descent algorithm for a variety of convex and non-convex loss functions, for both classical and robust regression and classification problems. See Wang (2011) <doi:10.2202/1557-4679.1304>, Wang (2012) <doi:10.3414/ME11-02-0020>, Wang (2018) <doi:10.1080/10618600.2018.1424635>, Wang (2018) <doi:10.1214/18-EJS1404>.
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2025-04-22 |
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r-bsicons
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Easily use 'Bootstrap' icons inside 'Shiny' apps and 'R Markdown' documents. More generally, icons can be inserted in any 'htmltools' document through inline 'SVG'.
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2025-04-22 |
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r-bshazard
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The function estimates the hazard function non parametrically from a survival object (possibly adjusted for covariates). The smoothed estimate is based on B-splines from the perspective of generalized linear mixed models. Left truncated and right censoring data are allowed.
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2025-04-22 |
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r-brucer
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Broadly useful convenient and efficient R functions that bring users concise and elegant R data analyses. This package includes easy-to-use functions for (1) basic R programming (e.g., set working directory to the path of currently opened file; import/export data from/to files in any format; print tables to Microsoft Word); (2) multivariate computation (e.g., compute scale sums/means/... with reverse scoring); (3) reliability analyses and factor analyses; (4) descriptive statistics and correlation analyses; (5) t-test, multi-factor analysis of variance (ANOVA), simple-effect analysis, and post-hoc multiple comparison; (6) tidy report of statistical models (to R Console and Microsoft Word); (7) mediation and moderation analyses (PROCESS); and (8) additional toolbox for statistics and graphics.
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2025-04-22 |
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r-broom.mixed
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Convert fitted objects from various R mixed-model packages into tidy data frames along the lines of the 'broom' package. The package provides three S3 generics for each model: tidy(), which summarizes a model's statistical findings such as coefficients of a regression; augment(), which adds columns to the original data such as predictions, residuals and cluster assignments; and glance(), which provides a one-row summary of model-level statistics.
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2025-04-22 |
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r-broom.helpers
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Provides suite of functions to work with regression model 'broom::tidy()' tibbles. The suite includes functions to group regression model terms by variable, insert reference and header rows for categorical variables, add variable labels, and more.
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2025-04-22 |
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r-bridgesampling
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Provides functions for estimating marginal likelihoods, Bayes factors, posterior model probabilities, and normalizing constants in general, via different versions of bridge sampling (Meng & Wong, 1996, <http://www3.stat.sinica.edu.tw/statistica/j6n4/j6n43/j6n43.htm>). Gronau, Singmann, & Wagenmakers (2020) <doi:10.18637/jss.v092.i10>.
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2025-04-22 |
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r-brms
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Fit Bayesian generalized (non-)linear multivariate multilevel models using 'Stan' for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include both theory-driven and data-driven non-linear terms, auto-correlation structures, censoring and truncation, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their prior knowledge. Models can easily be evaluated and compared using several methods assessing posterior or prior predictions. References: Bürkner (2017) <doi:10.18637/jss.v080.i01>; Bürkner (2018) <doi:10.32614/RJ-2018-017>; Bürkner (2021) <doi:10.18637/jss.v100.i05>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
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2025-04-22 |
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r-bootnet
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Bootstrap methods to assess accuracy and stability of estimated network structures and centrality indices <doi:10.3758/s13428-017-0862-1>. Allows for flexible specification of any undirected network estimation procedure in R, and offers default sets for various estimation routines.
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2025-04-22 |
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r-botor
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Fork-safe, raw access to the 'Amazon Web Services' ('AWS') 'SDK' via the 'boto3' 'Python' module, and convenient helper functions to query the 'Simple Storage Service' ('S3') and 'Key Management Service' ('KMS'), partial support for 'IAM', the 'Systems Manager Parameter Store' and 'Secrets Manager'.
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2025-04-22 |
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r-bonsai
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Bindings for additional tree-based model engines for use with the 'parsnip' package. Models include gradient boosted decision trees with 'LightGBM' (Ke et al, 2017.) and conditional inference trees and conditional random forests with 'partykit' (Hothorn and Zeileis, 2015. and Hothorn et al, 2006. <doi:10.1198/106186006X133933>).
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2025-04-22 |
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r-blogdown
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Write blog posts and web pages in R Markdown. This package supports the static site generator 'Hugo' (<https://gohugo.io>) best, and it also supports 'Jekyll' (<https://jekyllrb.com>) and 'Hexo' (<https://hexo.io>).
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2025-04-22 |
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r-blandr
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Carries out Bland Altman analyses (also known as a Tukey mean-difference plot) as described by JM Bland and DG Altman in 1986 <doi:10.1016/S0140-6736(86)90837-8>. This package was created in 2015 as existing Bland-Altman analysis functions did not calculate confidence intervals. This package was created to rectify this, and create reproducible plots. This package is also available as a module for the 'jamovi' statistical spreadsheet (see <https://www.jamovi.org> for more information).
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2025-04-22 |
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r-blastula
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Compose and send out responsive HTML email messages that render perfectly across a range of email clients and device sizes. Helper functions let the user insert embedded images, web link buttons, and 'ggplot2' plot objects into the message body. Messages can be sent through an 'SMTP' server, through the 'RStudio Connect' service, or through the 'Mailgun' API service <https://www.mailgun.com/>.
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2025-04-22 |
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r-bkmr
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Implementation of a statistical approach for estimating the joint health effects of multiple concurrent exposures, as described in Bobb et al (2015) <doi:10.1093/biostatistics/kxu058>.
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2025-04-22 |
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r-biostatr
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Datasets and functions for the book "Initiation à la Statistique avec R", F. Bertrand and M. Maumy-Bertrand (2022, ISBN:978-2100782826 Dunod, fourth edition).
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2025-04-22 |
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r-biotools
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Tools designed to perform and evaluate cluster analysis (including Tocher's algorithm), discriminant analysis and path analysis (standard and under collinearity), as well as some useful miscellaneous tools for dealing with sample size and optimum plot size calculations. A test for seed sample heterogeneity is now available. Mantel's permutation test can be found in this package. A new approach for calculating its power is implemented. biotools also contains tests for genetic covariance components. Heuristic approaches for performing non-parametric spatial predictions of generic response variables and spatial gene diversity are implemented.
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2025-04-22 |
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r-biostat3
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Utility functions, datasets and extended examples for survival analysis. This extends a range of other packages, some simple wrappers for time-to-event analyses, datasets, and extensive examples in HTML with R scripts. The package also supports the course Biostatistics III entitled "Survival analysis for epidemiologists in R".
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2025-04-22 |
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r-biodiversityr
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Graphical User Interface (via the R-Commander) and utility functions (often based on the vegan package) for statistical analysis of biodiversity and ecological communities, including species accumulation curves, diversity indices, Renyi profiles, GLMs for analysis of species abundance and presence-absence, distance matrices, Mantel tests, and cluster, constrained and unconstrained ordination analysis. A book on biodiversity and community ecology analysis is available for free download from the website. In 2012, methods for (ensemble) suitability modelling and mapping were expanded in the package.
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2025-04-22 |
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r-biomod2
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Functions for species distribution modeling, calibration and evaluation, ensemble of models, ensemble forecasting and visualization. The package permits to run consistently up to 10 single models on a presence/absences (resp presences/pseudo-absences) dataset and to combine them in ensemble models and ensemble projections. Some bench of other evaluation and visualisation tools are also available within the package.
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2025-04-22 |
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r-binsreg
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Provides tools for statistical analysis using the binscatter methods developed by Cattaneo, Crump, Farrell and Feng (2023a) <arXiv:1902.09608>, Cattaneo, Crump, Farrell and Feng (2023b) <https://nppackages.github.io/references/Cattaneo-Crump-Farrell-Feng_2023_NonlinearBinscatter.pdf> and Cattaneo, Crump, Farrell and Feng (2023c) <arXiv:1902.09615>. Binscatter provides a flexible way of describing the relationship between two variables based on partitioning/binning of the independent variable of interest. binsreg(), binsqreg() and binsglm() implement binscatter least squares regression, quantile regression and generalized linear regression respectively, with particular focus on constructing binned scatter plots. They also implement robust (pointwise and uniform) inference of regression functions and derivatives thereof. binstest() implements hypothesis testing procedures for parametric functional forms of and nonparametric shape restrictions on the regression function. binspwc() implements hypothesis testing procedures for pairwise group comparison of binscatter estimators. binsregselect() implements data-driven procedures for selecting the number of bins for binscatter estimation. All the commands allow for covariate adjustment, smoothness restrictions and clustering.
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2025-04-22 |
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r-binhf
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Binomial Haar-Fisz transforms for Gaussianization as in Nunes and Nason (2009).
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2025-04-22 |
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r-bingroup
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Methods for estimation and hypothesis testing of proportions in group testing designs: methods for estimating a proportion in a single population (assuming sensitivity and specificity equal to 1 in designs with equal group sizes), as well as hypothesis tests and functions for experimental design for this situation. For estimating one proportion or the difference of proportions, a number of confidence interval methods are included, which can deal with various different pool sizes. Further, regression methods are implemented for simple pooling and matrix pooling designs. Methods for identification of positive items in group testing designs: Optimal testing configurations can be found for hierarchical and array-based algorithms. Operating characteristics can be calculated for testing configurations across a wide variety of situations.
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2025-04-22 |
