Package providing a number of functions for working with Two- and Four-parameter Beta and closely related distributions (i.e., the Gamma- Binomial-, and Beta-Binomial distributions). Includes, among other things: - d/p/q/r functions for Four-Parameter Beta distributions and Generalized "Binomial" (continuous) distributions, and d/p/r- functions for Beta- Binomial distributions. - d/p/q/r functions for Two- and Four-Parameter Beta distributions parameterized in terms of their means and variances rather than their shape-parameters. - Moment generating functions for Binomial distributions, Beta-Binomial distributions, and observed value distributions. - Functions for estimating classification accuracy and consistency, making use of the Classical Test-Theory based Livingston and Lewis (L&L) and Hanson and Brennan approaches. A shiny app is available, providing a GUI for the L&L approach when used for binary classifications. For url to the app, see documentation for the LL.CA() function. Livingston and Lewis (1995) <doi:10.1111/j.1745-3984.1995.tb00462.x>. Lord (1965) <doi:10.1007/BF02289490>. Hanson (1991) <https://files.eric.ed.gov/fulltext/ED344945.pdf>.

Call the data wrappers for Bursa Metropolitan Municipality's Open Data Portal <https://acikyesil.bursa.bel.tr/>. This will return all datasets stored in different formats.

This package provides functions to compute pair-wise dissimilarities (distance matrices) and multiple-site dissimilarities, separating the turnover and nestedness-resultant components of taxonomic (incidence and abundance based), functional and phylogenetic beta diversity.

Bayesian power/type I error calculation and model fitting using the power prior and the normalized power prior for proportional hazards models with piecewise constant hazard. The methodology and examples of applying the package are detailed in <doi:10.48550/arXiv.2404.05118>. The Bayesian clinical trial design methodology is described in Chen et al. (2011) <doi:10.1111/j.1541-0420.2011.01561.x>, and Psioda and Ibrahim (2019) <doi:10.1093/biostatistics/kxy009>. The proportional hazards model with piecewise constant hazard is detailed in Ibrahim et al. (2001) <doi:10.1007/978-1-4757-3447-8>.

This package implements regression models for binary data on the absolute risk scale. These models are applicable to cohort and population-based case-control data.

Time series regression using dynamic linear models fit using MCMC. See Scott and Varian (2014) <DOI:10.1504/IJMMNO.2014.059942>, among many other sources.

Large panel data sets are often subject to common trends. However, it can be difficult to determine the exact number of these common factors and analyse their properties. The package implements the Barigozzi and Trapani (2022) <doi:10.1080/07350015.2021.1901719> test, which not only provides an efficient way of estimating the number of common factors in large nonstationary panel data sets, but also gives further insights on factor classes. The routine identifies the existence of (i) a factor subject to a linear trend, (ii) the number of zero-mean I(1) and (iii) zero-mean I(0) factors. Furthermore, the package includes the Integrated Panel Criteria by Bai (2004) <doi:10.1016/j.jeconom.2003.10.022> that provide a complementary measure for the number of factors.

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

This package provides a computationally-efficient leading-eigenvalue approximation to tail probabilities and quantiles of large quadratic forms, in particular for the Sequence Kernel Association Test (SKAT) used in genomics <doi:10.1002/gepi.22136>. Also provides stochastic singular value decomposition for dense or sparse matrices.

Calculates nonparametric pointwise confidence intervals for the survival distribution for right censored data, and for medians [Fay and Brittain <DOI:10.1002/sim.6905>]. Has two-sample tests for dissimilarity (e.g., difference, ratio or odds ratio) in survival at a fixed time, and differences in medians [Fay, Proschan, and Brittain <DOI:10.1111/biom.12231>]. Basically, the package gives exact inference methods for one- and two-sample exact inferences for Kaplan-Meier curves (e.g., generalizing Fisher's exact test to allow for right censoring), which are especially important for latter parts of the survival curve, small sample sizes or heavily censored data. Includes mid-p options.

Design dose escalation using Bayesian logistic regression modeling in Phase I oncology trial.

This package provides simplified Brazilian territorial meshes derived from official data published by the Brazilian Institute of Geography and Statistics (IBGE) <https://www.ibge.gov.br/> as local spatial objects, with no download required at use time. Municipal meshes cover selected official editions from 2000 onwards whenever the number of municipalities changes, and current meshes are available for states, regions and other geographic levels. Convenience functions support filtering, joining and plotting the maps, as well as consulting Brazilian territorial codes.

R functions for "The Basics of Item Response Theory Using R" by Frank B. Baker and Seock-Ho Kim (Springer, 2017, ISBN-13: 978-3-319-54204-1) including iccplot(), icccal(), icc(), iccfit(), groupinv(), tcc(), ability(), tif(), and rasch(). For example, iccplot() plots an item characteristic curve under the two-parameter logistic model.

Allows the user to manage easily R packages removal and installation. It offers many functions to display installed packages according to specific dates and removes them if needed. The user is always prompted when running the removal functions in order to confirm the required action. It also provides functions that will install Github starred R packages whether available on CRAN or not.

Discovers, downloads, verifies, and opens bathymetry assets from the National Oceanic and Atmospheric Administration (NOAA) BlueTopo product for user supplied areas of interest. The package keeps source files intact by default, uses terra for spatial data access, supports explicit native-resolution selection policies, and records provenance for reproducible extraction workflows. It accesses the NOAA BlueTopo web service at <https://noaa-ocs-nationalbathymetry-pds.s3.amazonaws.com/> and references product documentation at <https://nauticalcharts.noaa.gov/data/bluetopo.html>.

Make some distributions from the C++ library Boost available in R'. In addition, the normal-inverse Gaussian distribution and the generalized inverse Gaussian distribution are provided. The distributions are represented by R6 classes. The method to sample from the generalized inverse Gaussian distribution is the one given in "Random variate generation for the generalized inverse Gaussian distribution" Luc Devroye (2012) <doi:10.1007/s11222-012-9367-z>.

Bayesian Latent Class Analysis using several different methods.

Designed to simplify and streamline the process of reading and processing large volumes of data in R, this package offers a collection of functions tailored for bulk data operations. It enables users to efficiently read multiple sheets from Microsoft Excel and Google Sheets workbooks, as well as various CSV files from a directory. The data is returned as organized data frames, facilitating further analysis and manipulation. Ideal for handling extensive data sets or batch processing tasks, bulkreadr empowers users to manage data in bulk effortlessly, saving time and effort in data preparation workflows. Additionally, the package seamlessly works with labelled data from SPSS and Stata.

Implementation of the bootkmeans algorithm, a bootstrap augmented k-means algorithm that returns probabilistic cluster assignments. From paper by Ghashti, J.S., Andrews, J.L. Thompson, J.R.J., Epp, J. and H.S. Kochar (2025), "A bootstrap augmented k-means algorithm for fuzzy partitions" (Submitted).

Collection of procedures to perform Bayesian analysis on a variety of factor models. Currently, it includes: "Bayesian Exploratory Factor Analysis" (befa) from G. Conti, S. Frühwirth-Schnatter, J.J. Heckman, R. Piatek (2014) <doi:10.1016/j.jeconom.2014.06.008>, an approach to dedicated factor analysis with stochastic search on the structure of the factor loading matrix. The number of latent factors, as well as the allocation of the manifest variables to the factors, are not fixed a priori but determined during MCMC sampling.

This package provides pathwise estimation of regularized logistic propensity score models using covariate balancing loss functions rather than maximum likelihood. Regularization paths are fit via the adelie elastic-net solver with a glmnet'-like interface, yielding balancing weights that target covariate balance for the ATE and ATT. Under lasso penalization, lambda bounds the maximum covariate imbalance, so the regularization path traces a sequence of decreasing imbalance tolerances. For details, see Sverdrup & Hastie (2026) <doi:10.48550/arXiv.2602.18577>.

This package implements bidirectional two-stage least squares (Bi-TSLS) estimation for identifying bidirectional causal effects between two variables in the presence of unmeasured confounding. The method uses proxy variables (negative control exposure and outcome) along with at least one covariate to handle confounding.

Calculates the bidimensional regression between two 2D configurations following the approach by Tobler (1965).

This project aims to enable the method of Path Analysis to infer causalities from data. For this we propose a hybrid approach, which uses Bayesian network structure learning algorithms from data to create the input file for creation of a PA model. The process is performed in a semi-automatic way by our intermediate algorithm, allowing novice researchers to create and evaluate their own PA models from a data set. The references used for this project are: Koller, D., & Friedman, N. (2009). Probabilistic graphical models: principles and techniques. MIT press. <doi:10.1017/S0269888910000275>. Nagarajan, R., Scutari, M., & Lèbre, S. (2013). Bayesian networks in r. Springer, 122, 125-127. Scutari, M., & Denis, J. B. <doi:10.1007/978-1-4614-6446-4>. Scutari M (2010). Bayesian networks: with examples in R. Chapman and Hall/CRC. <doi:10.1201/b17065>. Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1 - 36. <doi:10.18637/jss.v048.i02>.