This package provides functions to perform Bayesian nonparametric univariate and multivariate density estimation and clustering, by means of Pitman-Yor mixtures, and dependent Dirichlet process mixtures for partially exchangeable data. See Corradin et al. (2021) <doi:10.18637/jss.v100.i15> for more details.

Buckley-James regression for right-censoring survival data with high-dimensional covariates. Implementations for survival data include boosting with componentwise linear least squares, componentwise smoothing splines, regression trees and MARS. Other high-dimensional tools include penalized regression for survival data. See Wang and Wang (2010) <doi:10.2202/1544-6115.1550>.

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.

Fast and accurate calculation of Blaker's binomial and Poisson confidence limits (and some related stuff).

This package provides tools to calibrate, validate, and make predictions with the General Unified Threshold model of Survival adapted for Bee species. The model is presented in the publication from Baas, J., Goussen, B., Miles, M., Preuss, T.G., Roessing, I. (2022) <doi:10.1002/etc.5423> and Baas, J., Goussen, B., Taenzler, V., Roeben, V., Miles, M., Preuss, T.G., van den Berg, S., Roessink, I. (2024) <doi:10.1002/etc.5871>, and is based on the GUTS framework Jager, T., Albert, C., Preuss, T.G. and Ashauer, R. (2011) <doi:10.1021/es103092a>. The authors are grateful to Bayer A.G. for its financial support.

This package provides a colour-first toolkit for the analysis of qualitative (categorical) traits in plant breeding and genetics. It tests observed segregation against the classical Mendelian expectations, fitting every standard mono-, di- and trihybrid ratio automatically and ranking them by goodness of fit, with Yates continuity correction and Monte Carlo exact tests for sparse tables. It partitions chi-square across families into pooled and heterogeneity components, so that a poor overall fit can be attributed either to the hypothesised ratio or to variation between families. Genetic linkage is estimated by maximum likelihood from two-locus second filial generation and backcross data, with logarithm of odds scores and likelihood-ratio confidence intervals for the recombination fraction. The package further computes Shannon-Weaver and Simpson diversity for descriptor states used in distinctness, uniformity and stability testing, and performs multiple correspondence analysis and Gower-distance clustering of mixed categorical and quantitative descriptors. Every analysis returns a tidy result object and a publication-ready ggplot2 figure. Methods follow Mather (1951, <ISBN:9780416470406>), Allard (1956) <doi:10.3733/hilg.v24n10p235> and Gower (1971) <doi:10.2307/2528823>.

Finite Population bootstrap algorithms to estimate the variance of the Horvitz-Thompson estimator for single-stage sampling. For a survey of bootstrap methods for finite populations, see Mashreghi et Al. (2016) <doi:10.1214/16-SS113>.

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>.

This package provides tools for statistical analysis using the binscatter methods developed by Cattaneo, Crump, Farrell and Feng (2024) <https://nppackages.github.io/references/Cattaneo-Crump-Farrell-Feng_2024_AER.pdf>, Cattaneo, Crump, Farrell and Feng (2025) <https://nppackages.github.io/references/Cattaneo-Crump-Farrell-Feng_2025_Stata.pdf> and Cattaneo, Crump, Farrell and Feng (2026) <https://nppackages.github.io/references/Cattaneo-Crump-Farrell-Feng_2026_RESTAT.pdf>. 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.

An interactive document on the topic of binary logistic regression analysis using rmarkdown and shiny packages. Runtime examples are provided in the package function as well as at <https://analyticmodels.shinyapps.io/BinaryLogisticRegressionModelling/>.

This package provides methods for assessing animal movement from telemetry and biologging data using non-parametric Bayesian methods. This includes features for pre- processing and analysis of data, as well as the visualization of results from the models. This framework does not rely on standard parametric density functions, which provides flexibility during model fitting. Further details regarding part of this framework can be found in Cullen et al. (2022) <doi:10.1111/2041-210X.13745>.

This package provides a Bayesian latent space model for complex networks, either weighted or unweighted. Given an observed input graph, the estimates for the latent coordinates of the nodes are obtained through a Bayesian MCMC algorithm. The overall likelihood of the graph depends on a fundamental probability equation, which is defined so that ties are more likely to exist between nodes whose latent space coordinates are close. The package is mainly based on the model by Hoff, Raftery and Handcock (2002) <doi:10.1198/016214502388618906> and contains some extra features (e.g., removal of the Procrustean step, weights implemented as coefficients of the latent distances, 3D plots). The original code related to the above model was retrieved from <https://www2.stat.duke.edu/~pdh10/Code/hoff_raftery_handcock_2002_jasa/>. Users can inspect the MCMC simulation, create and customize insightful graphical representations or apply clustering techniques.

Israeli baby names provided by Israel's Central Bureau of Statistics (CBS/LAMAS). Contains names used for at least 5 children in a given year, covering sectors "Jewish", "Muslim", "Christian-Arab", and "Druze" from 1949-2024. Legacy 1948 data and archived "Other" sector data are provided as separate datasets. Primary data source: CBS Release 391/2025 <https://www.cbs.gov.il/he/mediarelease/DocLib/2025/391/11_25_391t1.xlsx>.

Calculates the Boltzmann entropy of a landscape gradient. This package uses the analytical method created by Gao, P., Zhang, H. and Li, Z., 2018 (<doi:10.1111/tgis.12315>) and by Gao, P. and Li, Z., 2019 (<doi:10.1007/s10980-019-00854-3>). It also extend the original ideas by allowing calculations on data with missing values.

The Bayesian Adjustment for Confounding (BAC) algorithm (Wang et al., 2012) can be used to estimate the causal effect of a continuous exposure on a continuous outcome. This package provides an approximate sensitivity analysis of BAC with regards to the hyperparameter omega. BACprior also provides functions to guide the user in their choice of an appropriate omega value. The method is based on Lefebvre, Atherton and Talbot (2014).

Bayesian kernel machine regression (from the bkmr package) is a Bayesian semi-parametric generalized linear model approach under identity and probit links. There are a number of functions in this package that extend Bayesian kernel machine regression fits to allow multiple-chain inference and diagnostics, which leverage functions from the future', rstan', and coda packages. Reference: Bobb, J. F., Henn, B. C., Valeri, L., & Coull, B. A. (2018). Statistical software for analyzing the health effects of multiple concurrent exposures via Bayesian kernel machine regression. ; <doi:10.1186/s12940-018-0413-y>.

This package provides a platform for interactive data analysis designed to simplify development, deployment, interaction, and exploration (TEDDIE). The package enables users to create customized analyses and deploy them to end users, who can perform interactive analyses and export results to RTF or HTML files. It allows developers to focus on R code for analysis rather than managing HTML or Shiny application code.

It makes the creation of networks from sequences of RNA, with this is done the abstraction of characteristics of these networks with a methodology of maximum entropy for the purpose of making a classification between the classes of the sequences. There are two data present in the BASiNET package, "mRNA", and "ncRNA" with 10 sequences. These sequences were taken from the data set used in the article (LI, Aimin; ZHANG, Junying; ZHOU, Zhongyin, 2014) <doi:10.1186/1471-2105-15-311>, these sequences are used to run examples.

This package provides consistent batch means estimation of Monte Carlo standard errors.

This package provides tools for constructing board/grid based games, as well as readily available game(s) for your entertainment.

This package provides a collection of functions to test spatial autocorrelation between variables, including Moran I, Geary C and Getis G together with scatter plots, functions for mapping and identifying clusters and outliers, functions associated with the moments of the previous statistics that will allow testing whether there is bivariate spatial autocorrelation, and a function that allows identifying (visualizing neighbours) on the map, the neighbors of any region once the scheme of the spatial weights matrix has been established.

This is a port of the WTC MATLAB package written by Aslak Grinsted and the wavelet program written by Christopher Torrence and Gibert P. Compo. This package can be used to perform univariate and bivariate (cross-wavelet, wavelet coherence, wavelet clustering) analyses.

Generates a list, with a size defined by the user, containing the main scientific references and the frequency distribution of authors and journals in the list obtained. The database is a dataframe with academic production metadata made available by bibliographic collections such as Scopus, Web of Science, etc. The temporal evolution of scientific production on a given topic is presented and ordered lists of articles are constructed by number of citations and of authors and journals by level of productivity. Massimo Aria, Corrado Cuccurullo. (2017) <doi:10.1016/j.joi.2017.08.007>. Caibo Zhou, Wenyan Song. (2021) <doi:10.1016/j.jclepro.2021.126943>.

Interactive box plot using plotly for clinical trial analysis.