Create disposable R packages for testing. You can create, install and load multiple R packages with a single function call, and then unload, uninstall and destroy them with another function call. This is handy when testing how some R code or an R package behaves with respect to other packages.

Fits Bayesian additive regression trees (BART; Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>) while allowing the updating of predictors or response so that BART can be incorporated as a conditional model in a Gibbs/Metropolis-Hastings sampler. Also serves as a drop-in replacement for package BayesTree'.

The load estimation method is based on a general factor model to solve the estimates of load and specific variance. The philosophy of the package is described in Guangbao Guo. (2022). <doi:10.1007/s00180-022-01270-z>.

It is used to identify dysregulated pathways based on a pre-ranked gene pair list. A fast algorithm is used to make the computation really fast. The data in package DysPIAData is needed.

This package provides tools for estimating the Remaining Useful Life (RUL) of degrading systems using linear mixed-effects models and creating a health index. It supports both univariate and multivariate degradation signals. For multivariate inputs, the signals are merged into a univariate health index prior to modeling. Linear and exponential degradation trajectories are supported (the latter using a log transformation). Remaining Useful Life (RUL) distributions are estimated using Bayesian updating for new units, enabling on-site predictive maintenance. Based on the methodology of Liu and Huang (2016) <doi:10.1109/TASE.2014.2349733>.

This package provides a library of density, distribution function, quantile function, (bounded) raw moments and random generation for a collection of distributions relevant for the firm size literature. Additionally, the package contains tools to fit these distributions using maximum likelihood and evaluate these distributions based on (i) log-likelihood ratio and (ii) deviations between the empirical and parametrically implied moments of the distributions. We add flexibility by allowing the considered distributions to be combined into piecewise composite or finite mixture distributions, as well as to be used when truncated. See Dewitte (2020) <https://hdl.handle.net/1854/LU-8644700> for a description and application of methods available in this package.

This package provides a metapackage that brings together a curated collection of R packages containing domain-specific datasets. It includes time series data, educational metrics, crime records, medical datasets, and oncology research data. Designed to provide researchers, analysts, educators, and data scientists with centralized access to structured and well-documented datasets, this metapackage facilitates reproducible research, data exploration, and teaching applications across a wide range of domains. Included packages: - timeSeriesDataSets': Time series data from economics, finance, energy, and healthcare. - educationR': Datasets related to education, learning outcomes, and school metrics. - crimedatasets': Datasets on global and local crime and criminal behavior. - MedDataSets': Datasets related to medicine, public health, treatments, and clinical trials. - OncoDataSets': Datasets focused on cancer research, survival, genetics, and biomarkers.

Density surface modelling of line transect data. A Generalized Additive Model-based approach is used to calculate spatially-explicit estimates of animal abundance from distance sampling (also presence/absence and strip transect) data. Several utility functions are provided for model checking, plotting and variance estimation.

Exploratory analysis of a data base. Using the functions of this package is possible to filter the data set detecting atypical values (outliers) and to perform exploratory analysis through visual inspection or dispersion measures. With this package you can explore the structure of your data using several parameters at the same time joining statistical parameters with different graphics. Finally, this package aid to confirm or reject the hypothesis that your data structure presents a normal distribution. Therefore this package is useful to get a previous insight of your data before to carry out statistical analysis.

Provide tools for drought monitoring based on univariate and multivariate drought indicators.Statistical drought prediction based on Ensemble Streamflow Prediction (ESP), drought risk assessments, and drought propagation are also provided. Please see Hao Zengchao et al. (2017) <doi:10.1016/j.envsoft.2017.02.008>.

Seasonal- and calendar adjustment of time series with daily frequency using the DSA approach developed by Ollech, Daniel (2018): Seasonal adjustment of daily time series. Bundesbank Discussion Paper 41/2018.

Area under the curve (AUC; Myerson et al., 2001) <doi:10.1901/jeab.2001.76-235> is a popular measure used in discounting research. Although the calculation of AUC is standardized, there are differences in AUC based on some assumptions. For example, Myerson et al. (2001) <doi:10.1901/jeab.2001.76-235> assumed that (with delay discounting data) a researcher would impute an indifference point at zero delay equal to the value of the larger, later outcome. However, this practice is not clearly followed. This imputed zero-delay indifference point plays an important role in log and ordinal versions of AUC. Ordinal and log versions of AUC are described by Borges et al. (2016)<doi:10.1002/jeab.219>. The package can calculate all three versions of AUC [and includes a new version: IHS(AUC)], impute indifference points when x = 0, calculate ordinal AUC in the case of Halton sampling of x-values, and account for probability discounting AUC.

Data screening is an important first step of any statistical analysis. dataMaid auto generates a customizable data report with a thorough summary of the checks and the results that a human can use to identify possible errors. It provides an extendable suite of test for common potential errors in a dataset.

Implementation of frequency tables and bar charts for qualitative variables and checkbox fields. This package implements tables and charts used in reports at Funarte (National Arts Foundation) and OBEC (Culture and Creative Economy Observatory) in Brazil, and its main purpose is to simplify the use of R for people with a background in the humanities and arts. Examples and details can be viewed in this presentation from 2026: <https://formacao2026.netlify.app/assets/modulo_3/modulo3#/title-slide>.

This package provides vectorised functions for computing p-values of various common discrete statistical tests, as described e.g. in Agresti (2002) <doi:10.1002/0471249688>, including their distributions. Exact and approximate computation methods are provided. For exact ones, several procedures of determining two-sided p-values are included, which are outlined in more detail in Hirji (2006) <doi:10.1201/9781420036190>.

Fast C++ implementation of Dynamic Time Warping for time series dissimilarity analysis, with applications in environmental monitoring and sensor data analysis, climate science, signal processing and pattern recognition, and financial data analysis. Built upon the ideas presented in Benito and Birks (2020) <doi:10.1111/ecog.04895>, provides tools for analyzing time series of varying lengths and structures, including irregular multivariate time series. Key features include individual variable contribution analysis, restricted permutation tests for statistical significance, and imputation of missing data via GAMs. Additionally, the package provides an ample set of tools to prepare and manage time series data.

This package provides functions designed to connect disease-related differential proteins and co-expression network. It provides the basic statics analysis included t test, ANOVA analysis. The network construction is not offered by the package, you can used WGCNA package which you can learn in Peter et al. (2008) <doi:10.1186/1471-2105-9-559>. It also provides module analysis included PCA analysis, two enrichment analysis, Planner maximally filtered graph extraction and hub analysis.

This package provides methods for testing the equality between groups of estimated density functions. The package implements FDET (Fourier-based Density Equality Testing) and MDET (Moment-based Density Equality Testing), two new approaches introduced by the author. Both methods extend an earlier testing approach by Delicado (2007), "Functional k-sample problem when data are density functions" <doi:10.1007/s00180-007-0047-y>, which is referred to as DET (Density Equality Testing) in this package for clarity. FDET compares groups of densities based on their global shape using Fourier transforms, while MDET tests for differences in distributional moments. All methods are described in Anarat, Krutmann and Schwender (2025), "Testing for Differences in Extrinsic Skin Aging Based on Density Functions" (Submitted).

This package performs drug demand forecasting by modeling drug dispensing data while taking into account predicted enrollment and treatment discontinuation dates. The gap time between randomization and the first drug dispensing visit is modeled using interval-censored exponential, Weibull, log-logistic, or log-normal distributions (Anderson-Bergman (2017) <doi:10.18637/jss.v081.i12>). The number of skipped visits is modeled using Poisson, zero-inflated Poisson, or negative binomial distributions (Zeileis, Kleiber & Jackman (2008) <doi:10.18637/jss.v027.i08>). The gap time between two consecutive drug dispensing visits given the number of skipped visits is modeled using linear regression based on least squares or least absolute deviations (Birkes & Dodge (1993, ISBN:0-471-56881-3)). The number of dispensed doses is modeled using linear or linear mixed-effects models (McCulloch & Searle (2001, ISBN:0-471-19364-X)).

This package provides a specific and comprehensive framework for the analyses of time-to-event data in agriculture. Fit non-parametric and parametric time-to-event models. Compare time-to-event curves for different experimental groups. Plots and other displays. It is particularly tailored to the analyses of data from germination and emergence assays. The methods are described in Onofri et al. (2022) "A unified framework for the analysis of germination, emergence, and other time-to-event data in weed science", Weed Science, 70, 259-271 <doi:10.1017/wsc.2022.8>.

Various methods for the identification of trend and seasonal components in time series (TS) are provided. Among them is a data-driven locally weighted regression approach with automatically selected bandwidth for equidistant short-memory time series. The approach is a combination / extension of the algorithms by Feng (2013) <doi:10.1080/02664763.2012.740626> and Feng, Y., Gries, T., and Fritz, M. (2020) <doi:10.1080/10485252.2020.1759598> and a brief description of this new method is provided in the package documentation. Furthermore, the package allows its users to apply the base model of the Berlin procedure, version 4.1, as described in Speth (2004) <https://www.destatis.de/DE/Methoden/Saisonbereinigung/BV41-methodenbericht-Heft3_2004.pdf?__blob=publicationFile>. Permission to include this procedure was kindly provided by the Federal Statistical Office of Germany.

This package provides functions to randomly select, return, and print quotes or entire scenes from the American version of the show the Office. Receive laughs from one of of the greatest sitcoms of all time on demand. Add these functions to your .Rprofile to get a good laugh everytime you start a new R session.

This package implements the DAAREM method for accelerating the convergence of slow, monotone sequences from smooth, fixed-point iterations such as the EM algorithm. For further details about the DAAREM method, see Henderson, N.C. and Varadhan, R. (2019) <doi:10.1080/10618600.2019.1594835>.

This package provides methods for estimating multi-stage optimal dynamic treatment regimes for survival outcomes with dependent censoring. Cho, H., Holloway, S. T., and Kosorok, M. R. (2022) <doi:10.1093/biomet/asac047>.