Event dataset repository including both real-life and artificial event logs. They can be used in combination with functionalities provided by the bupaR packages. Janssenswillen et al. (2020) <http://ceur-ws.org/Vol-2703/paperTD7.pdf>.

This package creates text, LaTeX', Markdown, or Bootstrap-styled HTML-formatted odds ratio tables with confidence intervals for multiple logistic regression models.

Given two samples of size n_1 and n_2 from a data set where each sample consists of K functional observations (channels), each recorded on T grid points, the function energy method implements a hypothesis test of equality of channel-wise mean at each channel using the bootstrapped distribution of maximum energy to control family wise error. The function energy_method_complex accomodates complex valued functional observations.

Bayesian estimation of spatial weight matrices in spatial econometric panel models. Allows for estimation of spatial autoregressive (SAR), spatial error (SEM), spatial Durbin (SDM), spatial error Durbin (SDEM) and spatially lagged explanatory variable (SLX) type specifications featuring an unknown spatial weight matrix. Methodological details are given in Krisztin and Piribauer (2022) <doi:10.1080/17421772.2022.2095426>.

This package provides various statistical methods for evaluating heterogeneous treatment effects (HTE) in randomized experiments. The package includes tools to estimate uniform confidence bands for estimation of the group average treatment effect sorted by generic machine learning algorithms (GATES). It also provides the tools to identify a subgroup of individuals who are likely to benefit from a treatment the most "exceptional responders" or those who are harmed by it. Detailed reference in Imai and Li (2023) <doi:10.48550/arXiv.2310.07973>.

This package provides functions are provided to determine production frontiers and technical efficiency measures through non-parametric techniques based upon regression trees. The package includes code for estimating radial input, output, directional and additive measures, plotting graphical representations of the scores and the production frontiers by means of trees, and determining rankings of importance of input variables in the analysis. Additionally, an adaptation of Random Forest by a set of individual Efficiency Analysis Trees for estimating technical efficiency is also included. More details in: <doi:10.1016/j.eswa.2020.113783>.

Enables R users to run large language models locally using GGUF model files and the llama.cpp inference engine. Provides a complete R interface for loading models, generating text completions, extracting vector representations, and streaming responses in real-time. Includes grammar-constrained generation for structured output, text classification, and retrieval-augmented generation (RAG) pipelines. Supports local inference without requiring cloud APIs or internet connectivity, ensuring complete data privacy and control. Based on the llama.cpp project by Georgi Gerganov (2023) <https://github.com/ggml-org/llama.cpp>.

This package provides a lightweight implementation of functions and methods for fast and fully automatic time series modeling and forecasting using Echo State Networks (ESNs).

Misc functions programmed by Eduard Szöcs. Provides read_regnie() to read gridded precipitation data from German Weather Service (DWD, see <http://www.dwd.de/> for more information).

An implementation of the clustering methods of categorical data discussed in Amiri, S., Clarke, B., and Clarke, J. (2015). Clustering categorical data via ensembling dissimilarity matrices. Preprint <arXiv:1506.07930>.

This package provides convenience functions for researching experiences including user, customer, patient, employee, and other human experiences. It provides a suite of tools to simplify data exploration such as benchmarking, comparing groups, and checking for differences. The outputs translate statistical approaches in applied experience research to human readable output.

Environmental seismology is a scientific field that studies the seismic signals, emitted by Earth surface processes. This package provides all relevant functions to read/write seismic data files, prepare, analyse and visualise seismic data, and generate reports of the processing history.

Data sets from the book "Forecasting with exponential smoothing: the state space approach" by Hyndman, Koehler, Ord and Snyder (Springer, 2008).

Runs ecological niche models over all combinations of user-defined settings (i.e., tuning), performs cross validation to evaluate models, and returns data tables to aid in selection of optimal model settings that balance goodness-of-fit and model complexity. Also has functions to partition data spatially (or not) for cross validation, to plot multiple visualizations of results, to run null models to estimate significance and effect sizes of performance metrics, and to calculate range overlap between model predictions, among others. The package was originally built for Maxent models (Phillips et al. 2006, Phillips et al. 2017), but the current version allows possible extensions for any modeling algorithm. The extensive vignette, which guides users through most package functionality but unfortunately has a file size too big for CRAN, can be found here on the package's Github Pages website: <https://jamiemkass.github.io/ENMeval/articles/ENMeval-2.0-vignette.html>.

Software of esDesign is developed to implement the adaptive enrichment designs with sample size re-estimation presented in Lin et al. (2021) <doi: 10.1016/j.cct.2020.106216>. In details, three-proposed trial designs are provided, including the AED1-SSR (or ES1-SSR), AED2-SSR (or ES2-SSR) and AED3-SSR (or ES3-SSR). In addition, this package also contains several widely used adaptive designs, such as the Marker Sequential Test (MaST) design proposed Freidlin et al. (2014) <doi:10.1177/1740774513503739>, the adaptive enrichment designs without early stopping (AED or ES), the sample size re-estimation procedure (SSR) based on the conditional power proposed by Proschan and Hunsberger (1995), and some useful functions. In details, we can calculate the futility and/or efficacy stopping boundaries, the sample size required, calibrate the value of the threshold of the difference between subgroup-specific test statistics, conduct the simulation studies in AED, SSR, AED1-SSR, AED2-SSR and AED3-SSR.

Estimate prior variable weights for Bayesian Additive Regression Trees (BART). These weights correspond to the probabilities of the variables being selected in the splitting rules of the sum-of-trees. Weights are estimated using empirical Bayes and external information on the explanatory variables (co-data). BART models are fitted using the dbarts R package. See Goedhart and others (2023) <doi:10.1002/sim.70004> for details.

Enables users to incorporate expert opinion with parametric survival analysis using a Bayesian or frequentist approach. Expert Opinion can be provided on the survival probabilities at certain time-point(s) or for the difference in mean survival between two treatment arms. Please reference it's use as Cooney, P., White, A. (2023) <doi:10.1177/0272989X221150212>.

Predicts enrollment and events at the design or analysis stage using specified enrollment and time-to-event models through simulations.

Enables launching a series of simulations of a computer code from the R session, and to retrieve the simulation outputs in an appropriate format for post-processing treatments. Five sequential sampling schemes and three coupled-to-MCMC schemes are implemented.

This package provides function to transform latex math expressions into format HTML or Office Open XML Math'. The XML result can then be included in HTML', Microsoft Word documents or Microsoft PowerPoint presentations by using a Markdown document or the R package officer'.

Datasets from Nelson, Coffin and Copeland "Introductory Statistics for Engineering Experimentation" (Elsevier, 2003) with sample code.

This package provides functions to profile a dataset, identify anomalies (special values, outliers, and inliers, defined as data values that are repeated unusually often), and compare data subsets with respect to either numerical or categorical variable distributions.

Special functions that enhance other mixed effect model packages by creating overlayed, reduced rank, and reduced model matrices together with multiple data sets to practice the use of these models. For more details see Covarrubias-Pazaran (2016) <doi:10.1371/journal.pone.0156744>.

Energy landscape analysis (ELA) is a systematic method for analyzing an energy landscape represented as a weighted network. This R package is especially designed to analyze ecological dynamics, i.e., transitions in community compositions. For details of the analysis framework, please visit Suzuki K et al. (2021) <doi:10.1002/ecm.1469>.