This package provides a set of regular time-series datasets, describing the US electricity grid. That includes the total demand and supply, and as well as the demand by energy source (coal, solar, wind, etc.). Source: US Energy Information Administration (Dec 2019) <https://www.eia.gov/>.

Geneâ based association tests to model count data with excessive zeros and rare variants using zero-inflated Poisson/zero-inflated negative Binomial regression framework. This method was originally described by Fan, Sun, and Li in Genetic Epidemiology 46(1):73-86 <doi:10.1002/gepi.22438>.

This add-on to the package CellNOptR handles time-course data, as opposed to steady state data in CellNOptR. It scales the simulation step to allow comparison and model fitting for time-course data. Future versions will optimize delays and strengths for each edge.

Channel interference in mass cytometry can cause spillover and may result in miscounting of protein markers. We develop a nonparametric finite mixture model and use the mixture components to estimate the probability of spillover. We implement our method using expectation-maximization to fit the mixture model.

Range-v3 is an extension of the Standard Template Library that makes its iterators and algorithms more powerful by making them composable. Unlike other range-like solutions which, seek to do away with iterators, in range-v3 ranges are an abstraction layer on top of iterators.

This package provides functions for working with the scrypt key derivation functions. Scrypt is a password-based key derivation function created by Colin Percival. The algorithm was specifically designed to make it costly to perform large-scale custom hardware attacks by requiring large amounts of memory.

This package provides tools to create dynamic, submission-ready manuscripts, which conform to American Psychological Association manuscript guidelines. It provides R Markdown document formats for manuscripts (PDF and Word) and revision letters (PDF). Helper functions facilitate reporting statistical analyses or create publication-ready tables and plots.

This package provides tools to more conveniently perform tasks associated with add-on packages. pacman conveniently wraps library and package related functions and names them in an intuitive and consistent fashion. It seeks to combine functionality from lower level functions which can speed up workflow.

This package implements Freund and Schapire's Adaboost.M1 algorithm and Breiman's Bagging algorithm using classification trees as individual classifiers. Once these classifiers have been trained, they can be used to predict on new data. Also, cross validation estimation of the error can be done.

cl-rmath is a simple, autogenerated foreign interface for the standalone R API libRmath. There has been no effort to provide a high-level interface for the original library, instead, this library is meant to serve as a building block for such an interface.

The fbcp command provided by this package can be used to copy the primary frame buffer to the secondary frame buffer of a Raspberry Pi. It can for example mirror the primary HDMI output to a secondary LCD display connected to the Raspberry Pi board.

Hoe is a rake/rubygems helper for project Rakefiles. It helps manage, maintain, and release projects and includes a dynamic plug-in system allowing for easy extensibility. Hoe ships with plug-ins for all the usual project tasks including rdoc generation, testing, packaging, deployment, and announcement.

GLI allows you to create command-line applications in Ruby with Git-Like Interfaces: that is, they take subcommands in the style of git and gem. GLI uses a simple domain-specific language, but retains all the power of the built-in OptionParser.

Hoe is a rake/rubygems helper for project Rakefiles. It helps manage, maintain, and release projects and includes a dynamic plug-in system allowing for easy extensibility. Hoe ships with plug-ins for all the usual project tasks including rdoc generation, testing, packaging, deployment, and announcement.

This package contains a variety of functions, based around regime shift analysis of paleoecological data. Citations: Rodionov() from Rodionov (2004) <doi:10.1029/2004GL019448> Lanzante() from Lanzante (1996) <doi:10.1002/(SICI)1097-0088(199611)16:11%3C1197::AID-JOC89%3E3.0.CO;2-L> Hellinger_trans from Numerical Ecology, Legendre & Legendre (ISBN 9780444538680) rolling_autoc from Liu, Gao & Wang (2018) <doi:10.1016/j.scitotenv.2018.06.276> Sample data sets lake_data & lake_RSI processed from Bush, Silman & Urrego (2004) <doi:10.1126/science.1090795> Sample data set January_PDO from NOAA: <https://www.ncei.noaa.gov/access/monitoring/pdo/>.

This package provides tools for implementing Retrieval-Augmented Generation (RAG) workflows with Large Language Models (LLM). Includes functions for document processing, text chunking, embedding generation, storage management, and content retrieval. Supports various document types and embedding providers ('Ollama', OpenAI'), with DuckDB as the default storage backend. Integrates with the ellmer package to equip chat objects with retrieval capabilities. Designed to offer both sensible defaults and customization options with transparent access to intermediate outputs. For a review of retrieval-augmented generation methods, see Gao et al. (2023) "Retrieval-Augmented Generation for Large Language Models: A Survey" <doi:10.48550/arXiv.2312.10997>.

This package provides a tool for multiply imputing missing data using MIDAS', a deep learning method based on denoising autoencoder neural networks (see Lall and Robinson, 2022; <doi:10.1017/pan.2020.49>). This algorithm offers significant accuracy and efficiency advantages over other multiple imputation strategies, particularly when applied to large datasets with complex features. Alongside interfacing with Python to run the core algorithm, this package contains functions for processing data before and after model training, running imputation model diagnostics, generating multiple completed datasets, and estimating regression models on these datasets. For more information see Lall and Robinson (2023) <doi:10.18637/jss.v107.i09>.

r-stitch is an R program for reference panel free, read aware, low coverage sequencing genotype imputation. STITCH runs on a set of samples with sequencing reads in BAM format, as well as a list of positions to genotype, and outputs imputed genotypes in VCF format.

An efficient Rcpp implementation of the Adaptive Rejection Metropolis Sampling (ARMS) algorithm proposed by Gilks, W. R., Best, N. G. and Tan, K. K. C. (1995) <doi:10.2307/2986138>. This allows for sampling from a univariate target probability distribution specified by its (potentially unnormalised) log density.

We aim to deal with data with measurement error in the response and misclassification censoring status under an AFT model. This package primarily contains three functions, which are used to generate artificial data, correction for error-prone data and estimate the functional covariates for an AFT model.

Simple radiocarbon calibration and chronological analysis. This package allows the calibration of radiocarbon ages and modern carbon fraction values using multiple calibration curves. It allows the calculation of highest density region intervals and credible intervals. The package also provides tools for visualising results and estimating statistical summaries.

The goal of the package is to provide an easy-to-use method for estimating degrees of relatedness (up to the second degree) for extreme low-coverage data. The package also allows users to quantify and visualise the level of confidence in the estimated degrees of relatedness.

The vctrs package provides a concept of vector prototype that can be especially useful when deploying models and code. Serialize these object prototypes to JSON so they can be used to check and coerce data in production systems, and deserialize JSON back to the correct object prototypes.

Perform the functional modeling methods of Huang and Wang (2018) <doi:10.1111/biom.12741> to accommodate dependent error in covariates of the proportional hazards model. The adopted measurement error model has minimal assumptions on the dependence structure, and an instrumental variable is supposed to be available.