Write modelling results into a database for tigreBrowser', a web-based tool for browsing figures and summary data of independent model fits, such as Gaussian process models fitted for each gene or other genomic element. The browser is available at <https://github.com/PROBIC/tigreBrowser>.

This contains functions that can be used to estimate the time-dependent precision-recall curve (PRC) and the corresponding area under the PRC for right-censored survival data. It also compute time-dependent ROC curve and its corresponding area under the ROC curve (AUC). See Beyene, Chen and Kifle (2024) <doi:10.1002/bimj.202300135>.

This package provides a framework to work with decision rules. Rules can be extracted from supported models, augmented with (custom) metrics using validation data, manipulated using standard dataframe operations, reordered and pruned based on a metric, predict on unseen (test) data. Utilities include; Creating a rulelist manually, Exporting a rulelist as a SQL case statement and so on. The package offers two classes; rulelist and ruleset based on dataframe.

Accurately estimates phase shifts by accounting for period changes and for the point in the circadian cycle at which the stimulus occurs. See Tackenberg et al. (2018) <doi:10.1177/0748730418768116>.

This package provides a graphics output device for R that records plots in a LaTeX-friendly format. The device transforms plotting commands issued by R functions into LaTeX code blocks. When included in a LaTeX document, these blocks are interpreted with the help of TikZ'---a graphics package for TeX and friends written by Till Tantau. Using the tikzDevice', the text of R plots can contain LaTeX commands such as mathematical formula. The device also allows arbitrary LaTeX code to be inserted into the output stream.

This package provides a collection of functions to plot acid/base titration curves (pH vs. volume of titrant), complexation titration curves (pMetal vs. volume of EDTA), redox titration curves (potential vs.volume of titrant), and precipitation titration curves (either pAnalyte or pTitrant vs. volume of titrant). Options include the titration of mixtures, the ability to overlay two or more titration curves, and the ability to show equivalence points.

This package provides a collection of functions for Kronecker structured covariance estimation and testing under the array normal model. For estimation, maximum likelihood and Bayesian equivariant estimation procedures are implemented. For testing, a likelihood ratio testing procedure is available. This package also contains additional functions for manipulating and decomposing tensor data sets. This work was partially supported by NSF grant DMS-1505136. Details of the methods are described in Gerard and Hoff (2015) <doi:10.1016/j.jmva.2015.01.020> and Gerard and Hoff (2016) <doi:10.1016/j.laa.2016.04.033>.

Data filtering module for teal applications. Allows for interactive filtering of data stored in data.frame and MultiAssayExperiment objects. Also displays filtered and unfiltered observation counts.

Helper functions for empirical research in financial economics, addressing a variety of topics covered in Scheuch, Voigt, and Weiss (2023) <doi:10.1201/b23237>. The package is designed to provide shortcuts for issues extensively discussed in the book, facilitating easier application of its concepts. For more information and resources related to the book, visit <https://www.tidy-finance.org/r/index.html>.

This package provides a tidy interface for integrating large language model (LLM) APIs such as Claude', Openai', Gemini','Mistral and local models via Ollama into R workflows. The package supports text and media-based interactions, interactive message history, batch request APIs, and a tidy, pipeline-oriented interface for streamlined integration into data workflows. Web services are available at <https://www.anthropic.com>, <https://openai.com>, <https://aistudio.google.com/>, <https://mistral.ai/> and <https://ollama.com>.

Test functions are often used to test computer code. They are used in optimization to test algorithms and in metamodeling to evaluate model predictions. This package provides test functions that can be used for any purpose.

Unobserved components time series model using the linear innovations state space representation (single source of error) with choice of error distributions and option for dynamic variance. Methods for estimation using automatic differentiation, automatic model selection and ensembling, prediction, filtering, simulation and backtesting. Based on the model described in Hyndman et al (2012) <doi:10.1198/jasa.2011.tm09771>.

Disaggregates low frequency time series data to higher frequency series. Implements the following methods for temporal disaggregation: Boot, Feibes and Lisman (1967) <DOI:10.2307/2985238>, Chow and Lin (1971) <DOI:10.2307/1928739>, Fernandez (1981) <DOI:10.2307/1924371> and Litterman (1983) <DOI:10.2307/1391858>.

Interfaces with the Hugging Face tokenizers library to provide implementations of today's most used tokenizers such as the Byte-Pair Encoding algorithm <https://huggingface.co/docs/tokenizers/index>. It's extremely fast for both training new vocabularies and tokenizing texts.

Several statistical test functions as well as a function for exploratory data analysis to investigate classifiers allocating individuals to one of three disjoint and ordered classes. In a single classifier assessment the discriminatory power is compared to classification by chance. In a comparison of two classifiers the null hypothesis corresponds to equal discriminatory power of the two classifiers. See also "ROC Analysis for Classification and Prediction in Practice" by Nakas, Bantis and Gatsonis (2023), ISBN 9781482233704.

Create a time-varying dataset using features, exposure, and look back specifications.

Detection of outliers in time series following the Chen and Liu (1993) <DOI:10.2307/2290724> procedure. Innovational outliers, additive outliers, level shifts, temporary changes and seasonal level shifts are considered.

Fits mixtures of multivariate t-distributions (with eigen-decomposed covariance structure) via the expectation conditional-maximization algorithm under a clustering or classification paradigm.

Write output (plots and tables) ensuring traceability back to code. Includes a graphics saver with simple automation of stamping with source, destination and creation time. A list of plots can be saved at once. A user-friendly selection of output dimensions for presentations, on-screen inspections, and more available.

This package provides a latent, quasi-independent truncation time is assumed to be linked with the observed dependent truncation time, the event time, and an unknown transformation parameter via a structural transformation model. The transformation parameter is chosen to minimize the conditional Kendall's tau (Martin and Betensky, 2005) <doi:10.1198/016214504000001538> or the regression coefficient estimates (Jones and Crowley, 1992) <doi:10.2307/2336782>. The marginal distribution for the truncation time and the event time are completely left unspecified. The methodology is applied to survival curve estimation and regression analysis.

Calculate Expert Team on Climate Change Detection and Indices (ETCCDI) <-- (acronym) climate indices from daily or hourly temperature and precipitation data. Provides flexible data handling.

This package implements a task queue system for asynchronous parallel computing using PostgreSQL <https://www.postgresql.org/> as a backend. Designed for embarrassingly parallel problems where tasks do not communicate with each other. Dynamically distributes tasks to workers, handles uneven load balancing, and allows new workers to join at any time. Particularly useful for running large numbers of independent tasks on high-performance computing (HPC) clusters with SLURM <https://slurm.schedmd.com/> job schedulers.

Information on all of the TriMet stops in the Portland Metro Area. It includes information such as the longitude, latitude, cross street, and direction of the stop. TriMet has catalogued these stops, 6880 in total.

This package provides a tidy-style interface for applying differential privacy to data frames. Provides pipe-friendly functions to add calibrated noise, compute private statistics, and track privacy budgets using the epsilon-delta differential privacy framework. Implements the Laplace mechanism (Dwork et al. 2006 <doi:10.1007/11681878_14>) and the Gaussian mechanism for achieving differential privacy as described in Dwork and Roth (2014) <doi:10.1561/0400000042>.