Doubly-robust, non-parametric estimators for the transported average treatment effect from Rudolph, Williams, Stuart, and Diaz (2023) <doi:10.48550/arXiv.2304.00117> and the intent-to-treatment average treatment effect from Rudolph and van der Laan (2017) <doi:10.1111/rssb.12213>. Estimators are fit using cross-fitting and nuisance parameters are estimated using the Super Learner algorithm.

An R shiny app designed for diverse text analysis tasks, offering a wide range of methodologies tailored to Natural Language Processing (NLP) needs. It is a versatile, general-purpose tool for analyzing textual data. tall features a comprehensive workflow, including data cleaning, preprocessing, statistical analysis, and visualization, all integrated for effective text analysis.

Utilizing the OpenAI API as the back end (<https://platform.openai.com/docs/api-reference>), TheOpenAIR offers R wrapper functions for the ChatGPT endpoint and several high-level functions that enable the integration of ChatGPT capabilities in diverse data-related tasks, such as data cleansing and automated analytics script generation.

Generate a palette of tints, shades or both from a single colour.

This package provides a common way of validating a biological assay for is through a procedure, where m levels of an analyte are measured with n replicates at each level, and if all m estimates of the coefficient of variation (CV) are less than some prespecified level, then the assay is declared validated for precision within the range of the m analyte levels. Two limitations of this procedure are: there is no clear statistical statement of precision upon passing, and it is unclear how to modify the procedure for assays with constant standard deviation. We provide tools to convert such a procedure into a set of m hypothesis tests. This reframing motivates the m:n:q procedure, which upon completion delivers a 100q% upper confidence limit on the CV. Additionally, for a post-validation assay output of y, the method gives an ``effective standard deviation interval of log(y) plus or minus r, which is a 68% confidence interval on log(mu), where mu is the expected value of the assay output for that sample. Further, the m:n:q procedure can be straightforwardly applied to constant standard deviation assays. We illustrate these tools by applying them to a growth inhibition assay. This is an implementation of the methods described in Fay, Sachs, and Miura (2018) <doi:10.1002/sim.7528>.

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>.

Computes the product moments of the truncated multivariate normal distribution, particularly for cases involving patterned variance-covariance matrices. It also has the capability to calculate these moments with arbitrary positive-definite matrices, although performance may degrade for high-dimensional variables.

This package implements an algorithm for variable selection in high-dimensional linear regression using the "tilted correlation", a new way of measuring the contribution of each variable to the response which takes into account high correlations among the variables in a data-driven way.

Time-Temperature Superposition analysis is often applied to frequency modulated data obtained by Dynamic Mechanic Analysis (DMA) and Rheometry in the analytical chemistry and physics areas. These techniques provide estimates of material mechanical properties (such as moduli) at different temperatures in a wider range of time. This package provides the Time-Temperature superposition Master Curve at a referred temperature by the three methods: the two wider used methods, Arrhenius based methods and WLF, and the newer methodology based on derivatives procedure. The Master Curve is smoothed by B-splines basis. The package output is composed of plots of experimental data, horizontal and vertical shifts, TTS data, and TTS data fitted using B-splines with bootstrap confidence intervals.

This package provides simple and fast date manipulation functions operating on character strings in the format DD.MM.YYYY'. Functions include extraction of day, month, and year components, computation of differences between two dates, and addition or subtraction of months.

U.S. Department of the Treasury, Alcohol and Tobacco Tax and Trade Bureau (TTB) collects data and reports on monthly beer industry production and operations. This data package includes a collection of 10 years (2006 - 2015) worth of data on materials used at U.S. breweries in pounds reported by the Brewer's Report of Operations and the Quarterly Brewer's Report of Operations forms, ready for data analysis. This package also includes historical tax rates on distilled spirits, wine, beer, champagne, and tobacco products as individual data sets.

Computes phylogenetic distances between any two taxa using hierarchical lineage data retrieved from The Taxonomicon <http://taxonomicon.taxonomy.nl>, a comprehensive curated classification of all life based on Systema Naturae 2000 (Brands, 1989 <http://taxonomicon.taxonomy.nl>). Given any two taxon names, retrieves their full lineages, identifies the most recent common ancestor (MRCA), and computes a dissimilarity index based on lineage depth. Outputs native dist objects, enabling direct integration with the R statistical ecosystem for hierarchical clustering, principal coordinate analysis (PCoA), and multivariate ecological analyses. Supports individual distance queries, pairwise distance matrices, clade filtering, and lineage utilities.

Transforms long data into a matrix form to allow for ease of input into modelling packages for regression, principal components, imputation or machine learning. It does this by pivoting on user defined columns, generating a key-value table for variable names to ensure one-to-one mappings are preserved. It is particularly useful when the indicator names in the columns are long descriptive strings, for example "Energy imports, net (% of energy use)". High level analysis wrapper functions for correlation and principal components analysis are provided.

Cluster data without specifying the number of clusters using the Table Invitation Prior (TIP) introduced in the paper "Clustering Gene Expression Using the Table Invitation Prior" by Charles W. Harrison, Qing He, and Hsin-Hsiung Huang (2022) <doi:10.3390/genes13112036>. TIP is a Bayesian prior that uses pairwise distance and similarity information to cluster vectors, matrices, or tensors.

Autoregressive distributed lag (A[R]DL) models (and their reparameterized equivalent, the Generalized Error-Correction Model [GECM]) are the workhorse models in uncovering dynamic inferences. ADL models are simple to estimate; this is what makes them attractive. Once these models are estimated, what is less clear is how to uncover a rich set of dynamic inferences from these models. We provide tools for recovering those inferences. These tools apply to traditional time-series quantities of interest: especially instantaneous effects for any period and cumulative effects for any period (including the long-run effect). They also allow for a variety of shock histories to be applied to the independent variable (beyond just a one-time, one-unit increase) as well as the recovery of inferences in levels for shocks applies to (in)dependent variables in differences (what we call the Generalized Dynamic Response Function). These effects are also available for the general conditional dynamic model advocated by Warner, Vande Kamp, and Jordan (2026 <doi:10.1017/psrm.2026.10087>). We also provide the actual formulae for these effects.

Table, Listings, and Graphs (TLG) library for common outputs used in clinical trials.

Calculates the community four moments (mean, variance, skewness, and kurtosis) of a given trait based on the moments described in Wieczynski et al. (2019) <doi:10.1073/pnas.1813723116>. These functional metrics are extremely useful in characterizing the distribution of traits in a plant community. It also provides tidyverse-friendly wrappers to seamlessly calculate advanced functional diversity indices (e.g., FDis, Rao's Q) using fundiversity (Grenie et al. 2023 <doi:10.1111/ecog.06585>) and functional rarity indices using funrar (Grenie et al. 2017 <doi:10.1111/ddi.12629>). Evaluating these community-weighted moments and diversity metrics allows researchers to evaluate shifts in optimal phenotypes and understand ecological filtering with exactness.

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>.

This package provides functions for compounding and discounting calculations included here serve as a complete reference for various scenarios of time value of money. Raymond M. Brooks (â Financial Management,â 2018, ISBN: 9780134730417). Sheridan Titman, Arthur J. Keown, John D. Martin (â Financial Management: Principles and Applications,â 2017, ISBN: 9780134417219). Jonathan Berk, Peter DeMarzo, David Stangeland, Andras Marosi (â Fundamentals of Corporate Finance,â 2019, ISBN: 9780134735313). S. A. Hummelbrunner, Kelly Halliday, Ali R. Hassanlou (â Contemporary Business Mathematics with Canadian Applications,â 2020, ISBN: 9780135285015).

Multiscale multifractal analysis (MMA) (GieraÅ towski et al., 2012)<DOI:10.1103/PhysRevE.85.021915> is a time series analysis method, designed to describe scaling properties of fluctuations within the signal analyzed. The main result of this procedure is the so called Hurst surface h(q,s) , which is a dependence of the local Hurst exponent h (fluctuation scaling exponent) on the multifractal parameter q and the scale of observation s (data window width).

We propose an optimality criterion to determine the required training set, r-score, which is derived directly from Pearson's correlation between the genomic estimated breeding values and phenotypic values of the test set <doi:10.1007/s00122-019-03387-0>. This package provides two main functions to determine a good training set and its size.

Collection of shiny widgets to support teal applications. Enables the manipulation of application layout and plot or table settings.

Several datasets which describe the challenges and results of competitions in Tournament of Champions. This data is useful for practicing data wrangling, graphing, and analyzing how each season of Tournament of Champions played out.

Easy visualization, wrangling, and feature engineering of time series data for forecasting and machine learning prediction. Consolidates and extends time series functionality from packages including dplyr', stats', xts', forecast', slider', padr', recipes', and rsample'.