A fast, flexible and transparent framework to estimate context-specific word and short document embeddings using the 'a la carte' 
    embeddings approach developed by Khodak et al. (2018) <doi:10.48550/arXiv.1805.05388> and evaluate hypotheses about covariate effects on embeddings using 
    the regression framework developed by Rodriguez et al. (2021)<doi:10.1017/S0003055422001228>. New version of the package applies a new estimator to measure the distance between word embeddings as described in Green et al. (2025) <doi:10.1017/pan.2024.22>.

Search across R files with contextual results, highlights and clickable links. Includes an add-in for further workflow enhancement.

Various utilities for evaluating continued fractions.

Converts numbers to continued fractions and back again. A solver for Pell's Equation is provided.  The method for calculating roots in continued fraction form is provided without published attribution in such places as Professor Emeritus Jonathan Lubin, <http://www.math.brown.edu/jlubin/> and his post to StackOverflow, <https://math.stackexchange.com/questions/2215918> , or Professor Ron Knott, e.g., <https://r-knott.surrey.ac.uk/Fibonacci/cfINTRO.html> .

Provides an object class for dealing with many multivariate
    probability distributions at once, useful for simulation.

Provides functions to perform statistical inference of data organized in contingency tables. This package is a companion to the "Statistical Analysis of Contingency Tables" book by Fagerland et al. <ISBN 9781466588172>.

A collection of synthetic datasets simulating sales transactions from a fictional company. The dataset includes various related tables that contain essential business and operational data, useful for analyzing sales performance and other business insights. Key tables included in the package are:
  - "sales": Contains data on individual sales transactions, including order details, pricing, quantities, and customer information.
  - "customer": Stores customer-specific details such as demographics, geographic location, occupation, and birthday.
  - "store": Provides information about stores, including location, size, status, and operational dates.
  - "orders": Contains details about customer orders, including order and delivery dates, store, and customer data.
  - "product": Contains data on products, including attributes such as product name, category, price, cost, and weight.
  - "calendar": A time-based table that includes date-related attributes like year, month, quarter, day, and working day indicators.
  This dataset is ideal for practicing data analysis, performing time-series analysis, creating reports, or simulating business intelligence scenarios.