Simulation and estimation tools for 
    various types of ambit processes, including trawl processes and weighted
    trawl processes.

Accompanies the book "Designing experiments and 
    analyzing data: A model comparison perspective" (3rd ed.) by 
    Maxwell, Delaney, & Kelley (2018; Routledge). 
    Contains all of the data sets in the book's chapters and 
    end-of-chapter exercises. Information about the book is available at 
    <https://designingexperiments.com/>.

Generate code for use with the Optical Mark Recognition free software Auto Multiple Choice (AMC). More specifically, this package provides functions that use as input the question and answer texts, and output the LaTeX code for AMC.

A tool that "multiply imputes" missing data in a single cross-section
  (such as a survey), from a time series (like variables collected for
  each year in a country), or from a time-series-cross-sectional data
  set (such as collected by years for each of several countries).
  Amelia II implements our bootstrapping-based algorithm that gives
  essentially the same answers as the standard IP or EMis approaches,
  is usually considerably faster than existing approaches and can
  handle many more variables.  Unlike Amelia I and other statistically
  rigorous imputation software, it virtually never crashes (but please
  let us know if you find to the contrary!).  The program also
  generalizes existing approaches by allowing for trends in time series
  across observations within a cross-sectional unit, as well as priors
  that allow experts to incorporate beliefs they have about the values
  of missing cells in their data.  Amelia II also includes useful
  diagnostics of the fit of multiple imputation models.  The program
  works from the R command line or via a graphical user interface that
  does not require users to know R.

Implements anomaly detection as binary classification for cross-sectional data. Uses maximum likelihood estimates and normal probability functions to classify observations as anomalous. The method is presented in the following lecture from the Machine Learning course by Andrew Ng: <https://www.coursera.org/learn/machine-learning/lecture/C8IJp/algorithm/>, and is also described in: Aleksandar Lazarevic, Levent Ertoz, Vipin Kumar, Aysel Ozgur, Jaideep Srivastava (2003) <doi:10.1137/1.9781611972733.3>.