Tu & Zhou (1999) <doi:10.1002/(SICI)1097-0258(19991030)18:20%3C2749::AID-SIM195%3E3.0.CO;2-C> showed that comparing the means of populations whose data-generating distributions are non-negative with excess zero observations is a problem of great importance in the analysis of medical cost data. In the same study, Tu & Zhou discuss that it can be difficult to control type-I error rates of general-purpose statistical tests for comparing the means of these particular data sets. This package allows users to perform a modified bootstrap-based t-test that aims to better control type-I error rates in these situations.

Residual balancing is a robust method of constructing weights for
  marginal structural models, which can be used to estimate (a) the average treatment effect in 
  a cross-sectional observational study, (b) controlled direct/mediator effects in causal mediation
  analysis, and (c) the effects of time-varying treatments in panel data (Zhou and Wodtke 2020
  <doi:10.1017/pan.2020.2>). This package provides three functions, rbwPoint(), rbwMed(), and rbwPanel(),
  that produce residual balancing weights for estimating (a), (b), (c), respectively.

Data for the examples and exercises in the book "R by Example".  
    Jim Albert and Maria Rizzo (2012, ISBN 978-1-4614-1365-3).

Relational Class Analysis (RCA) is a method for detecting
        heterogeneity in attitudinal data (as described in Goldberg
        A., 2011, Am. J. Soc, 116(5)).

Memoization can be used to speed up repetitive and computational expensive function calls.  The first time a function that implements memoization is called the results are stored in a cache memory.  The next time the function is called with the same set of parameters, the results are momentarily retrieved from the cache avoiding repeating the calculations.  With this package, any R object can be cached in a key-value storage where the key can be an arbitrary set of R objects.  The cache memory is persistent (on the file system).

Tools for preprocessing and processing canopy photographs with 
      support for raw data reading. Provides methods to address variability in 
      sky brightness and to mitigate errors from image acquisition in 
      non-diffuse light. Works with all types of fish-eye lenses, and some 
      methods also apply to conventional lenses.