Chunkwise Text-File Processing for 'dplyr'
Text data can be processed chunkwise using 'dplyr' commands. These are recorded and executed per data chunk,
so large files can be processed with limited memory using the 'LaF' package.

Text data can be processed chunkwise using 'dplyr' commands. These
are recorded and executed per data chunk, so large files can be processed with
limited memory using the 'LaF' package.

c(person("Edwin", "de Jonge", email="edwindjonge@gmail.com", role=c("aut","cre")))
Data stored in text file can be processed chunkwise using 'dplyr' commands. These
are recorded and executed per data chunk, so large files can be processed with
limited memory using the 'LaF' package.

c(person("Edwin", "de Jonge", email="edwindjonge@gmail.com", role=c("aut","cre"), comment = c(ORCID = "0000-0002-6580-4718")))
Edwin de Jonge [aut, cre] (<https://orcid.org/0000-0002-6580-4718>)

person(given = "Atsushi",
family = "Yasumoto",
role = c("aut", "cph", "cre"),
email = "atusy.rpkg@gmail.com",
comment = c(ORCID = "0000-0002-8335-495X"))
Set chunk hooks for 'R Markdown' documents <https://rmarkdown.rstudio.com/>,
and improve user experience.
For example, change units of figure sizes, benchmark chunks, and number
lines on code blocks.
Atsushi Yasumoto [aut, cph, cre]
(<https://orcid.org/0000-0002-8335-495X>)

Functions for Conditional Hypergeometric Distributions
An implementation of the probability mass function, cumulative density function, quantile function, random number generator, maximum likelihood estimator, and p-value generator from a conditional hypergeometric distribution: the distribution of how many items are in the overlap of all samples when samples of arbitrary size are each taken without replacement from populations of arbitrary size.

Confidence or Prediction Intervals, Quantiles, and Probabilities
for Statistical Models
c(person("John", "Haman", role = c("cre", "aut"),
email = "mail@johnhaman.org"),
person("Matt", "Avery", role = "aut",
email = "mavery@ida.org"),
person("Institute for Defense Analyses", role = "cph"))
Functions to append confidence intervals, prediction intervals,
and other quantities of interest to data frames. All appended quantities
are for the response variable, after conditioning on the model and covariates.
This package has a data frame first syntax that allows for easy piping.
Currently supported models include (log-) linear, (log-) linear mixed,
and generalized linear models.
arm, dplyr, lme4, magrittr, merTools, methods, stats, tibble
John Haman [cre, aut],
Matt Avery [aut],
Institute for Defense Analyses [cph]