Description: Provides various styles of function chaining methods: Pipe
operator, Pipe object, and pipeline function, each representing a distinct
pipeline model yet sharing almost a common set of features: A value can be
piped to the first unnamed argument of a function and to dot symbol in an
enclosed expression. The syntax is designed to make the pipeline more
readable and friendly to a wide range of operations.

Title: Flexible Binding for Complex Function Evaluation with the Base R
Description: Provides a simple function to bind a piped object to a placeholder
symbol to enable complex function evaluation with the base R |> pipe.
Author: Brenton M. Wiernik [cre, aut] (<https://orcid.org/0000-0001-9560-6336>,

Title: Convert Nested Functions to Pipes
Description: To take nested function calls and convert them to a more readable form using pipes from package 'magrittr'.
Imports: magrittr, miniUI (>= 0.1.1), rstudioapi (>= 0.4), shiny (>=
Packaged: 2016-09-14 18:20:41 UTC; sahilseth

Description: A framework for defining 'pipelines' of functions for applying data transformations,
model estimation and inverse-transformations, resulting in predicted value generation (or
model-scoring) functions that automatically apply the entire pipeline of functions required to go
Suggests: knitr, testthat, rmarkdown, modelr, tidyverse
Packaged: 2016-12-16 01:48:36 UTC; alexioannides

Packaged: 2016-12-19 14:10:19 UTC; alexioannides

Title: Diagnostics, Reliability and Predictive Maintenance of Pipeline
Authors@R: person("Yuri", "Possokhov", email = "possokhoff@gmail.com",
Description: Functions representing some useful empirical and data-driven
models of heat losses, corrosion diagnostics, reliability and predictive
maintenance of pipeline systems. The package is an option for digital
transformation of technical engineering departments of heat generating and
heat transferring companies. Methods are described in
Author: Yuri Possokhov [aut, cre] (<https://orcid.org/0000-0002-3570-4337>)

Description: An implementation of the generated effect modifier (GEM) method. This method constructs composite variables by linearly combining pre-treatment scalar patient characteristics to create optimal treatment effect modifiers in linear models. The optimal linear combination is called a GEM. Treatment is assumed to have been assigned at random. For reference, see E Petkova, T Tarpey, Z Su, and RT Ogden. Generated effect modifiers (GEMs) in randomized clinical trials. Biostatistics (First published online: July 27, 2016, <doi:10.1093/biostatistics/kxw035>).
Imports: plyr, MASS, ggplot2, Rcpp, RcppArmadillo
Packaged: 2016-11-07 21:18:48 UTC; foreverlovetoyousz