A comprehensive bundle of utilities for the estimation of probability of informed trading models: original PIN in Easley and O'Hara (1992) and Easley et al. (1996); Multilayer PIN (MPIN) in Ersan (2016); Adjusted PIN (AdjPIN) in Duarte and Young (2009); and volume-synchronized PIN (VPIN) in Easley et al. (2011, 2012). Implementations of various estimation methods suggested in the literature are included. Additional compelling features comprise posterior probabilities, an implementation of an expectation-maximization (EM) algorithm, and PIN decomposition into layers, and into bad/good components. Versatile data simulation tools, and trade classification algorithms are among the supplementary utilities. The package provides fast, compact, and precise utilities to tackle the sophisticated, error-prone, and time-consuming estimation procedure of informed trading, and this solely using the raw trade-level data.

Collect  marketing data from Pinterest Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
    Use four spaces when indenting paragraphs within the Description.

Convert Chinese characters into Pinyin (the official romanization system for Standard Chinese in mainland China, Malaysia, Singapore, and Taiwan. See <https://en.wikipedia.org/wiki/Pinyin> for details), Sijiao (four or five numerical digits per character. See <https://en.wikipedia.org/wiki/Four-Corner_Method>.), Wubi (an input method with five strokes. See <https://en.wikipedia.org/wiki/Wubi_method>) or user-defined codes.

Implements the Product of Independent beta Probabilities dose Escalation (PIPE) design for dual-agent Phase I trials as described in Mander AP, Sweeting MJ (2015) <DOI:10.1002/sim.6434>.

Provides a simple function to bind a piped object to a placeholder
    symbol to enable complex function evaluation with the base R |> pipe.

To take nested function calls and convert them to a more readable form using pipes from package 'magrittr'.

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
  from input to predicted output.