## Overview of the package

An overview of the PINstimation package, and its main functionalities.

PINstimation-package
An R package for estimating the probability of informed trading

## Factorizations of PIN likelihood functions

Log-transformations of the different PIN likelihood functions (PIN, MPIN, AdjPIN) to avoid floating-point errors.

fact_pin_eho() fact_pin_lk() fact_pin_e() fact_mpin() fact_adjpin()
Factorizations of the different PIN likelihood functions

## Original PIN model

### Initial sets for PIN estimation

Implementation of the algorithms developed to generate initial parameter sets for the estimation of the original PIN model.

initials_pin_ea()
Initial parameter sets of Ersan & Alici (2016)
initials_pin_gwj()
Initial parameter set of Gan et al.(2015)
initials_pin_yz()
Initial parameter sets of Yan and Zhang (2012)

### Estimation of PIN model

Implementation of maximum likelihood estimation of the original PIN model using the different algorithms of initial parameter sets.

pin()
PIN estimation - custom initial parameter sets
pin_bayes()
PIN estimation - Bayesian approach
pin_ea()
PIN estimation - initial parameter sets of Ersan & Alici (2016)
pin_gwj()
PIN estimation - initial parameter set of Gan et al. (2015)
pin_yz()
PIN estimation - initial parameter sets of Yan & Zhang (2012)

### Simulation of PIN Data

Using the function generatedata_mpin(), we can generate data following the original PIN model by setting the argument layers to 1.

generatedata_mpin()
Simulation of MPIN model data

### PIN posterior probabilities

Computation of posterior probabilties of trading days at the optimal probabilities, and rate parameters.

get_posteriors()
Posterior probabilities for PIN and MPIN estimates

## Multilayer PIN model

### Layer Detection in datasets

Implementation of the different algorithms of MPIN information layer detection in trade data.

detectlayers_e() detectlayers_eg() detectlayers_ecm()

### Initial sets for MPIN estimation

Implementation of the algorithm of Ersan (2016) to generate initial parameter sets for the estimation of the multilayer PIN model.

initials_mpin()
MPIN initial parameter sets of Ersan (2016)

### Estimation of MPIN model

Implementation of maximum likelihood estimation of the multilayer PIN model using standard methods, and the Expectation-Maximization algorithm.

mpin_ecm()
MPIN model estimation via an ECM algorithm
mpin_ml()
MPIN model estimation via standard ML methods

### Simulation of MPIN Data

Using either random, or provided parameters, or range of parameters; generation of levels of daily buyer-initiated, and seller-initated trades following the distribution of trade levels in the multilayer PIN model.

generatedata_mpin()
Simulation of MPIN model data

### MPIN posterior probabilities

Computation of posterior probabilties of trading days at the optimal probabilities, and rate parameters.

get_posteriors()
Posterior probabilities for PIN and MPIN estimates

### Initial sets for AdjPIN estimation

Implementation of three algorithms to generate initial parameter sets for the estimation of the Adjusted PIN model.

initials_adjpin()
AdjPIN initial parameter sets of Ersan & Ghachem (2022b)
initials_adjpin_cl()
AdjPIN initial parameter sets of Cheng and Lai (2021)
initials_adjpin_rnd()

Implementation of maximum likelihood estimation of the Adjusted PIN model using standard methods, and the Expectation-Maximization algorithm.

adjpin()

Using random parameters, provided parameters, or range(s) of parameters; generation of levels of daily buyer-initiated, and seller-initated trades following the distribution of trade levels in the Adjusted PIN model.

generatedata_adjpin()

## Volume-Synchronized PIN model

Implementation of estimation of the volume-synchronized PIN model.

vpin()
Estimation of Volume-Synchronized PIN model

## Aggregation of high-frequency data

Implementation of four classification algorithms in order to aggregate high frequency data into daily data.

aggregate_trades()
Aggregation of high-frequency data

## Datasets

Preloaded data sets used in illustrating examples throughout the package.

dailytrades
Example of quarterly data
hfdata

## Data simulation classes

Details of the S4 classes used to generate S4 objects that contain the generation parameters of the generated datasets or series of datasets.

show(<dataset>)
Simulated data object
show(<data.series>)
List of dataset objects

## Estimation results classes

Details of the S4 classes used to generate S4 objects that contain the estimation results of the different estimation functions.

show(<estimate.adjpin>)
show(<estimate.mpin>)
MPIN estimation results
show(<estimate.mpin.ecm>) selectModel() getSummary()
MPIN estimation results (ECM)
show(<estimate.pin>)
PIN estimation results
show(<estimate.vpin>)
VPIN estimation results

## Other functions

Function to customize the display of outputs in the R console.

set_display_digits()
Package-wide number of digits