_pkgdown.yml

---
url: https://www.pinstimation.com

home:
  title: "PINstimation - A package for the estimation of models of
  probability of informed trading"
  links:
    - text: R paper on PINstimation
      href: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4117946

development:
  mode: auto

authors:
  Montasser Ghachem:
    href: package-authors.html
  Oguz Ersan:
    href: package-authors.html

template:
  bootstrap: 5
  bootswatch: yeti
  bslib:
    base_font: {google: "Open Sans"}
    heading_font: {google: "Roboto Slab"}
    code_font: {google: "IBM Plex Mono"}
  includes:
    in_header: |
      <!-- Global site tag (gtag.js) - Google Analytics -->
      <script async
      src="https://www.googletagmanager.com/gtag/js?id=G-0SVLS0LDBY">
      </script>
      <script>
        window.dataLayer = window.dataLayer || [];
        function gtag(){dataLayer.push(arguments);}
        gtag('js', new Date());

        gtag('config', 'G-0SVLS0LDBY');
      </script>
  repo:
    url:
      issue: https://github.com/monty-se/PINstimation

navbar:
  structure:
    left: [intro, research, reference, articles, authors, tutorials, news]
    right: [search, github]
  components:
    research:
      text: "Research"
      href: research.html
      aria-label: Research
    authors:
      text: "Authors"
      href: package-authors.html
      aria-label: Authors
    github:
      text: "Github"
      icon: fa-github
      href: https://github.com/monty-se/

reference:
- title: Overview of the package
  desc: >
    An overview of the PINstimation package, and its main
    functionalities.
- contents:
    - PINstimation-package

- title: Factorizations of PIN likelihood functions
  desc: >
    Log-transformations of the different PIN likelihood functions
    (PIN, MPIN, AdjPIN) to avoid floating-point errors.
- contents:
    - fact_pin_e
    - fact_pin_eho
    - fact_pin_lk
    - fact_mpin
    - fact_adjpin

- title: Original PIN model
- subtitle: Initial sets for PIN estimation
  desc: >
    Implementation of the algorithms developed to generate initial
    parameter sets for the estimation of the original PIN model.
- contents:
    - initials_pin_ea
    - initials_pin_gwj
    - initials_pin_yz
- subtitle: Estimation of PIN model
  desc: >
    Implementation of maximum likelihood estimation of the
    original PIN model using the different algorithms of initial
    parameter sets.
- contents:
    - starts_with("pin")
- subtitle: Simulation of PIN Data
  desc: >
    Using the function generatedata_mpin(), we can generate data
    following the original PIN model by setting the argument
    `layers` to 1.
- contents:
    - generatedata_mpin
- subtitle: PIN posterior probabilities
  desc: >
    Computation of posterior probabilties of trading days at the
    optimal probabilities, and rate parameters.
- contents:
    - get_posteriors

- title: Multilayer PIN model
- subtitle: Layer Detection in datasets
  desc: >
    Implementation of the different algorithms of MPIN information layer detection
    in trade data.
- contents:
    - starts_with("detectlayers")
- subtitle: Initial sets for MPIN estimation
  desc: >
    Implementation of the algorithm of Ersan (2016) to generate initial parameter
    sets for the estimation of the multilayer PIN model.
- contents:
    - initials_mpin
- subtitle: Estimation of MPIN model
  desc: >
    Implementation of maximum likelihood estimation of the multilayer PIN model
    using standard methods, and the Expectation-Maximization algorithm.
- contents:
    - starts_with("mpin")
- subtitle: Simulation of MPIN Data
  desc: >
    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.
- contents:
    - generatedata_mpin
- subtitle: MPIN posterior probabilities
  desc: >
    Computation of posterior probabilties of trading days at the optimal
    probabilities, and rate parameters.
- contents:
    - get_posteriors

- title: Adjusted PIN model
- subtitle: Initial sets for AdjPIN estimation
  desc: >
    Implementation of three algorithms to generate initial parameter sets for
    the estimation of the Adjusted PIN model.
- contents:
    - starts_with("initials_adjpin")
- subtitle: Estimation of AdjPIN model
  desc: >
    Implementation of maximum likelihood estimation of the Adjusted PIN model
    using standard methods, and the Expectation-Maximization algorithm.
- contents:
    - adjpin
- subtitle: Simulation of AdjPIN Data
  desc: >
    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.
- contents:
    - generatedata_adjpin


- title: Volume-Synchronized PIN model
  desc: >
    Implementation of estimation of the volume-synchronized PIN model.
- contents:
    - vpin

- title: Aggregation of high-frequency data
  desc: >
    Implementation of four classification algorithms in order to aggregate
    high frequency data into daily data.
- contents:
    - aggregate_trades

- title: Datasets
  desc: >
    Preloaded data sets used in illustrating examples throughout the package.
- contents:
    - dailytrades
    - hfdata

- title: Data simulation classes
  desc: >
    Details of the S4 classes used to generate S4 objects that contain the
    generation parameters of the generated datasets or series of datasets.
- contents:
    - dataset-class
    - data.series-class


- title: Estimation results classes
  desc: >
    Details of the S4 classes used to generate S4 objects that contain the
    estimation results of the different estimation functions.
- contents:
    - starts_with("estimate.")


- title: Other functions
  desc: >
    Function to customize the display of outputs in the R console.
- contents:
    - set_display_digits
...