PINstimation v0.1.2

New Features

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• We introduce a new function called classify_trades() that enables users to
  classify high-frequency (HF) trades individually, without aggregating them.
  For each HF trade, the function assigns a variable that is set to TRUE if the
  trade is buyer-initiated, or FALSE if it is seller-initiated.

• The aggregate_trades() function enables users to aggregate high-frequency
  (HF) trades at different frequencies. In the previous version, HF trades were
  automatically aggregated into daily trade data. However, with the updated
  version, users can now specify the desired frequency, such as every 15 minutes.

New Bugfixes

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• We identified and corrected an error in the mpin_ecm() function. Previously,
  the function would sometimes produce inconsistent results as the posterior
  distribution allowed for the existence of information layers with a probability
  of zero. We have now fixed this issue and the function produces correct results.

• We have made some updates to the mpin_ml() function to better handle cases
  where the MPIN estimation fails for all initial parameter sets. Specifically,
  we have fixed an error in the display of the estimation results when such failure
  occurs. With these updates, the function should now be able to handle such
  failures more robustly and provide appropriate feedback.

• We have simplified the ECM estimation functions, with a particular focus on
  the adjpin() function. We have improved the convergence condition of the
  iterative process used in the ECM estimation. Moreover, we rounded the values
  of the parameters at each iteration to a relevant number of decimals. This
  shall result in a faster convergence and prevent issues with decreasing
  likelihood values.

PINstimation v0.1.1

New Features

• The functions pin(), pin_*(), mpin_ml(), mpin_ecm(), adjpin(), vpin(), and aggregate_trades()
  accept now, for their arguments data, datasets of type matrix. In the previous version, only dataframes
  are accepted; which did not allow users, for instance, to use rollapply() of the package zoo .

• Introduction of the function pin_bayes() that estimates the original pin model using a bayesian
  approach as described in Griffin et al.(2021).

Bug Fixes

• Fixed an error in the function initials_pin_ea() as it used to produce some parameter sets with
  negative values for trade intensity rates. The negative trade intensity rates are set to zero.

• Fixed two errors in the function vpin(): (1) A bug in the calculation steps of vpin (2) The argument
  verbose did not work properly.

• Fixed an issue with resetting the plan for the future (future::plan) used for parallel processing.

PINstimation v0.0.1-beta

What's Changed

• Added a new function pin_bayes() that implements the Bayesian approach of Griffin et al.(2021)
• Fixed small errors in the implementation of the function vpin()
• Fixed the code of initials_pin_ea() to avoid rare instances where the function produced negative trading rates
• Simplified the computation of the factorizations of the PIN likelihood functions
• Simplified the process of check and validation of the different function arguments

PINstimation v0.1.0

Initial release

Changes:
fixed future::plan reset