New features: New CI workflow step, lint, to ensure notebooks style consistency https://github.com/diana-hep/madminer/commit/2047dc7851e265dc2d36ff6694256dd23f086ffb. Internal changes: Drop support for Python 3.6 https://github.com/diana-hep/madminer/pull/484 (support ends on December 2021). Added support for Python 3.9 https://github.com/diana-hep/madminer/pull/484. Upgrade notebook examples to Python3 syntax https://github.com/diana-hep/madminer/pull/480. Upgrade numpy minimum version to version 1.20.0 https://github.com/diana-hep/madminer/pull/486. Complete rewrite of the HDF5 interface functions https://github.com/diana-hep/madminer/pull/483. Definition of dataclass models to ease attribute access https://github.com/diana-hep/madmine...
New features: Smearing functions in LHEProcessor More powerful observable definitions and cuts in L...
New features: AsymptoticLimits now supports the SALLINO method, estimating the likelihood with one-...
New features: Smarter sampling: MadMiner now keeps track of which events where generated (sampled) ...
Internal changes: Dropped legacy Python 2 support. MadMiner now only supports Python 3.6+. Replace ...
New features: New python2_override keyword in MadMiner.run() and MadMiner.run_multiple() to allow u...
New features: Automatic shuffling of MadMiner HDF5 files after reading in LHE or Delphes files Bug...
New features: Expanded systematics system. Users now declare systematics with MadMiner.add_systemat...
Bug fixes: Various small bug fixes. Internal changes: Refactoring the code structure, moving to m...
New features: Nuisance parameters for ratio-based methods! This required a major refactoring of mad...
New features: Nuisance parameters to model systematic uncertainties, currently only from PDF / scal...
New features: Prototype implementation of joint score computations with finite differences (with Ma...
New features: New madminer.likelihood class will let the user define more powerful likelihood funct...
Internal changes: Disentangled and cleaned up the dependencies. pip install madminer will now only ...
Bug fixes: Fixed Cut LHE parsing after the introduction of data classes in v0.9.0 (https://github.c...
Bug fixes: Fixed a Python2 -> Python3 migration issue when dealing with dictionary values (thanks @...
New features: Smearing functions in LHEProcessor More powerful observable definitions and cuts in L...
New features: AsymptoticLimits now supports the SALLINO method, estimating the likelihood with one-...
New features: Smarter sampling: MadMiner now keeps track of which events where generated (sampled) ...
Internal changes: Dropped legacy Python 2 support. MadMiner now only supports Python 3.6+. Replace ...
New features: New python2_override keyword in MadMiner.run() and MadMiner.run_multiple() to allow u...
New features: Automatic shuffling of MadMiner HDF5 files after reading in LHE or Delphes files Bug...
New features: Expanded systematics system. Users now declare systematics with MadMiner.add_systemat...
Bug fixes: Various small bug fixes. Internal changes: Refactoring the code structure, moving to m...
New features: Nuisance parameters for ratio-based methods! This required a major refactoring of mad...
New features: Nuisance parameters to model systematic uncertainties, currently only from PDF / scal...
New features: Prototype implementation of joint score computations with finite differences (with Ma...
New features: New madminer.likelihood class will let the user define more powerful likelihood funct...
Internal changes: Disentangled and cleaned up the dependencies. pip install madminer will now only ...
Bug fixes: Fixed Cut LHE parsing after the introduction of data classes in v0.9.0 (https://github.c...
Bug fixes: Fixed a Python2 -> Python3 migration issue when dealing with dictionary values (thanks @...
New features: Smearing functions in LHEProcessor More powerful observable definitions and cuts in L...
New features: AsymptoticLimits now supports the SALLINO method, estimating the likelihood with one-...
New features: Smarter sampling: MadMiner now keeps track of which events where generated (sampled) ...