We're pleased to announce MSMBuilder 3.4. It contains a plethora of new features, bug fixes, and improvements. API Changes Range-based slicing on dataset objects is no longer allowed. Keys in the dataset object don't have to be continuous. The empty slice, e.g. ds[:] loads all trajectories in a list (#610). Ward clustering has been renamed AgglomerativeClustering in scikit-learn. Please use the new msmbuilder wrapper class AgglomerativeClustering. An alias for Ward has been made available (#685). PCCA.trimmed_microstates_to_macrostates has been removed. This dictionary was actually keyed by untrimmed microstate labels. PCCA.transform would throw an exception when operating on a system with trimming because it was using this misleading dictionary. Please use pcca.microstate_mapping_ for this functionality (#709). UnionDataset has been removed after deprecation in 3.3. Please use FeatureUnion instead (#671). SubsetFeaturizer and ilk have been removed from the msmbuilder.featurizer namespace. Please import them from msmbuilder.featurizer.subset (#738). FirstSlicer has been removed. Use Slicer(first=x) for the same functionality (#738). msmbuilder.featurizer.load has been removed. Featurizer.save has been removed. Please use utils.load, utils.dump (#738). New Features Dataset objects can call, fit_transform_with() to simplify the common pattern of applying an estimator to a dataset object to produce a new dataset object (#610). kinetic_mapping is a new option to tICA. It's similar to weighted_transform, but based on a better theoretical framework. weighted_transform is deprecated (#766). VonMisesFeaturizer uses soft bins around the unit-circle to give an alternate representation of dihedral angles (#744). MarkovStateModel has a partial_transform() method (#707). KapaAngleFeaturizer is available via the command line (#681). MarkovStateModel has a new attribute, percent_retained_, for ergodic trimming (#689). AlphaAngleFeaturizer computes the dihedral angles between alpha carbons (#691). FunctionFeaturizer computes features based on an arbitrary Python function or callable (#717). Automatic State Partitioning (APM) uses kinetic information to cluster conformations (#748). Improvements Consistent counts setup and ergodic cutoff across various flavors of Markov models (#718, #729, #701, #705). Tests no longer depend on sklearn.hmm, which has been removed (#690). Improvements to RSMDFeaturizer (#695, #764). SparseTICA is completely re-written with large performance improvements when dealing with large numbers of features (#704). Links for downloading example data are un-broken after figshare changed URLs (#751).