0.4.0

Added

• bt.io.to_mudata: combine data types measured on the same samples into one
  MuData, keeping the samples every modality has (in the first one's order,
  with a warning naming how many each lost). Modality names: taxa,
  function, function_by_taxon, metabolites, host.
• bt.tl.mmvec: which metabolites go with which microbe (mmvec, Morton et
  al. 2019), fitted by scikit-bio, as a microbes x metabolites table of
  row-centred log probabilities.
• bt.ml.PrevalenceFilter and bt.ml.CLR: scikit-learn transformers, so a
  prevalence filter and CLR are fitted inside each cross-validation fold.
  They pass scikit-learn's estimator checks and give bt.pp.filter_features's
  and bt.pp.clr's results. Relative abundance is scikit-learn's own
  Normalizer(norm="l1").
• bt.ml.to_torch: a PyTorch dataset over a table's samples, densifying one
  row at a time, with integer or float labels from an obs column. It needs
  the new extra biotapy[torch] (torch>=2.9).
• bt.ml.embed: one embedding per sample from a pretrained model, returned or
  stored in obsm["X_<model>"]. Models are plugins: a package registers one
  in the entry-point group biotapy.embeddings, and biotapy checks what it
  returns.
• MGM, the Microbial General Model (Zhang et al. 2026), as the first such
  model, behind the new extra biotapy[mgm] (torch>=2.9,
  transformers>=5). Its pretrained weights (MIT) are downloaded once from
  the microformer-mgm 0.5.8 wheel on PyPI and checked against its SHA-256;
  biotapy's embeddings are within 2e-6 of MGM's own code.
• bt.datasets.biocrust: mmvec's soil example, microbes and metabolites of
  a desert biocrust after wetting, downloaded and cached on first use.
• Guide pages for multi-omics and machine learning, and three tutorials:
  multi-omics with mmvec, leak-free cross-validation on the HMP2 cohort, and
  MGM embeddings of GlobalPatterns.

Changed

• The pseudocount warning of bt.pp.clr and bt.pp.philr ends with the
  step that gave it, (pp.clr) or (pp.philr).
• bt.pp.filter_features raises TypeError naming min_prevalence or
  min_total when the threshold is a bool or not a number.
• bt.pp.tax_glom, bt.fn.func_glom and bt.pl.bar's fill sum a table
  of bools or integers narrower than 64 bits in 64 bits, as NumPy's sum
  does (unsigned stays unsigned): an int8 table no longer wraps around, and
  a bool table is counted instead of saturating at True. An int32 table
  now gives int64 sums.
• bt.da.ancombc2 documents that scikit-bio 0.7.4 can fail to estimate the
  bias with two samples in the reference level, where R returns results;
  biotapy raises naming scikit-bio
  (scikit-bio#2631).
• The Coming-from-R page marks the phyloseq calls biotapy does not cover yet
  "not in 0.4".