Provides routines to create some quaternions splines:
    Barry-Goldman algorithm, De Casteljau algorithm, and Kochanek-Bartels
    algorithm. The implementations are based on the Python library
    'splines'. Quaternions splines allow to construct spherical curves. 
    References: Barry and Goldman <doi:10.1145/54852.378511>, 
    Kochanek and Bartels <doi:10.1145/800031.808575>.

A collection of utility functions for community ecology analyses,
    with emphasis on workflows using the 'phyloseq' and 'vegan' packages.
    Includes functions for normalizing OTU tables, computing alpha diversity
    via rarefaction (using a fast C++ implementation), differential abundance
    comparisons with compact letter displays, primer checking for amplicon
    sequencing, plotting QIIME 2/DADA2 generated transition stats and miscellaneous helpers for ordination plots and taxonomic
    name formatting.

Provides functions for quickly writing (and 
  reading back) a data.frame to file in 'SQLite' format. The name 
  stands for *Store Tables using 'SQLite'*, or alternatively for *Quick 
  Store Tables* (either way, it could be pronounced as *Quest*). 
  For data.frames containing the supported data 
  types it is intended to work as a drop-in replacement for the 
  'write_*()' and 'read_*()' functions provided by similar packages.

Run lapply() calls in parallel by submitting them to 
    'gridengine' clusters using the 'qsub' command.

Compute the Quantum Toxic Burden Index from multi-exposure
    panels using a fixed quantum-inspired entanglement encoder; method
    reference (2026) <doi:10.5281/zenodo.20476574>. Provides percentile
    encoding, optional potency-weighted readout, and synergy diagnostics
    for environmental mixture burden scores.

Provides several methods for computing the Quantile Treatment
    Effect (QTE) and Quantile Treatment Effect on the Treated (QTT). The main
    cases covered are (i) treatment is randomly assigned, (ii) treatment is as
    good as randomly assigned after conditioning on covariates (selection on
    observables) using the methods of Firpo (2007)
    <doi:10.1111/j.1468-0262.2007.00738.x>, and (iii) identification is based
    on a Difference in Differences assumption, with support for several
    varieties including Athey and Imbens (2006)
    <doi:10.1111/j.1468-0262.2006.00668.x>, Callaway and Li (2019)
    <doi:10.3982/QE935>, and Callaway, Li, and Oka (2018)
    <doi:10.1016/j.jeconom.2018.06.008>. Version 2.0 adds a unified staggered
    treatment adoption API (built on 'ptetools') for all DiD-based estimators,
    as well as a new lagged-outcome unconfoundedness estimator ('lou_qtt').