The Tabular Matrix Problems via Pseudoinverse Estimation (TMPinv)
    is a two-stage estimation method that reformulates structured table-based
    systems - such as allocation problems, transaction matrices, and
    input-output tables - as structured least-squares problems. Based on the
    Convex Least Squares Programming (CLSP) framework, TMPinv solves systems
    with row and column constraints, block structure, and optionally reduced
    dimensionality by (1) constructing a canonical constraint form and applying
    a pseudoinverse-based projection, followed by (2) a convex-programming
    refinement stage to improve fit, coherence, and regularization (e.g., via
    Lasso, Ridge, or Elastic Net).

Fit (exponential or diffusion) response-time extended multinomial processing tree (RT-MPT) models 
      by Klauer and	Kellen (2018) <doi:10.1016/j.jmp.2017.12.003> and Klauer, Hartmann, and Meyer-Grant (submitted). 
      The RT-MPT class not only incorporate	frequencies like traditional multinomial processing tree (MPT) models, 
      but also latencies. This enables it	to estimate process completion times and encoding plus motor execution times 
      next to the process probabilities	of traditional MPTs. 'rtmpt' is a hierarchical Bayesian framework and posterior 
      samples are sampled using a Metropolis-within-Gibbs sampler (for exponential RT-MPTs) or Hamiltonian-within-Gibbs 
      sampler (for diffusion RT-MPTs).

Quickly imports, processes, analyzes, and visualizes mass-spectrometric 
	data.  Includes functions for easily extracting specific data and measurements
	from large (multi-gigabyte) raw Bruker data files, as well as a set of S3 object
	classes for manipulating and measuring mass spectrometric peaks and plotting
	peaks and spectra using the 'ggplot2' package.

Read raw and processed data from acoustic ejection mass
	spectrometry (AEMS) files produced by the Sciex EchoMS instrument.  Includes
	functions to create interactive reader objects, extract raw intensity 
	measurements, mass spectra, and fully-processed mass-transition intensity
	areas.  Methods for data processing and analysis are described in Rimmer et
	al. (2025) <doi:10.1021/acs.analchem.5c03730>. Supports both multiple
	reaction monitoring (MRM) and full-scan (neutral loss and precursor ion)
	data formats.