An interface to 'DifferentialEquations.jl' <https://diffeq.sciml.ai/dev/> from the R programming language.
  It has unique high performance methods for solving ordinary differential equations (ODE), stochastic differential equations (SDE),
  delay differential equations (DDE), differential-algebraic equations (DAE), and more. Much of the functionality,
  including features like adaptive time stepping in SDEs, are unique and allow for multiple orders of magnitude speedup over more common methods.
  Supports GPUs, with support for CUDA (NVIDIA), AMD GPUs, Intel oneAPI GPUs, and Apple's Metal (M-series chip GPUs).
  'diffeqr' attaches an R interface onto the package, allowing seamless use of this tooling by R users. For more information,
  see Rackauckas and Nie (2017) <doi:10.5334/jors.151>.

Metrics of difference for comparing pairs of variables or pairs of maps representing real or categorical variables at original and multiple resolutions.

A complete workflow for large-scale, two-condition
    high-throughput drug screening (HTS). It compares drug sensitivity between
    any two experimental conditions - for example irradiated versus
    non-irradiated cells, cancer versus normal cell lines, or treated versus
    untreated samples - across many plates and experiments. The package covers
    the full pipeline: control-based normalisation, plate-level quality-control
    metrics (Z-factor, Z-prime, signal-to-background, signal-to-noise and
    strictly standardised mean difference), replicate-consistency checks,
    four-parameter logistic dose-response fitting with area under the curve
    (AUC) estimation, differential (delta) AUC scoring with within-plate
    standardisation, cut-off and sigma-based hit selection, and
    publication-ready heatmap, scatter and quality-control visualisations.

Package to fit diffusion-based IRT models to response and 
	response time data. Models are fit using marginal maximum 
	likelihood. Parameter restrictions (fixed value and equality 
	constraints) are possible. In addition, factor scores (person drift 
	rate and person boundary separation) can be estimated. Model fit 
	assessment tools are also available. The traditional diffusion model 
	can be estimated as well.