Time series analysis based on lambda transformer and variational seq2seq, built on 'Torch'.

Runtime for serving containers that can execute R code on the 
    'AWS Lambda' serverless compute service <https://aws.amazon.com/lambda/>.
    Provides the necessary functionality for handling the various endpoints
    required for accepting new input and sending responses.

Lambert W x F distributions are a generalized framework to analyze
    skewed, heavy-tailed data. It is based on an input/output system, where the
    output random variable (RV) Y is a non-linearly transformed version of an input
    RV X ~ F with similar properties as X, but slightly skewed (heavy-tailed).
    The transformed RV Y has a Lambert W x F distribution. This package contains
    functions to model and analyze skewed, heavy-tailed data the Lambert Way:
    simulate random samples, estimate parameters, compute quantiles, and plot/
    print results nicely. The most useful function is 'Gaussianize',
    which works similarly to 'scale', but actually makes the data Gaussian.
    A do-it-yourself toolkit allows users to define their own Lambert W x
    'MyFavoriteDistribution' and use it in their analysis right away.

Interact with 'LaminDB'. 'LaminDB' is an open-source data
    framework for biology. This package allows you to query and download
    data from 'LaminDB' instances.

Approximate marginal maximum likelihood estimation of multidimensional
	latent variable models via adaptive quadrature or Laplace approximations to the 
	integrals in the likelihood function, as presented for confirmatory factor 
	analysis models in Jin, S., Noh, M., and Lee, Y. (2018) 
	<doi:10.1080/10705511.2017.1403287>, for item response theory models in 
	Andersson, B., and Xin, T. (2021) <doi:10.3102/1076998620945199>, and for 
	generalized linear latent variable models in Andersson, B., Jin, S., and 
	Zhang, M. (2023) <doi:10.1016/j.csda.2023.107710>. Models implemented include
	the generalized	partial credit model, the graded response model, and generalized 
	linear latent variable models for Poisson, negative-binomial and normal 
	distributions. Supports a combination of binary, ordinal, count and continuous 
	observed variables and multiple	group models.