Eigenvalue-based estimation of the number of factors in
    approximate factor models. Designed to work when either N or T is
    large, without requiring both dimensions to grow simultaneously.
    Implements the eigenvalue ratio estimator of Ahn and Horenstein
    (2013) <doi:10.3982/ECTA8968>, the information criteria of Bai and
    Ng (2002) <doi:10.1111/1468-0262.00273>, the tuned penalty of
    Alessi, Barigozzi and Capasso (2010) <doi:10.1016/j.spl.2010.08.005>,
    the auto-covariance ratio estimator of Lam and Yao (2012)
    <doi:10.1214/12-AOS970>, and the edge distribution estimators of
    Onatski (2009) <doi:10.3982/ECTA6964> and Onatski (2010)
    <doi:10.1162/REST_a_00043>.

Markov chain Monte Carlo (MCMC) sampler for fully Bayesian estimation of latent factor stochastic volatility models with interweaving <doi:10.1080/10618600.2017.1322091>. Sparsity can be achieved through the usage of Normal-Gamma priors on the factor loading matrix <doi:10.1016/j.jeconom.2018.11.007>.

Function factories are functions that make functions. They can be 
    confusing to construct. Straightforward techniques can produce functions 
    that are fragile or hard to understand. While more robust techniques exist 
    to construct function factories, those techniques can be confusing. This 
    package is designed to make it easier to construct function factories.

Perform factorial analysis with a menu and draw graphs interactively thanks to 'FactoMineR' and a Shiny application.

Implements the Factor-Augmented Clustering Tree (FACT) algorithm
    for clustering time series data. The method constructs a classification 
    tree where splits are determined by covariates, and the splitting criterion
    is based on a group factor model representation of the time series within 
    each node. Both threshold-based and permutation-based tests are supported 
    for splitting decisions, with an option for parallel computation.
    For methodological details, see Hu, Li, Luo, and Wang (2025, in preparation), 
    Factor-Augmented Clustering Tree for Time Series.