Functions to estimate a factor model using discrete and continuous proxy variables. The function 'dproxyme' estimates a factor model of discrete proxy variables using an EM algorithm (Dempster, Laird, Rubin (1977) <doi:10.1111/j.2517-6161.1977.tb01600.x>; Hu (2008) <doi:10.1016/j.jeconom.2007.12.001>; Hu(2017) <doi:10.1016/j.jeconom.2017.06.002> ). The function 'cproxyme' estimates a linear factor model (Cunha, Heckman, and Schennach (2010) <doi:10.3982/ECTA6551>).

The tools herein calculate, print, summarize and plot pairwise differences that result from generalized linear models, general linear hypothesis tests and multinomial logistic regression models. For more information, see Armstrong (2013) <doi:10.32614/RJ-2013-021>.

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.