Implements bootstrap methods for linear regression models with errors following a time-varying process, focusing on approximating the distribution of the least-squares estimator for regression models with locally stationary errors. It enables the construction of bootstrap and classical confidence intervals for regression coefficients, leveraging intensive simulation studies and real data analysis.

It contains functions that solve least squares linear
	     regression problems under linear equality/inequality
	     constraints. Functions for solving quadratic programming
	     problems are also available, which transform such problems
	     into least squares ones first. It is developed based on the
	     'Fortran' program of Lawson and Hanson (1974, 1995), which is
	     public domain and available at
	     <http://www.netlib.org/lawson-hanson/>.

Analysis of dichotomous, ordinal, and continuous response data using latent space item response models (LSIRMs). Provides 1PL and 2PL LSIRMs for binary response data as described in Jeon et al. (2021) <doi:10.1007/s11336-021-09762-5>, extensions for continuous response data, and graded response models (GRM) for Likert-scale ordinal data as described in De Carolis et al. (2025) <doi:10.1080/00273171.2025.2605678>. Supports Bayesian model selection with spike-and-slab priors, adaptive MCMC algorithms, and methods for handling missing data under missing at random (MAR) and missing completely at random (MCAR) assumptions. Provides various diagnostic plots to inspect the latent space and summaries of estimated parameters.

Fits structural equation modeling via penalized likelihood.

Fits semi-confirmatory structural equation modeling (SEM) via penalized likelihood (PL) or penalized least squares (PLS). For details, please see Huang (2020) <doi:10.18637/jss.v093.i07>.

When the values of the outcome variable Y are either 0 or 1,
      the function lsm() calculates the estimation of the log likelihood 
      in the saturated model. 
      This model is characterized by Llinas (2006, ISSN:2389-8976) in section 2.3 
      through the assumptions 1 and 2. 
      The function LogLik() works (almost perfectly) when the number of independent
      variables K is high, but for small K it calculates wrong values in some cases.
      For this reason, when Y is dichotomous and the data are grouped in J populations,
      it is recommended to use the function lsm() because it works very well for all K.