Produces a group screening procedure that is based on maximum Lq-likelihood estimation, to simultaneously account for the group structure and data contamination in variable screening. The methods are described in Li, Y., Li, R., Qin, Y., Lin, C., & Yang, Y. (2021) Robust Group Variable Screening Based on Maximum Lq-likelihood Estimation. Statistics in Medicine, 40:6818-6834.<doi:10.1002/sim.9212>.

Estimate linear quantile mixtures based on Time-Constant (TC) and/or Time-Varying (TV), discrete, random coefficients.

Functions to fit quantile regression models for hierarchical
    data (2-level nested designs) as described in Geraci and
    Bottai (2014, Statistics and Computing) <doi:10.1007/s11222-013-9381-9>.
    A vignette is given in Geraci (2014, Journal of Statistical Software)
    <doi:10.18637/jss.v057.i13> and included in the package documents.
    The packages also provides functions to fit quantile models for
    independent	data and for count responses.

It fits a robust linear quantile regression model using a new family of zero-quantile distributions for the error term. Missing values and censored observations can be handled as well. This family of distribution includes skewed versions of the Normal, Student's t, Laplace, Slash and Contaminated Normal distribution. It also performs logistic quantile regression for bounded responses as shown in Galarza et.al.(2020) <doi:10.1007/s13571-020-00231-0>. It provides estimates and full inference. It also provides envelopes plots for assessing the fit and confidences bands when several quantiles are provided simultaneously.

Provides functions for calculating test statistics, simulating quantiles 
	and simulating p-values of likelihood ratio contrast tests in regression models 
	with a lack of identifiability.

Lexical response data is a package that 
    can be used for processing cued-recall, free-recall, 
    and sentence responses from memory experiments.

The programs were developed for estimation of parameters and testing exponential versus Pareto distribution during our work on hydrologic extremes. See Kozubowski, T.J., A.K. Panorska, F. Qeadan, and A. Gershunov (2007) <doi:10.1080/03610910802439121>, and Panorska, A.K., A. Gershunov, and T.J. Kozubowski (2007) <doi:10.1007/978-0-387-34918-3_26>.