Offers a flexible and user-friendly interface for visualizing conditional 
 effects from a broad range of regression models, including mixed-effects and generalized 
 additive (mixed) models. Compatible model types include lm(), rlm(), glm(), glm.nb(),
 betareg(), and gam() (from 'mgcv'); nonlinear models via nls(); generalized least 
 squares via gls(); and survival models via coxph() (from 'survival'). 
 Mixed-effects models with random intercepts and/or slopes can be fitted using lmer(), 
 glmer(), glmer.nb(), glmmTMB(), or gam() (from 'mgcv', via smooth terms). 
 Plots are rendered using base R graphics with extensive customization options. 
 Approximate confidence intervals for nls() and betareg() models are computed using 
 the delta method. Robust standard errors for rlm() are computed using the sandwich 
 estimator (Zeileis 2004) <doi:10.18637/jss.v011.i10>. For beta regression using 
 'betareg', see Cribari-Neto and Zeileis (2010) <doi:10.18637/jss.v034.i02>. For 
 mixed-effects models with 'lme4', see Bates et al. (2015) <doi:10.18637/jss.v067.i01>. 
 For models using 'glmmTMB', see Brooks et al. (2017) <doi:10.32614/RJ-2017-066>. 
 Methods for generalized additive models using 'mgcv' follow Wood (2017)
 <doi:10.1201/9781315370279>.

Functions are provided to determine production frontiers and technical 
    efficiency measures through non-parametric techniques based upon regression trees. 
    The package includes code for estimating radial input, output, directional and 
    additive measures, plotting graphical representations of the scores and the production 
    frontiers by means of trees, and determining rankings of importance of input variables 
    in the analysis. Additionally, an adaptation of Random Forest by a set of individual 
    Efficiency Analysis Trees for estimating technical efficiency is also included. More 
    details in: <doi:10.1016/j.eswa.2020.113783>.

Provides simple functions to create constraints for small test assembly problems 
    (e.g. van der Linden (2005, ISBN: 978-0-387-29054-6)) using sparse matrices. Currently, 
    'GLPK', 'lpSolve', 'Symphony', and 'Gurobi' are supported as solvers. The 'gurobi' package is not available from 
    any mainstream repository; see <https://www.gurobi.com/downloads/>.