Methods for frontier
    analysis, Data Envelopment Analysis (DEA), under different
    technology assumptions (fdh, vrs, drs, crs, irs, add/frh, and fdh+),
    and using different efficiency measures (input based, output based,
    hyperbolic graph, additive, super, and directional efficiency). Peers
    and slacks are available, partial price information can be included,
    and optimal cost, revenue and profit can be calculated. Evaluation of
    mergers is also supported.  Methods for graphing the technology sets
    are also included. There is also support for comparative methods based
    on Stochastic Frontier Analyses (SFA) and for convex nonparametric 
    least squares of convex functions (STONED). In general, the methods 
    can be used to solve not only standard models, but also many other 
    model variants. It complements the book, Bogetoft and Otto,
    Benchmarking with DEA, SFA, and R, Springer-Verlag, 2011, but can of
    course also be used as a stand-alone package.

Benchmark your CPU and compare against other CPUs.  Also
    provides functions for obtaining system specifications, such as RAM,
    CPU type, and R version.

Crowd sourced benchmarks from running the
    'benchmarkme' package.

Provides infrastructure to accurately measure and compare
    the execution time of R expressions.

Provides a set of models to estimate nonlinear longitudinal data using Bayesian estimation methods. These models include the: 1) Bayesian Piecewise Random Effects Model (Bayes_PREM()) which estimates a piecewise random effects (mixture) model for a given number of latent classes and a latent number of possible changepoints in each class, and can incorporate class and outcome predictive covariates (see Lamm (2022) <https://hdl.handle.net/11299/252533> and Lock et al., (2018) <doi:10.1007/s11336-017-9594-5>), 2) Bayesian Crossed Random Effects Model (Bayes_CREM()) which estimates a linear, quadratic, exponential, or piecewise crossed random effects models where individuals are changing groups over time (e.g., students and schools; see Rohloff et al., (2024) <doi:10.1111/bmsp.12334>), and 3) Bayesian Bivariate Piecewise Random Effects Model (Bayes_BPREM()) which estimates a bivariate piecewise random effects model to jointly model two related outcomes (e.g., reading and math achievement; see Peralta et al., (2022) <doi:10.1037/met0000358>).