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>).

R client for Bender Hyperparameters optimizer : <https://bender.dreem.com>
    The R client allows you to communicate with the Bender API and therefore submit some new trials within your R script itself.

Perform the Benford's Analysis to a data set in order to evaluate if it contains human fabricated data. For more details on the method see Moreau, 2021, Model Assist. Statist. Appl., 16 (2021) 73–79. <doi:10.3233/MAS-210517>.

Provides tools that make it easier to validate data using Benford's Law.

Several specialized statistical tests and support functions 
			for determining if numerical data could conform to Benford's law.