Defines thresholds for breaking data into a number of
discrete levels, minimizing the (mean) squared error within all bins.

Calculating Optimal and D-Augmented Designs
c(person(given = "Carlos",
family = "de la Calle-Arroyo",
role = c("aut", "cre"),
email = "carlos.calle.arroyo@gmail.com",
comment = c(ORCID = "0000-0002-5099-888X")),
person(given = "Jesús",
family = "López-Fidalgo",
role = c("aut"),
comment = c(ORCID = "0000-0001-7502-8188")),
person(given = "Licesio J.",
family = "Rodríguez-Aragón",
role = c("aut"),
comment = c(ORCID = "0000-0003-4970-3877"))
)
Calculates D-, Ds-, A- and I-optimal designs for non-linear models, via an implementation of the cocktail algorithm (Yu, 2011, <doi:10.1007/s11222-010-9183-2>). Compares designs via their efficiency, and D-augments any design with a controlled efficiency. An efficient rounding function has been provided to transform approximate designs to exact designs.
testthat (>= 3.0.0), mockery, markdown, tidyverse
ggplot2, purrr, rlang, crayon, cli, magrittr, dplyr, nleqslv,
shiny, DT, shinydashboard, shinyalert, plotly, hrbrthemes,
shinyjs, nloptr, orthopolynom
Carlos de la Calle-Arroyo [aut, cre]
(<https://orcid.org/0000-0002-5099-888X>),
Jesús López-Fidalgo [aut] (<https://orcid.org/0000-0001-7502-8188>),
Licesio J. Rodríguez-Aragón [aut]
(<https://orcid.org/0000-0003-4970-3877>)
Carlos de la Calle-Arroyo <carlos.calle.arroyo@gmail.com>

ggplot2, purrr, rlang, crayon, cli, magrittr, dplyr, nleqslv,
shiny, DT, shinydashboard, shinyalert, plotly, hrbrthemes,
shinyjs, orthopolynom

testthat (>= 3.0.0), mockery, markdown, tidyverse, DT,
shinydashboard, shinyalert, plotly, hrbrthemes, shinyjs,
orthopolynom, magrittr
ggplot2, purrr, rlang, crayon, cli, dplyr, nleqslv, shiny,
utils

A set of tools to support optimization methods (function
minimization with at most bounds and masks).
Tools to assist in safely applying user generated objective and
derivative function to optimization programs. These are primarily function
minimization methods with at most bounds and masks on the parameters.
Provides a way to check the basic computation of objective functions that
the user provides, along with proposed gradient and Hessian functions,
as well as to wrap such functions to avoid failures when inadmissible parameters
are provided. Check bounds and masks. Check scaling or optimality conditions.
Perform an axial search to seek lower points on the objective function surface.
Includes forward, central and backward gradient approximation codes.