DESCRIPTION

Package: impulse
Title: Fit Impulse and Sigmoidal Curves to Longitudinal Data Using TensorFlow
Version: 1.1.2
Authors@R: person(given = "Sean", family = "Hackett",
           role = c("aut", "cre"),
           email = "sean@calicolabs.com",
           comment = c(ORCID = "0000-0002-9553-4341"))
Author: Sean Hackett [aut, cre]
Maintainer: Sean Hackett <sean@calicolabs.com>
Description: Implements the phenomenological kinetic model of Chechik and Koller <doi:10.1089/cmb.2008.13TT> using
  Bayesian priors to improve interpretability. Two models can be fit: a sigmoidal model parameterized
  by a half-max time constant, an asymptote and a rate constant, as well as an impulse model which adds
  a second sigmoidal response described by a second time constant and asymptote. Priors enforce
  non-negativity of timing and rate coefficients and with appropriate tuning, focus support on plausible
  parameter ranges. TensorFlow is used to optimize the maximum posterior estimate (MAP) as a combination
  of a non-linear least squares likelihood and priors on kinetic coefficients.
License: MIT + file LICENSE
SystemRequirements: Python (>= 2.7), Conda
Encoding: UTF-8
LazyData: true
Depends:
    R (>= 3.1)
Imports:
    checkmate,
    dplyr,
    ggplot2,
    glue,
    purrr,
    reticulate,
    rlang,
    tensorflow (>= 1.9),
    tibble,
    tidyr (>= 1.0)
Suggests: 
    gridExtra,
    knitr,
    rcmdcheck,
    rmarkdown,
    testthat (>= 3.0.0)
VignetteBuilder: knitr
RoxygenNote: 7.2.0
Config/testthat/edition: 3