c(person("Gjalt-Jorn", "Peters",
email = "gjalt-jorn@behaviorchange.eu",
role = c("aut", "cre", "ctb"),
comment = c(ORCID = "0000-0002-0336-9589")),
person(given = "Rik",
family = "Crutzen",
role = c("ctb"),
email = "rik.crutzen@maastrichtuniversity.nl",
comment = c(ORCID = "0000-0002-3731-6610")))
covr, DiagrammeR (>= 1.0.0), haven, googlesheets4, knitr,
openxlsx, readxl, rmarkdown, rstudioapi, testthat, writexl,
XLConnect

Computational models for prospect theory and other theories of
risky decision making
Implements (cumulative) prospect theory and other
theories of risky decision making. A practically unlimited number of
choices can be specified. The package allows for different probability
weighting functions and utility functions to be specified, as well as their
parameters. Other features include the ability to plot individual
probability weighting curves and families of these curves, as well as
individual utility function curves. Single-stage decision trees can be
drawn. The probability simplex can also be drawn. Certainty equivalents and
risk premiums can be plotted. Theoretical predictions for different risky
decision making theories can be compared against each other and empirical
data.

Persistence Terrace for Topological Data Analysis
Plot the summary graphic called the persistence terrace for topological inference. It also provides the aid tool called the terrace area plot for determining the number of significant topological features. Moon, C., Giansiracusa, N., and Lazar, N. A. (2018) <doi:10.1080/10618600.2017.1422432>.
TDA, doParallel, foreach, plotly, viridis

Implements p-value computations using an approximation to the cumulative distribution function for a variety of tests for periodicity. These tests include harmonic regression tests with normal and double exponential errors as well as modifications of Fisher's g test. An accompanying vignette illustrates the application of these tests.
knitr, boot, lattice, rmarkdown, GeneCycle,

Efficient algorithms to implement Probit Tensor Factorization (PTF) model for statistical relational learning, which not only inherits the computation efficiency from the classic tensor factorization model but also accounts for the binary nature of relational data. The methodology is based on Ye Liu (2021) <https://repository.lib.ncsu.edu/bitstream/handle/1840.20/37507/etd.pdf?sequence=1> "Computational Methods for Complex Models with Latent Structure".