graphics, grDevices, inaparc, MASS, stats, utils
cluster, factoextra, fclust, knitr, rmarkdown, vegclust

Zeynel Cebeci [aut, cre],
Figen Yildiz [aut],
Alper Tuna Kavlak [aut],
Cagatay Cebeci [aut],
Hasan Onder [aut]

graphics, grDevices, inaparc, MASS, stats, utils, methods

Interfacing with ProPublica's 'Congress' API
c(
person("Michael W.", "Kearney", ,
email = "kearneymw@missouri.edu", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-0730-4694")),
person("Lingshu", "Hu", ,
role = c("ctb"),
comment = c(ORCID = "0000-0003-0304-882X")),
person("Alevia", "Iuliia", ,
role = c("ctb"),
comment = c(ORCID = "0000-0001-6270-8985"))
)
A simple interface for interacting with ProPublica's 'Congress' API,
which provides data about current and former members of both chambers of the
U.S. Congress and can be found at the following URL: <https://projects.propublica.org/api-docs/congress-api/>.

c(
person("Michael W.", "Kearney", ,
email = "kearneymw@missouri.edu", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-0730-4694")),
person("Lingshu", "Hu", ,
role = c("ctb"),
comment = c(ORCID = "0000-0003-0304-882X")),
person("Alieva", "Iuliia", ,
role = c("ctb"),
comment = c(ORCID = "0000-0001-6270-8985"))
)
curl, tfse (>= 0.5.0), jsonlite, tibble, utils

Partial and Semi-partial (Part) correlation
The R package ppcor can calculate parital and semi-partial
(part) correlations along with p-value.

Partial and Semi-Partial (Part) Correlation
Calculates partial and semi-partial
(part) correlations along with p-value.

Diagnosis and Visualizations Tools for Temporal Point Processes
c(person(given = "Sally",
family = "Sun",
role = "aut"),
person(given = "Owen G.",
family = "Ward",
role = c("aut","cre"),
email = "owen.ward@columbia.edu",
comment = c(ORCID = "0000-0002-9678-3542")),
person(given = "Jing",
family = "Wu",
role = "aut"),
person(given = "Xiaoxi",
family = "Zhao",
role = "aut"),
person(given = "Lihao",
family = "Xiao",
role = "ctb"),
person(given = "Tian",
family = "Zheng",
role = "aut"))
A suite of diagnostic tools for univariate point processes.
This includes tools for simulating and fitting both common and more
complex temporal point processes. We also include functions to
visualise these point processes and collect existing diagnostic
tools of Brown et al. (2002) <doi:10.1162/08997660252741149> and
Wu et al. (2020) <arxiv:2001.09359>,
which can be used to assess the fit of a chosen point process
model.
Sally Sun [aut],
Owen G. Ward [aut, cre] (<https://orcid.org/0000-0002-9678-3542>),
Jing Wu [aut],
Xiaoxi Zhao [aut],
Lihao Xiao [ctb],
Tian Zheng [aut]