c(person(given = "Kyle",
family = "Butts",
role = c("aut","cre"),
email = "kyle.butts@colorado.edu",
comment = c(ORCID = "0000-0002-9048-8059")),
person(given = "John",
family = "Gardner",
role = "aut",
email = "jrgardne@olemiss.edu",
comment = c(ORCID = "0000-0002-4028-6862")),
person(given = "Grant",
family = "McDermott",
role = "ctb",
email = "grantmcd@uoregon.edu",
comment = c(ORCID = "0000-0001-7883-8573")),
person(given = "Laurent",
family = "Berge",
role = "ctb",
email = "laurent.berge@u-bordeaux.fr"))
data.table, dplyr, magrittr, ggplot2, glue, stats, stringr,
rlang, tibble, rsample, purrr, cli, Matrix, Rcpp, did, broom,
methods, staggered, didimputation, boot
Kyle Butts [aut, cre] (<https://orcid.org/0000-0002-9048-8059>),
John Gardner [aut] (<https://orcid.org/0000-0002-4028-6862>),
Grant McDermott [ctb] (<https://orcid.org/0000-0001-7883-8573>),
Laurent Berge [ctb]

data.table, ggplot2, glue, stats, stringr, rlang, cli, Matrix,
Rcpp, did, broom, methods, staggered, didimputation, boot

Imputation Estimator from Borusyak, Jaravel, and Spiess (2021)
c(person(given = "Kyle",
family = "Butts",
role = c("aut","cre"),
email = "kyle.butts@colorado.edu",
comment = c(ORCID = "0000-0002-9048-8059")))
Estimates Two-way Fixed Effects difference-in-differences/event-study models using the imputation-based approach proposed by Borusyak, Jaravel, and Spiess (2021).
Matrix, magrittr, Rcpp, broom, dplyr, glue, methods, rlang,
stringr

Matrix, magrittr, Rcpp, broom, dplyr, glue, stringr, purrr,
tidyr

Compute Recency Frequency Monetary Scores for your Customer Data
person("Satish", "Hariharan", email = "satish181990@gmail.com", role = c("aut", "cre"))
This hosts the findRFM function which generates RFM scores on a 1-5 point scale for
customer transaction data. The function consumes a data frame with Transaction Number,
Customer ID, Date of Purchase (in date format) and Amount of Purchase as the attributes.
The function returns a data frame with RFM data for the sales information.

An implementation of the metrics dief@t and dief@k to measure the diefficiency (or continuous efficiency) of incremental approaches, see Acosta, M., Vidal, M. E., & Sure-Vetter, Y. (2017) <doi:10.1007/978-3-319-68204-4_1>. The metrics dief@t and dief@k allow for measuring the diefficiency during an elapsed time period t or while k answers are produced, respectively. dief@t and dief@k rely on the computation of the area under the curve of answer traces, and thus capturing the answer rate concentration over a time interval.
flux, fmsb, ggplot2, plyr, graphics, utils