c(person("Sparkfish Analytics", role=c("cph")),
person(given = "Hugo", family = "Saavedra", email = "analytics+hugo@sparkfish.com", role = c("aut", "cre")))
This is a cross-platform linear model to 'SQL' compiler. It generates 'SQL' from linear and generalized linear models. Its interface consists of a single function, modelc(), which takes the output of lm() or glm() functions (or any object which has the same signature) and outputs a 'SQL' character vector representing the predictions on the scale of the response variable as described in Dunn & Smith (2018) <doi:10.1007/978-1-4419-0118-7> and originating in Nelder & Wedderburn (1972) <doi:10.2307/2344614>. The resultant 'SQL' can be included in a 'SELECT' statement and returns output similar to that of the glm.predict() or lm.predict() predictions, assuming numeric types are represented in the database using sufficient precision. Currently log and identity link functions are supported.
Sparkfish Analytics [cph],
Hugo Saavedra [aut, cre]

Modeling physical computer codes with functional outputs using
clustering and dimensionality reduction
rpart, e1071, randomForest, gbm, klaR, wmtsa, mlegp
Statistical learning with vectorial inputs and smooth 1D
curves as outputs. The main function build a model from n
samples (x_i,y_i).

class, rpart, e1071, kernlab, randomForest, klaR, wmtsa,
mlegp, splines
Statistical learning with vectorial inputs and smooth 1D
curves as outputs. The main function builds a model from n
samples (x_i,y_i).

Data Sets Used Useful for Modeling Packages
Data sets used for demonstrating or testing model-related packages are contained in this package.

c(person(given = "Max",
family = "Kuhn",
role = c("aut", "cre"),
email = "max@rstudio.com"),
person(given = "RStudio",
role = "cph"))
Data sets used for demonstrating or testing model-related
packages are contained in this package.

c(
person("Max", "Kuhn", , "max@rstudio.com", role = c("aut", "cre")),
person("RStudio", role = c("cph", "fnd"))
)

Uses 'dplyr' and 'tidyeval' to fit statistical models inside the database. It currently supports KMeans and linear regression models.
dplyr(>= 0.7), rlang, purrr, tibble, tidyr, progress, ggplot2,
readr
dbplyr, testthat, knitr, rmarkdown, nycflights13, RSQLite,
methods, DBI, tidypredict, dbplot, covr