Imports: graphics, stats, utils, bayestestR (>= 0.10.0), effectsize (>=
Suggests: brms, coda, correlation, dplyr, emmeans (>= 1.5.3), gamm4,
gganimate, ggplot2, glmmTMB, knitr, lme4, logspline, MASS,
Matrix, merTools, mgcv, rmarkdown, rstanarm, rtdists, see,

Description: Implements a general interface for model-based estimations
for a wide variety of models (see list of supported models using the
function 'insight::supported_models()'), used in the computation of
marginal means, contrast analysis and predictions.
Imports: bayestestR (>= 0.11.5), effectsize (>= 0.5.0), insight (>=
Matrix, merTools, mgcv, patchwork, rmarkdown, rstanarm,
rtdists, see (>= 0.6.5), spelling, testthat

Imports: bayestestR (>= 0.11.5), effectsize (>= 0.6.0.1), insight (>=
Suggests: brms, coda, correlation, dplyr, emmeans (>= 1.5.3), forcats,
gamm4, gganimate, ggplot2, glmmTMB, knitr, lme4, logspline,
MASS, Matrix, marginaleffects, merTools, mgcv, patchwork,
rmarkdown, rstanarm, rtdists, see (>= 0.6.5), spelling,

performance (>= 0.8.0), graphics, stats, utils
report, rmarkdown, rstanarm, rtdists, see (>= 0.6.5), spelling,

Imports: bayestestR (>= 0.12.1), effectsize (>= 0.6.0.1), insight (>=
report, rmarkdown, rstanarm, rtdists, see (>= 0.7.0), spelling,
Packaged: 2022-05-27 19:37:31 UTC; indrajeetpatil

Imports: bayestestR (>= 0.12.1), effectsize (>= 0.7.0), insight (>=
performance (>= 0.9.0), graphics, stats, utils
Suggests: brms, correlation, emmeans (>= 1.5.3), gamm4, gganimate,
ggplot2, glmmTMB, knitr, lme4, logspline, Matrix,
marginaleffects, mgcv, patchwork, poorman, report, rmarkdown,
rstanarm, rtdists, see (>= 0.7.0), testthat

person(given = "Hugo", family = "Saavedra", email = "analytics+hugo@sparkfish.com", role = c("aut", "cre")))
Description: 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.