Depends: R (>= 2.0.0), urca, fBasics, fGarch, lattice

Depends: R (>= 2.0.0), urca, stabledist, fGarch, lattice

Description: Offers a gentle introduction to machine learning concepts for practitioners with a statistical pedigree: decomposition of model error (bias-variance trade-off), nonlinear correlations, information theory and functional permutation/bootstrap simulations. Székely GJ, Rizzo ML, Bakirov NK. (2007). <doi:10.1214/009053607000000505>. Reshef DN, Reshef YA, Finucane HK, Grossman SR, McVean G, Turnbaugh PJ, Lander ES, Mitzenmacher M, Sabeti PC. (2011). <doi:10.1126/science.1205438>.

Title: Iterative Proportional Fitting Algorithms for Nested Structures
comment = "Advisor of Amarin Siripanich"
Description: The Iterative Proportional Fitting (IPF) algorithm operates on count data.
This package offers implementations for several algorithms that extend this to
nested structures: 'parent' and 'child' items for both of which constraints can be provided.
The fitting algorithms include Iterative Proportional Updating <https://trid.trb.org/view/881554>,
Hierarchical IPF <doi:10.3929/ethz-a-006620748>, Entropy Optimization <https://trid.trb.org/view/881144>,
and Generalized Raking <doi:10.2307/2290793>. Additionally, a number of replication methods
is also provided such as 'Truncate, replicate, sample' <doi:10.1016/j.compenvurbsys.2013.03.004>.
Imports: BB, dplyr, hms, kimisc, Matrix, plyr, tibble, forcats, rlang,
Suggests: covr, testthat, MASS, sampling, XML, waldo
Author: Kirill Müller [aut, cph] (Creator of the package),
Kay W. Axhausen [ths] (Advisor of Kirill Müller),
Amarin Siripanich [aut, cre] (Contributed `ml_replicate()`),
Taha H. Rashidi [ths] (Advisor of Amarin Siripanich)

Suggests: covr, testthat (>= 3.0.0), MASS, sampling, XML, waldo

person("Matei", "Zaharia", email = "matei@databricks.com", role = c("aut", "cre")),
person("Javier", "Luraschi", email = "javier@rstudio.com", role = c("aut")),
person("Kevin", "Kuo", role = c("aut"), email = "kevin.kuo@rstudio.com",
Description: R interface to 'MLflow', open source platform for the complete machine
learning life cycle, see <https://mlflow.org/>. This package supports installing
'MLflow', tracking experiments, creating and running projects, and saving and
Imports: aws.s3, forge, fs, git2r, httpuv, httr, jsonlite, openssl,
processx, reticulate, packrat, purrr, swagger, withr, xml2,