Genetic Algorithms and C-Steps Based LTS (Least Trimmed Squares)
Estimation
Includes the ga.lts() function that estimates
LTS (Least Trimmed Squares) parameters using genetic algorithms
and C-steps. ga.lts() constructs a genetic algorithm to form a
basic subset and iterates C-steps as defined in Rousseeuw and
van-Driessen (2006) to calculate the cost value of the LTS
criterion. OLS (Ordinary Least Squares) regression is known to
be sensitive to outliers. A single outlying observation can
change the values of estimated parameters. LTS is a resistant
estimator even the number of outliers is up to half of the
data. This package is for estimating the LTS parameters with
lower bias and variance in a reasonable time. Version >=1.3
includes the function medmad for fast outlier detection in
linear regression.

This package includes the ga.lts function that estimates
LTS (Least Trimmed Squares) parameters using genetic algorithms
and C-steps. ga.lts() constructs a genetic algorithm to form a
basic subset and iterates C-steps as defined in Rousseeuw and
van-Driessen (2006) to calculate the cost value of the LTS
criterion. OLS(Ordinary Least Squares) regression is known to
be sensitive to outliers. A single outlying observation can
change the values of estimated parameters. LTS is a resistant
estimator even the number of outliers is up to half of the
data. This package is for estimating the LTS parameters with
lower bias and variance in a reasonable time. Version 1.3
included the function medmad for fast outlier detection in
linear regression.

Interface to Galvanize 'Highbond' Internal Audit Software
person(given = "Jonathan",
family = "Lin",
role = c("aut", "cre"),
email = "hello@jonlin.ca",
comment = c(ORCID = "0000-0001-8664-6846"))
An R interface to the Galvanize 'Highbond' API <https://docs-apis.highbond.com>.
magrittr, dplyr, httr, jsonlite, lubridate, rlang, tidyjson,
tibble, tidyr, tidyselect

Functions for fitting and working with generalized
additive models, as described in chapter 7 of "Statistical Models in
S" (Chambers and Hastie (eds), 1991), and "Generalized Additive
Models" (Hastie and Tibshirani, 1990).