Description: Linear Liu regression coefficient's estimation and testing with different Liu related measures such as MSE, R-squared etc.

Authors@R: c(person(given = c("Imdad", "Ullah"), family = "Muhammad", role = c("aut", "cre"), email = "mimdadasad@gmail.com",
person(given = "Aslam", family = "Muhammad", role = c("aut", "ctb"), email = "aslamasadi@bzu.edu.pk"))
iii. Imdadullah, Aslam, and Saima (2017)
Maintainer: Imdad Ullah Muhammad <mimdadasad@gmail.com>

Title: Local Interpretable (Model-Agnostic) Visual Explanations
person("Mateusz", "Staniak", role = c("cre","aut"),
person("Przemysław", "Biecek", role = "aut",
Description: Interpretability of complex machine learning models is a growing concern.
This package helps to understand key factors that drive the
decision made by complicated predictive model (so called black box model).
This is achieved through local approximations that are either based on
additive regression like model or CART like model that allows for
higher interactions. The methodology is based on Tulio Ribeiro, Singh, Guestrin (2016) <doi:10.1145/2939672.2939778>.
Suggests: knitr, rmarkdown, testthat, glmnet, e1071
Imports: mlr, dplyr, breakDown, data.table, forestmodel, lubridate,

More details can be found in Staniak, Biecek (2018) <arXiv:1804.01955>.
Suggests: knitr, rmarkdown, testthat, glmnet, covr, DALEX
Imports: mlr, dplyr, breakDown, data.table, forestmodel, shiny, MASS,

More details can be found in Staniak, Biecek (2018) <doi:10.32614/RJ-2018-072>.

Suggests: knitr, rmarkdown, testthat, glmnet, covr, DALEX, RWeka, mda,

Suggests: knitr, rmarkdown, testthat, glmnet, e1071, covr

Suggests: knitr, rmarkdown, testthat, glmnet, e1071, covr, DALEX

Description: Provides a wrapper around LiveChat's API. The R functions allow for
one to extract chat sessions, raw text of chats between agents and customers and
Imports: dplyr, purrr, jsonlite, data.table, magrittr

Title: "Eating the Liver of Data Science"
Description: Provides a collection of helper functions that make various techniques from data science more user-friendly for non-experts. In this way, our aim is to allow non-experts to become familiar with the techniques with only a minimal level of coding knowledge. Indeed, following an ancient Persian idiom, we refer to this as "eating the liver of data science" which could be interpreted as "getting intimately close with data science". Examples of procedures we include are: data partitioning for out-of-sample testing, computing Mean Squared Error (MSE) for quantifying prediction accuracy, and data transformation (z-score and min-max). Besides such helper functions, the package also includes several interesting datasets that are useful for multivariate analysis.