* Guided Analytics for Testing Manufacturing Parameters* (

**igate**)

An implementation of the initial guided analytics for parameter testing and controlband extraction framework. Functions are available for continuous and categorical target variables as well as for generating standardized reports of the conducted analysis. See <https://…/igate> for more information on the technology.

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**Retrieve Raw R Code from Popular Tutorials and Websites****rawr**)

Retrieves pure R code from popular R websites, including github <https://github.com>, kaggle <https://www.kaggle.com>, datacamp <https://www.datacamp.com>, and R blogs made using R blogdown <https://…/blogdown>.

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**Testing for Structural Breaks under Long Memory and Testing for Changes in Persistence****memochange**)

Test procedures and break point estimators for persistent processes that exhibit structural breaks in mean or in persistence. On the one hand the package contains the most popular approaches for testing whether a time series exhibits a break in persistence from I(0) to I(1) or vice versa, such as those of Busetti and Taylor (2004) and Leybourne, Kim, and Taylor (2007). The approach by Martins and Rodrigues (2014), which allows to detect changes from I(d1) to I(d2) with d1 and d2 being non-integers, is included as well. In case the tests reject the null of constant persistence, various breakpoint estimators are available to detect the point of the break as well as the order of integration in the two regimes. On the other hand the package contains the most popular approaches to test for a change-in-mean of a long-memory time series, which were recently reviewed by Wenger, Leschinski, and Sibbertsen (2018). These include memory robust versions of the CUSUM, sup-Wald, and Wilcoxon type tests. The tests either utilize consistent estimates of the long-run variance or a self normalization approach in their test statistics. Betken (2016) <doi:10.1111/jtsa.12187> Busetti and Taylor (2004) <doi:10.1016/j.jeconom.2003.10.028> Dehling, Rooch and Taqqu (2012) <doi:10.1111/j.1467-9469.2012.00799.x> Harvey, Leybourne and Taylor (2006) <doi:10.1016/j.jeconom.2005.07.002> Horvath and Kokoszka (1997) <doi:10.1016/S0378-3758(96)00208-X> Hualde and Iacone (2017) <doi:10.1016/j.econlet.2016.10.014> Iacone, Leybourne and Taylor (2014) <doi:10.1111/jtsa.12049> Leybourne, Kim, Smith, and Newbold (2003) <doi:10.1111/1368-423X.t01-1-00110> Leybourne and Taylor (2004) <doi:10.1016/j.econlet.2003.12.015> Leybourne, Kim, and Taylor (2006): <doi:10.1111/j.1467-9892.2006.00517.x> Martins and Rodrigues (2014) <doi:10.1016/j.csda.2012.07.021> Shao (2011) <doi:10.1111/j.1467-9892.2010.00717.x> Sibbertsen and Kruse (2009) <doi:10.1111/j.1467-9892.2009.00611.x> Wang (2008) <doi:10.1080/00949650701216604> Wenger, Leschinski and Sibbertsen (2018) <doi:10.1016/j.econlet.2017.12.007>.

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**Parametric G-Formula****gfoRmula**)

Implements the parametric g-formula algorithm of Robins (1986) <doi:10.1016/0270-0255(86)90088-6>. The g-formula can be used to estimate the causal effects of hypothetical time-varying treatment interventions on the mean or risk of an outcome from longitudinal data with time-varying confounding. This package allows: 1) binary or continuous/multi-level time-varying treatments; 2) different types of outcomes (survival or continuous/binary end of follow-up); 3) data with competing events or truncation by death and loss to follow-up and other types of censoring events; 4) different options for handling competing events in the case of survival outcomes; 5) a random measurement/visit process; 6) joint interventions on multiple treatments; and 7) general incorporation of a priori knowledge of the data structure.

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**Expectile and Quantile Regression****expectreg**)

Expectile and quantile regression of models with nonlinear effects

e.g. spatial, random, ridge using least asymmetric weighed squares / absolutes

as well as boosting; also supplies expectiles for common distributions.