* Confidence Intervals Utilizing Uncertain Prior Information* (

**ciuupi**)

Computes a confidence interval for a specified linear combination of the regression parameters in a linear regression model with iid normal errors with known variance when there is uncertain prior information that a distinct specified linear combination of the regression parameters takes a given value. This confidence interval, found by numerical constrained optimization, has the required minimum coverage and utilizes this uncertain prior information through desirable expected length properties. This confidence interval has the following three practical applications. Firstly, if the error variance has been accurately estimated from previous data then it may be treated as being effectively known. Secondly, for sufficiently large (dimension of the response vector) minus (dimension of regression parameter vector), greater than or equal to 30 (say), if we replace the assumed known value of the error variance by its usual estimator in the formula for the confidence interval then the resulting interval has, to a very good approximation, the same coverage probability and expected length properties as when the error variance is known. Thirdly, some more complicated models can be approximated by the linear regression model with error variance known when certain unknown parameters are replaced by estimates. This confidence interval is described in Kabaila, P. and Mainzer, R. (2017) <arXiv:1708.09543>, and is a member of the family of confidence intervals proposed by Kabaila, P. and Giri, K. (2009) <doi:10.1016/j.jspi.2009.03.018>.

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**Automatic Codebooks from Survey Metadata Encoded in Attributes****codebook**)

Easily automate the following tasks to describe data frames: computing reliabilities (internal consistencies, retest, multilevel) for psychological scales, summarise the distributions of scales and items graphically and using descriptive statistics, combine this information with metadata (such as item labels and labelled values) that is derived from R attributes. To do so, the package relies on ‘rmarkdown’ partials, so you can generate HTML, PDF, and Word documents. Codebooks are also available as tables (CSV, Excel, etc.).

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**Power Calculations for RD Designs****rdpower**)

The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. The ‘rdpower’ package provides tools to perform power and sample size calculations in RD designs: rdpower() calculates the power of an RD design and rdsampsi() calculates the required sample size to achieve a desired power. See Cattaneo, Titiunik and Vazquez-Bare (2018) <https://…o-Titiunik-VazquezBare_2018_Stata.pdf> for further methodological details.

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**Tune Random Forest of the ‘ranger’ Package****tuneRanger**)

Tuning random forest with one line. The package is mainly based on the packages ‘ranger’ and ‘mlrMBO’.

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**Multilevel Propensity Score Analysis****multilevelPSA**)

Conducts and visualizes propensity score analysis for multilevel, or clustered data. Bryer & Pruzek (2011) <doi:10.1080/00273171.2011.636693>.