* Estimation and Plotting of IDF Curves* (

**IDF**)

Intensity-duration-frequency (IDF) curves are a widely used analysis-tool in hydrology to assess extreme values of precipitation [e.g. Mailhot et al., 2007, <doi:10.1016/j.jhydrol.2007.09.019>]. The package ‘IDF’ provides a function to read precipitation data from German weather service (DWD) ‘webwerdis’ <http://…/webwerdis.html> files and Berlin station data from ‘Stadtmessnetz’ <http://…/index.html> files, and additionally IDF parameters can be estimated also from a given data.frame containing a precipitation time series. The data is aggregated to given levels yearly intensity maxima are calculated either for the whole year or given months. From these intensity maxima IDF parameters are estimated on the basis of a duration-dependent generalised extreme value distribution [Koutsoyannis et al., 1998, <doi:10.1016/S0022-1694(98)00097-3>]. IDF curves based on these estimated parameters can be plotted.

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**Convert a Matrix of Raw Values into Nice and Tidy Data****bioset**)

Functions to help dealing with raw data from measurements, like reading and transforming raw values organised in matrices, calculating and converting concentrations and calculating precision of duplicates / triplicates / … . It is compatible with and building on top of ‘tidyverse’-packages.

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**Nonlinear and Penalized Quantile Regression Coefficients Modeling****qrcmNP**)

Nonlinear and Penalized parametric modeling of quantile regression coefficient functions. Frumento P and Bottai M (2016) <doi:10.1111/biom.12410>.

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**Epistemic Network Analysis****rENA**)

ENA (Shaffer, D. W. (2017) Quantitative Ethnography. ISBN: 0578191687) is a method used to identify meaningful and quantifiable patterns in discourse or reasoning. ENA moves beyond the traditional frequency-based assessments by examining the structure of the co-occurrence, or connections in coded data. Moreover, compared to other methodological approaches, ENA has the novelty of (1) modeling whole networks of connections and (2) affording both quantitative and qualitative comparisons between different network models. Shaffer, D.W., Collier, W., & Ruis, A.R. (2016) <doi:10.18608/jla.2016.33.3>.

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**Granger Mediation Analysis****gma**)

Performs Granger mediation analysis (GMA) for time series. This package includes a single level GMA model and a two-level GMA model, for time series with hierarchically nested structure. The single level GMA model for the time series of a single participant performs the causal mediation analysis which integrates the structural equation modeling and the Granger causality frameworks. A vector autoregressive model of order p is employed to account for the spatiotemporal dependencies in the data. Meanwhile, the model introduces the unmeasured confounding effect through a nonzero correlation parameter. Under the two-level model, by leveraging the variabilities across participants, the parameters are identifiable and consistently estimated based on a full conditional likelihood or a two-stage method. See Zhao, Y., & Luo, X. (2017), Granger Mediation Analysis of Multiple Time Series with an Application to fMRI, <arXiv:1709.05328> for details.

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**Optimal Categorisation of Continuous Variables in Prediction Models****CatPredi**)

Allows the user to categorise a continuous predictor variable in a logistic or a Cox proportional hazards regression setting, by maximising the discriminative ability of the model. I Barrio, I Arostegui, MX Rodriguez-Alvarez, JM Quintana (2015) <doi:10.1177/0962280215601873>. I Barrio, MX Rodriguez-Alvarez, L Meira-Machado, C Esteban, I Arostegui (2017) <https://…/41.1.3.barrio-etal.pdf>.