* Blyth-Still-Casella Exact Binomial Confidence Intervals* (

**BlythStillCasellaCI**)

Computes Blyth-Still-Casella exact binomial confidence intervals based on a refining procedure proposed by George Casella (1986) <doi:10.2307/3314658>.

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**Genome-Wide Structural Equation Modeling****gwsem**)

Melds genome-wide association tests with structural equation modeling (SEM) using ‘OpenMx’. This package contains low-level C/C++ code to rapidly read genetic data encoded in U.K. Biobank or ‘plink’ formats. Prebuilt modeling options include one and two factor models. Alternately, analyses may utilize arbitrary, user-provided SEMs. See Verhulst, Maes, & Neale (2017) <doi:10.1007/s10519-017-9842-6> for details. An updated manuscript is in preparation.

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**Food Network Inference and Visualization****foodingraph**)

Displays a weighted undirected food graph from an adjacency matrix. Can perform confidence-interval bootstrap inference with mutual information or maximal information coefficient. Based on my Master 1 internship at the Bordeaux Population Health center. References : Reshef et al. (2011) <doi:10.1126/science.1205438>, Meyer et al. (2008) <doi:10.1186/1471-2105-9-461>, Liu et al. (2016) <doi:10.1371/journal.pone.0158247>.

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**Varying Coefficients****varycoef**)

Gives maximum likelihood estimation (MLE) method to estimate and predict spatially varying coefficient (SVC) Models. It supports covariance tapering by Furrer et al. (2006) <doi:10.1198/106186006X132178> to allow MLE on large data.

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**An Extension of the Taylor Diagram to Two-Dimensional Vector Data****SailoR**)

A new diagram for the verification of vector variables (wind, current, etc) generated by multiple models against a set of observations is presented in this package. It has been designed as a generalization of the Taylor diagram to two dimensional quantities. It is based on the analysis of the two-dimensional structure of the mean squared error matrix between model and observations. The matrix is divided into the part corresponding to the relative rotation and the bias of the empirical orthogonal functions of the data. The full set of diagnostics produced by the analysis of the errors between model and observational vector datasets comprises the errors in the means, the analysis of the total variance of both datasets, the rotation matrix corresponding to the principal components in observation and model, the angle of rotation of model-derived empirical orthogonal functions respect to the ones from observations, the standard deviation of model and observations, the root mean squared error between both datasets and the squared two-dimensional correlation coefficient. See the output of function UVError() in this package.