Some Functions to be Used as Shortcuts in RStudio (DescToolsAddIns)
RStudio as of recently offers the option to define addins and assign shortcuts to them. This package contains AddIns for a few most used functions in an analysts (at least mine) daily work (like str(), example(), plot(), head(), view(), Desc()). Most of these functions will get the current selection in RStudio’s editor window and send the specific command to the console while instantly executing it. Assigning shortcuts to these AddIns will spare you quite a few keystrokes.

Migration and Range Change Estimation in R (marcher)
A set of tools for likelihood-based estimation, model selection and testing of two- and three-range shift and migration models for animal movement data as described in Gurarie et al. (2017) <doi: 10.1111/1365-2656.12674>. Provided movement data (X, Y and Time), including irregularly sampled data, functions estimate the time, duration and location of one or two range shifts, as well as the ranging area and auto-correlation structure of the movment. Tests assess, for example, whether the shift was ‘significant’, and whether a two-shift migration was a true return migration.

Nonparametric and Semiparametric Mixture Estimation (nspmix)
Contains functions for maximum likelihood estimation of nonparametric and semiparametric mixture models.

Statistical Performance Measures to Evaluate Covariance Matrix Estimates (StatPerMeCo)
Statistical performance measures used in the econometric literature to evaluate conditional covariance/correlation matrix estimates (MSE, MAE, Euclidean distance, Frobenius distance, Stein distance, asymmetric loss function, eigenvalue loss function and the loss function defined in Eq. (4.6) of Engle et al. (2016) <doi:10.2139/ssrn.2814555>). Additionally, compute Eq. (3.1) and (4.2) of Li et al. (2016) <doi:10.1080/07350015.2015.1092975> to compare the factor loading matrix. The statistical performance measures implemented have been previously used in, for instance, Laurent et al. (2012) <doi:10.1002/jae.1248>, Amendola et al. (2015) <doi:10.1002/for.2322> and Becker et al. (2015) <doi:10.1016/j.ijforecast.2013.11.007>.

Optimized Kernel Regularized Least Squares (bigKRLS)
Functions for Kernel-Regularized Least Squares optimized for speed and memory usage are provided along with visualization tools. For working papers, sample code, and recent presentations visit <https://…/>.

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