Install Packages from Snapshots on the Checkpoint Server for Reproducibility (checkpoint)
The goal of checkpoint is to solve the problem of package reproducibility in R. Specifically, checkpoint allows you to install packages as they existed on CRAN on a specific snapshot date as if you had a CRAN time machine. To achieve reproducibility, the checkpoint() function installs the packages required or called by your project and scripts to a local library exactly as they existed at the specified point in time. Only those packages are available to your project, thereby avoiding any package updates that came later and may have altered your results. In this way, anyone using checkpoint’s checkpoint() can ensure the reproducibility of your scripts or projects at any time. To create the snapshot archives, once a day (at midnight UTC) we refresh the Austria CRAN mirror, on the “Managed R Archived Network” server (http://mran.revolutionanalytics.com/). Immediately after completion of the rsync mirror process, we take a snapshot, thus creating the archive. Snapshot archives exist starting from 2014-09-17.

Spatial Analysis and Modeling (spatialEco)
Utilities to support spatial data manipulation, query, sampling and modeling. Functions include models for species population density, download utilities for climate and global deforestation spatial products, spatial smoothing, multivariate separability, point process model for creating pseudo-absences and subsampling, polygon and point-distance landscape metrics, auto-logistic model, sampling models, cluster optimization and statistical exploratory tools.

Generalized Elastic Nets (gelnet)
The package implements several extensions of the elastic net regularization scheme. These extensions include individual feature penalties for the L1 term and feature-feature penalties for the L2 term.

A Stacked Autoencoder Implementation with Interface to ‘neuralnet’ (SAENET)
This package implements a stacked sparse autoencoder for dimension reduction of features and pre-training of feed-forward neural networks with the neuralnet package. The package also includes a predict function for the stacked autoencoder object to generate the compressed representation of new data if required. For the purposes of this package, ‘stacked’ is defined in line with http://ufldl.stanford.edu/wiki/index.php/Stacked_Autoencoders . The underlying sparse autoencoder is defined in the documentation of ‘autoencoder’.

Network Generator for Combinatorial Graph Problems (netgen)
Methods for the generation of a wide range of network geographies, e.g., grid networks or clustered networks. Useful for the generation of benchmarking instances for the investigation of, e.g., Vehicle-Routing-Problems or Travelling Salesperson Problems.

Computation of Bayes Factors for Common Designs (BayesFactor)
A suite of functions for computing various Bayes factors for simple designs, including contingency tables, one- and two-sample designs, one-way designs, general ANOVA designs, and linear regression.

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