Title: Descent-Based Calibrated Optimal Direct Estimation
Author: Chi Seng Pun, Matthew Zakharia Hadimaja
Description: Algorithms for solving a self-calibrated l1-regularized quadratic programming problem without parameter tuning. The algorithm, called DECODE, can handle high-dimensional data without cross-validation. It is found useful in high dimensional portfolio selection (see Pun (2018) <https://ssrn.com/abstract=3179569>) and large precision matrix estimation and sparse linear discriminant analysis (see Pun and Hadimaja (2019) <https://ssrn.com/abstract=3422590>).

Title: Low-Level Interface to the '.NET' Virtual Machine Along the
Description: Low-level interface to '.NET' virtual machine along the lines of the R C .call interface.  Can create '.NET' object, call methods, get or set properties, call static functions, etc.
SystemRequirements: mono 4.x or higher on OSX / Linux, .NET 4.x or
higher on Windows, 'msbuild' and 'nuget' available in the path

Title: Subset Searching Algorithm Using DPP Greedy MAP
Description: Given item set, item representation vector, and item ratings,
find a subset with better relevance-diversity trade-off. Also provide
machine learning algorithm to learn item representations maximizing
References: [1]Laming Chen, Guoxin Zhang, and Hanning Zhou(2017)<https://lsrs2017.files.wordpress.com/2017/08/lsrs_2017_lamingchen.pdf>
[2]Laming Chen, Guoxin Zhang, and Hanning Zhou(2018)<https://papers.nips.cc/paper/2018/file/dbbf603ff0e99629dda5d75b6f75f966-Paper.pdf>
[3]Wilhelm, Mark & Ramanathan, Ajith & Bonomo, Alexander & Jain, Sagar & Chi, Ed & Gillenwater, Jennifer(2018)<doi:10.1145/3269206.3272018>.
Packaged: 2021-05-19 13:32:12 UTC; menglingwu
Author: Sining Ng [aut, cre] (<https://orcid.org/0000-0003-3463-707X>),
Rasmus Munk Larsen [cph] (https://gitlab.com/rmlarsen1),

Packaged: 2021-05-31 12:48:06 UTC; menglingwu

Title: Applications of Empirical Dynamic Modeling from Time Series
Description: Contains C++ compiled objects that use time delay embedding to perform state-space reconstruction and nonlinear forecasting and an R interface to those objects using 'Rcpp'. It supports both the simplex projection method from Sugihara & May (1990) <DOI:10.1038/344734a0> and the S-map algorithm in Sugihara (1994) <DOI:10.1098/rsta.1994.0106>. In addition, this package implements convergent cross mapping as described by Sugihara et al. (2012) <DOI:10.1126/science.1227079>.