Showing 4 of total 4 results (show query)
cvxgrp
CVXR:Disciplined Convex Optimization
An object-oriented modeling language for disciplined convex programming (DCP) as described in Fu, Narasimhan, and Boyd (2020, <doi:10.18637/jss.v094.i14>). It allows the user to formulate convex optimization problems in a natural way following mathematical convention and DCP rules. The system analyzes the problem, verifies its convexity, converts it into a canonical form, and hands it off to an appropriate solver to obtain the solution. Interfaces to solvers on CRAN and elsewhere are provided, both commercial and open source.
Maintained by Anqi Fu. Last updated 5 months ago.
207 stars 12.89 score 768 scripts 51 dependents
geodacenter
rgeoda:R Library for Spatial Data Analysis
Provides spatial data analysis functionalities including Exploratory Spatial Data Analysis, Spatial Cluster Detection and Clustering Analysis, Regionalization, etc. based on the C++ source code of 'GeoDa', which is an open-source software tool that serves as an introduction to spatial data analysis. The 'GeoDa' software and its documentation are available at <https://geodacenter.github.io>.
Maintained by Xun Li. Last updated 21 days ago.
dataanalysisgeodageospatialcpp
73 stars 7.85 score 179 scripts 1 dependents
centerforstatistics-ugent
xnet:Two-Step Kernel Ridge Regression for Network Predictions
Fit a two-step kernel ridge regression model for predicting edges in networks, and carry out cross-validation using shortcuts for swift and accurate performance assessment (Stock et al, 2018 <doi:10.1093/bib/bby095> ).
Maintained by Joris Meys. Last updated 4 years ago.
11 stars 5.30 score 12 scripts
rjdverse
rjd3filters:Trend-Cycle Extraction with Linear Filters based on JDemetra+ v3.x
This package provides functions to build and apply symmetric and asymmetric moving averages (= linear filters) for trend-cycle extraction. In particular, it implements several modern approaches for real-time estimates from the viewpoint of revisions and time delay in detecting turning points. It includes the local polynomial approach of Proietti and Luati (2008), the Reproducing Kernel Hilbert Space (RKHS) of Dagum and Bianconcini (2008) and the Fidelity-Smoothness-Timeliness approach of Grun-Rehomme, Guggemos, and Ladiray (2018). It is based on Java libraries developped in 'JDemetra+' (<https://github.com/jdemetra>), time series analysis software.
Maintained by Alain Quartier-la-Tente. Last updated 27 days ago.
3 stars 5.19 score 77 scripts 3 dependents