Generates simulated paths from various financial stochastic volatility models
with jumps and applies the discrete nonlinear filter (DNF) of Kitagawa (1987) <doi:10.1080/01621459.1987.10478534> to
compute likelihood evaluations, filtering distribution estimates, and maximum likelihood parameter estimates.
The algorithm is implemented following the work of Bégin and Boudreault (2021) <doi:10.1080/10618600.2020.1840995>.

Map experimental data on custom-made SVG file
This package allows you to display experimental data on a
custom-made SVG image.

This package allows you to display experimental data on a custom-made SVG image.
R (>= 2.10.0), rJava, XML, grDevices, utils

Hoksan Yip, Patrick J.F. Groenen, Georgi Nalbantov
Implements the SVM-Maj algorithm to train data with
Support Vector Machine, this algorithm uses two efficient
updates, one for linear kernel and one for the nonlinear
kernel.

Implements the SVM-Maj algorithm to train data with support vector machine
as described in Groenen et al. (2008) <doi:10.1007/s11634-008-0020-9>.
This algorithm uses two efficient updates, one for linear kernel and one
for the nonlinear kernel.
reshape2, scales, gridExtra, dplyr, ggplot2, kernlab

Causal Effect Estimation and Diagnostics with Support Vector
Machines
Causal effect estimation in observational data often requires identifying a set of untreated observations that are comparable to some treated group of interest.  This package provides a suite of functions for identifying such a set of observations and for implementing standard and new diagnostics tools.  The primary function, svmmatch(), uses support vector machines to identify a region of common support between treatment and control groups.  A sensitivity analysis, balance checking, and assessment of the region of overlap between treated and control groups is included.  The Bayesian implementation allows for recovery of uncertainty estimates for the treatment effect and all other parameters.

Determines networks of significant synchronization between the discrete states of nodes; see Tumminello et al <doi:10.1371/journal.pone.0017994>.