Description: A collection of time series is partially cointegrated if a linear combination of these time series can be found so that the residual spread is partially autoregressive - meaning that it can be represented as a sum of an autoregressive series and a random walk. This concept is useful in modeling certain sets of financial time series and beyond, as it allows for the spread to contain transient and permanent components alike. Partial cointegration has been introduced by Clegg and Krauss (2017) <doi:10.1080/14697688.2017.1370122>, along with a large-scale empirical application to financial market data. The partialCI package comprises estimation, testing, and simulation routines for partial cointegration models in state space. Clegg et al. (2017) <https://hdl.handle.net/10419/150014> provide an in in-depth discussion of the package functionality as well as illustrating examples in the fields of finance and macroeconomics.

Author: Vaclav Fidler and Nico Nagelkerke
Description: Computes Odds Ratio adjusted for a vector of possibly

Description: Provides a 'partialised' class that extends the partialising
function of 'purrr' by making it easier to change the arguments. This is
similar to the function-like object in 'Julia'
Packaged: 2022-05-03 14:10:30 UTC; uchidamizuki

Title: Optimisation with Particle Swarm Optimisation
Description: A toolbox to create a particle swarm optimisation (PSO), the package contains two classes: the Particle and the Particle Swarm, this two class is used to run the PSO with methods to easily print, plot and save the result.

Title: A Graph Based Particle Simulator Based on D3-Force
Description: Simulating particle movement in 2D space has many application. The
'particles' package implements a particle simulator based on the ideas
behind the 'd3-force' 'JavaScript' library. 'particles' implements all
forces defined in 'd3-force' as well as others such as vector fields, traps,
Imports: tidygraph, rlang, igraph, stats, magrittr, Rcpp, mgcv, digest,

Imports: tidygraph, rlang, igraph, stats, mgcv, digest, dplyr
Suggests: knitr, rmarkdown, ggraph, covr