A7-time-series-panel-series: `collapse` Time-Series and Panel-Series

Description

collapse provides the following functions to work with time-dependent data:

flag, and the lag- and lead- operators L and F are S3 generics to efficiently compute sequences of lags and leads on ordered or unordered time-series and panel data.
fdiff, fgrowth, and the operators D and G are S3 generics to efficiently compute sequences of suitably lagged and iterated differences and growth rates or log-differences on ordered or unordered time-series and panel data. They can also be used to compute forward (leaded) differences or growth rates.
psmat is an S3 generic to efficiently expand panel-vectors or plm::pseries and data.frame's or plm::pdata.frame's to panel-series matrices and 3D arrays, respectively.
psacf, pspacf and psccf are S3 generics to compute estimates of the auto-, partial auto- and cross- correlation or covariance functions for panel-vectors or plm::pseries, and multivariate versions for data.frame's or plm::pdata.frame's.

S3 Generic	Methods	Description
`flag/L/F`	`default, matrix, data.frame, pseries, pdata.frame, grouped_df`	compute (sequences of) lags and leads
`fdiff/D`	`default, matrix, data.frame, pseries, pdata.frame, grouped_df`	compute (sequences of lagged and iterated) differences
`fgrowth/G`	`default, matrix, data.frame, pseries, pdata.frame, grouped_df`	compute (sequences of lagged and iterated) growth rates or log-differences
`psmat`	`default, pseries, data.frame, pdata.frame`	convert panel-data to matrix/array
`psacf`	`default, pseries, data.frame, pdata.frame`	compute ACF on panel-data
`pspacf`	`default, pseries, data.frame, pdata.frame`	compute PACF on panel-data