Uses both ridge and lasso penalties (and extensions) to penalize
specific parameters in structural equation models. The package offers additional
cost functions, cross validation, and other extensions beyond traditional structural
equation models. Also contains a function to perform exploratory mediation (XMed).

Ross Jacobucci[aut,cre],
Kevin J. Grimm [ctb],
Andreas M. Brandmaier [ctb],
Sarfaraz Serang [ctb],
Rogier A. Kievit [ctb],
Florian Scharf [ctb]

Ross Jacobucci[aut,cre],
Kevin J. Grimm [ctb],
Andreas M. Brandmaier [ctb],
Sarfaraz Serang [ctb],
Rogier A. Kievit [ctb],
Florian Scharf [ctb],
Xiaobei Li [ctb]
snowfall, MASS, GA, caret, glmnet, ISLR, lbfgs, numDeriv,
psych, knitr, nloptr, NlcOptim, optimx, semPlot, colorspace,
plyr, matrixStats, stringr

Ross Jacobucci[aut,cre],
Kevin J. Grimm [ctb],
Andreas M. Brandmaier [ctb],
Sarfaraz Serang [ctb],
Rogier A. Kievit [ctb],
Florian Scharf [ctb],
Xiaobei Li [ctb],
Ai Ye [ctb]
snowfall, markdown, MASS, GA, caret, glmnet, ISLR, lbfgs,
numDeriv, psych, knitr, nloptr, NlcOptim, optimx, semPlot,
colorspace, plyr, matrixStats, stringr

Non-Parametric Bayesian Spectrum Estimation for Multirate Data
c(person("Ben", "Powell", role=c("aut", "cre"),
email="ben.powell@bristol.ac.uk"),
person("Guy", "Nason", role=c("aut"),
email="g.p.nason@bristol.ac.uk"))
Computes linear Bayesian spectral estimates from multirate
data for second-order stationary time series. Provides credible
intervals and methods for plotting various spectral estimates.

c(person("Ben", "Powell", role=c("aut", "cre"),
email="ben.powell@york.ac.uk"),
person("Guy", "Nason", role=c("aut"),
email="g.nason@imperial.ac.uk"))

Computes linear Bayesian spectral estimates from multirate	data for second-order stationary time series. Provides credible intervals and methods for plotting various spectral estimates. Please see the paper `Should we sample a time series more frequently?' (doi below) for a full description of and motivation for the methodology.

Detect and Test Regular Sequences and Subsequences
For a sequence of event occurence times, we are interested
in finding subsequences in it that are too "regular". We define
regular as being significantly different from a homogeneous
Poisson process. The departure from the Poisson process is
measured using a L1 distance. See Di and Perlman 2007 for more
details.