Description: Knowledge domain visualization using 'mpa' co-words method as the word clustering method and network graphs with 'D3.js' library as visualization tool.
Imports: htmlwidgets, igraph, mpa, networkD3, rvest, stringr, tm, xml2

Title: Kernel-Smoothed Nonparametric Methods for Environmental Exposure
Author: Yuchen Yang <yuchen.y@uky.edu>, Li Chen <lichenuky@uky.edu>
Description: Calculate the kernel-smoothed nonparametric estimator for the exposure distribution in presence of detection limits.

Title: Expectile Regression in Reproducing Kernel Hilbert Space
Description: An efficient algorithm inspired by majorization-minimization principle for solving the entire solution path of a flexible nonparametric expectile regression estimator constructed in a reproducing kernel Hilbert space.

Title: Multivariate Kalman filter and smoother, simulation smoother and
Description: Fast multivariate Kalman filter, smoother, simulation
smoother and forecasting. Uses exact diffuse initialisation
when distributions of some or all elements of initial state

Description: Package KFAS provides functions for fast Kalman filtering,
smoothing, forecasting and simulation of multivariate
time-variant state space models. All functions can use exact
diffuse initialisation when distributions of some or all
elements of initial state vector are unknown. Functions use
sequential processing algorithm, which is faster than standard
approach, and it also allows singularity of prediction error

state and disturbance smoothing, forecasting and simulation of
multivariate time-variant state space models. All functions can
use exact diffuse initialisation when distributions of some or
all elements of initial state vector are unknown. Functions use

all elements of initial state vector are unknown. Filtering,
state smoothing and simulation functions use sequential
processing algorithm, which is faster than standard approach,
and it also allows singularity of prediction error variance

Package: KFAS
Version: 0.4.5
Date: 2009-10-19
Title: Multivariate Kalman filter and smoother, simulation smoother and
forecasting of gaussian state space models. Approximate
likelihood of linear non-gaussian state space models.
Author: Jouni Helske <jouni.v.t.lehtonen@jyu.fi>
Maintainer: Jouni Helske <jouni.v.t.lehtonen@jyu.fi>
Depends: R (>= 2.8.0)
Description: Package KFAS provides functions for fast Kalman filtering,
state and disturbance smoothing, forecasting and simulation of
multivariate time-variant state space models. All functions can
use exact diffuse initialisation when distributions of some or
all elements of initial state vector are unknown. Filtering,
state smoothing and simulation functions use sequential
processing algorithm, which is faster than standard approach,
and it also allows singularity of prediction error variance
matrix. KFAS also contains function which computes approximate
likelihood of linear non-gaussian state space model.
License: GPL (>= 2)
Packaged: 2009-10-19 10:35:21 UTC; jovetale
Repository: CRAN
Date/Publication: 2009-10-19 16:16:27