dplyr, futile.logger, lubridate, mime, stringr, webutils

Run a Regenerative Markov chain in Parallel on a Cluster
This package provides functions for a user to run a
regenerative Markov chain in parallel, split it up into tours
and form Confidence Intervals on parameters of interest.

Locally weighted regression, semiparametric and
conditionally parametric regression, fourier and cubic spline
functions, GMM and linearized spatial logit and probit,
k-density functions and counterfactuals, subcenter
identification, misclassification probit, nonparametric
quantile regression and conditional density functions,
Machado-Mata decomposition for quantile regressions, spatial AR
model, repeat sales models, conditionally parametric logit and
probit
akima, car, classInt, lattice, locfit, maptools, mlogit,
quantreg, RColorBrewer, SparseM, spatstat, spdep

akima, lattice, locfit, quantreg, SparseM
car, classInt, maptools, mlogit, RColorBrewer, spatstat,
spdep

Locally weighted regression, semiparametric and
conditionally parametric regression, fourier and cubic spline
functions, GMM and linearized spatial logit and probit,
k-density functions and counterfactuals, nonparametric quantile
regression and conditional density functions, Machado-Mata
decomposition for quantile regressions, spatial AR model,
repeat sales models, conditionally parametric logit and probit
lattice, locfit, maptools, quantreg, RANN, SparseM
car, classInt, mlogit, RColorBrewer, spatstat, spdep

The 1001 time series from the M-competition (Makridakis et
al. 1982) and the 3003 time series from the IJF-M3 competition
(Makridakis and Hibon, 2000).
R (>= 2.0.0), graphics, stats, tseries, forecast

R (>= 2.10), graphics, stats, tseries, forecast

Rob J Hyndman with assistance from Muhammad Akram and Christoph
Bergmeir.

The 1001 time series from the M-competition (Makridakis et al. 1982) <DOI:10.1002/for.3980010202> and the 3003 time series from the IJF-M3 competition (Makridakis and Hibon, 2000) <DOI:10.1016/S0169-2070(00)00057-1>.
Rob J Hyndman with assistance from Muhammad Akram, Christoph Bergmeir and Mitchell O'Hara-Wild