Estimating Long Memory Parameter using Wavelet
Sandipan Samanta and Dr. Ranjit Kumar Paul
Estimation of the long memory parameter using wavelets. Other estimation techniques like
GPH (Geweke and Porter-Hudak,1983, <DOI:10.1111/j.1467-9892.1983.tb00371.x>)
and Semiparametric methods(Robinson, P. M.,1995, <DOI:10.1214/aos/1176324317>) also have included.

Weakly Associated Vectors (WAVE) Sampling
c(
person("Raphaël", "Jauslin", email = "raphael.jauslin@unine.ch",role = c("aut", "cre"),
comment = c(ORCID = "0000-0003-1088-3356")
),
person("Yves", "Tillé",role = c("aut"),
comment = c(ORCID = "0000-0003-0904-5523"))
)
Spatial data are generally auto-correlated, meaning that if two
units selected are close to each other, then it is likely that they share the
same properties. For this reason, when sampling in the population it is often
needed that the sample is well spread over space. A new method to draw a sample
from a population with spatial coordinates is proposed. This method is called
wave (Weakly Associated Vectors) sampling. It uses the less correlated vector
to a spatial weights matrix to update the inclusion probabilities vector
into a sample.
knitr, rmarkdown, ggplot2, ggvoronoi, sampling,
BalancedSampling, sp, sf, stats

Spatial data are generally auto-correlated, meaning that if two
units selected are close to each other, then it is likely that they share the
same properties. For this reason, when sampling in the population it is often
needed that the sample is well spread over space. A new method to draw a sample
from a population with spatial coordinates is proposed. This method is called
wave (Weakly Associated Vectors) sampling. It uses the less correlated vector
to a spatial weights matrix to update the inclusion probabilities vector
into a sample. For more details see Raphaël Jauslin and Yves Tillé (2019) <arXiv:1910.13152>.

Spatial data are generally auto-correlated, meaning that if two
units selected are close to each other, then it is likely that they share the
same properties. For this reason, when sampling in the population it is often
needed that the sample is well spread over space. A new method to draw a sample
from a population with spatial coordinates is proposed. This method is called
wave (Weakly Associated Vectors) sampling. It uses the less correlated vector
to a spatial weights matrix to update the inclusion probabilities vector
into a sample. For more details see Raphaël Jauslin and Yves Tillé (2019) <doi:10.1007/s13253-020-00407-1>.
knitr, rmarkdown, ggplot2, sampling, BalancedSampling, stats