K Nearest Neighbor Forecasting with a Tailored Similarity Metric
Functions to implement K Nearest Neighbor forecasting using a weighted similarity metric tailored to the problem of forecasting univariate time series where recent observations, seasonal patterns, and exogenous predictors are all relevant in predicting future observations of the series in question. For more information on the formulation of this similarity metric please see Trupiano (2021) <https://github.com/mtrupiano1/knnwtsim/blob/main/README.md>.

Functions to implement K Nearest Neighbor forecasting using a weighted similarity metric tailored to the problem of forecasting univariate time series where recent observations, seasonal patterns, and exogenous predictors are all relevant in predicting future observations of the series in question. For more information on the formulation of this similarity metric please see Trupiano (2021) <arXiv:2112.06266>.

Rina Foygel Barber, Emmanuel Candes, Evan Patterson
The knockoff filter is a procedure for controlling the false
discovery rate (FDR) when performing variable selection. For more information,
see the website below and the accompanying paper.

Rina Barber, Emmanuel Candes, Evan Patterson
The knockoff filter for controlling the false discovery rate

Knockoff Filter for Controlling the False Discovery Rate

The Knockoff Filter for Controlled Variable Selection
The knockoff filter is a general procedure for controlling the false discovery rate (FDR)
when performing variable selection.
For more information, see the website below and the accompanying paper: Candes et al.,
"Panning for Gold: Model-free Knockoffs for High-dimensional Controlled Variable Selection",
2016, <arXiv:1610.02351>.

c(person("Rina", "Foygel Barber", role = c("ctb"),
comment="Development of the original Fixed-X Knockoffs"),
person("Emmanuel", "Candes", role = c("ctb"),
comment="Development of Model-X Knockoffs and original Fixed-X Knockoffs"),
person("Lucas", "Janson", role = c("ctb"),
comment="Development of Model-X Knockoffs"),
person("Evan", "Patterson", role = c("aut"),
comment="Original R package for the original Fixed-X Knockoffs"),
person("Matteo", "Sesia", role = c("aut","cre"),
comment="R package for Model-X Knockoffs", email="msesia@stanford.edu"))
The knockoff filter is a general procedure for controlling the false discovery rate (FDR)
when performing variable selection.
For more information, see the website below and the accompanying paper: Candes et al.,
"Panning for Gold: Model-X Knockoffs for High-dimensional Controlled Variable Selection",
2016, <arXiv:1610.02351>.
Rdsdp, Matrix, corpcor, glmnet, RSpectra, gtools, utils
Rina Foygel Barber [ctb] (Development of the original Fixed-X
Knockoffs),
Emmanuel Candes [ctb] (Development of Model-X Knockoffs and original
Fixed-X Knockoffs),
Lucas Janson [ctb] (Development of Model-X Knockoffs),
Evan Patterson [aut] (Original R package for the original Fixed-X
Knockoffs),
Matteo Sesia [aut, cre] (R package for Model-X Knockoffs)