Depends: R (>= 2.5.1),leaps, locfit, mgcv, tseries
Description: Contains R functions and datasets detailed in the book
"Time Series Analysis with Applications in R (second edition)"
Packaged: Sat Jul 31 18:02:10 2010; root

"Time Series Analysis with Applications in R (second edition)" by Jonathan Cryer and Kung-Sik Chan.
X-CRAN-Comment: Orphaned and corrected in 2020-05-03 as check problems

Title: Time Series Artificial Neural Network
Authors@R: c(person("Md", "Yeasin", role = c("aut","cre"), email = "yeasin.iasri@gmail.com"),
person("Ranjit Kumar", "Paul", role = "aut", email = "ranjitstat@gmail.com"),
person("Dipro", "Sinha", role = "aut", email = "diprosinha@gmail.com"))
Description: The best ANN structure for time series data analysis is a demanding need in the present era.
This package will find the best-fitted ANN model based on forecasting accuracy.
The optimum size of the hidden layers was also determined after determining the number of lags to be included.
This package has been developed using the algorithm of Paul and Garai (2021) <doi:10.1007/s00500-021-06087-4>.

Description: Time series aggregation for incomplete time series data.
Author: JS Lessels. Faculty of Agriculture, Food and Natural Resources.
Maintainer: Jason Scott Lessels <jason.lessels@sydney.edu.au>
Packaged: 2010-06-09 00:01:29 UTC; jasonlessels

Packaged: 2010-06-12 04:21:48 UTC; jasonlessels

Collate: 'daysAgg.R' 'hoursAgg.R' 'monthsAgg.R' 'timeSeries.R'

Title: Tools for Causal Inference with Possibly Invalid Instrumental
Description: Two stage curvature identification with machine learning for causal
inference in settings when instrumental variable regression is not suitable
because of potentially invalid instrumental variables. Based on Guo and
Buehlmann (2022) "Two Stage Curvature Identification with Machine Learning:
Causal Inference with Possibly Invalid Instrumental Variables"
Suggests: fda, MASS, testthat (>= 3.0.0), withr
Imports: xgboost, Rfast, stats, ranger, parallel
Author: David Carl [aut, cre] (<https://orcid.org/0000-0003-0615-0133>),