Package: yahtsee 0.0.0.9000

yahtsee: Yet Another Hierachical Time Series Extension and Expansion

An opinionated approach to building hierarchical time series models in R using INLA and inlabru.

Authors:Nicholas Tierney [aut, cre], Aarathy Babu [aut], Mitchell O'Hara-Wild [ctb]

# Install 'yahtsee' in R:

install.packages('yahtsee', repos = c('https://njtierney.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

On CRAN:

Last updated from:94e4f8cb5e. Checks:7 ERROR, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel	ERROR	142
source / vignettes	OK	251
linux-release	ERROR	150
macos-release	ERROR	102
macos-oldrel	ERROR	104
windows-devel	ERROR	110
windows-release	ERROR	105
windows-oldrel	ERROR	104
wasm-release	OK	152

Last update: 2021-06-11
Started: 2021-06-11

Help page	Topics
Helper function to add group ID information	add_group_id
Create object of class "hts_inla"	as_hts_inla
augment: Add predictions and residuals to data	augment.hts_inla
Helper function to show the available likelihoods that can be used	available_likelihoods
create formula to pass to inlabru::bru	build_bru_formula
Create data to be used when fitting a model with fit_hts	create_hts_data
Fit a hierarchical time series model	fit_hts
Extract fitted values from 'hts_inla' model object	fitted.hts_inla
Check if formula variables are present in data	formula_vars_in_data
Glance: A single row summary of a model fit	glance.hts_inla
Build hts call	hts_builder
Example model for use in testing and examples	hts_example_model
Install yahtsee dependencies	install_yahtsee_deps
Prevalence Rate data of Malaria in Africa	malaria_africa_ts
Predict from hts_inla model	predict.hts_inla
Create prediction data	prediction_data
Tidy: summarise a model's statistical findings	tidy.hts_inla
Who Regions	who_regions
Checks if system requirements are present	yahtsee_sitrep