Package: marp 0.1.0

Jie Kang

marp: Model-Averaged Renewal Process

To implement a model-averaging approach with different renewal models, with a primary focus on forecasting large earthquakes. Based on six renewal models (i.e., Poisson, Gamma, Log-Logistics, Weibull, Log-Normal and BPT), model-averaged point estimates are calculated using AIC (or BIC) weights. Additionally, both percentile and studentized bootstrapped model-averaged confidence intervals are constructed. In comparison, point and interval estimation from the individual or "best" model (determined via model selection) can be retrieved.

Authors:Jie Kang [aut, cre, cph], Chris Scott [ctb], Albert Savary [ctb]

marp_0.1.0.tar.gz
marp_0.1.0.zip(r-4.5)marp_0.1.0.zip(r-4.4)marp_0.1.0.zip(r-4.3)
marp_0.1.0.tgz(r-4.4-any)marp_0.1.0.tgz(r-4.3-any)
marp_0.1.0.tar.gz(r-4.5-noble)marp_0.1.0.tar.gz(r-4.4-noble)
marp_0.1.0.tgz(r-4.4-emscripten)marp_0.1.0.tgz(r-4.3-emscripten)
marp.pdf |marp.html
marp/json (API)
NEWS

# Install 'marp' in R:
install.packages('marp', repos = c('https://kanji709.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/kanji709/marp/issues

On CRAN:

25 exports 1 stars 0.84 score 3 dependencies 2 scripts 217 downloads

Last updated 2 years agofrom:1066db6c30. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 27 2024
R-4.5-winOKAug 27 2024
R-4.5-linuxOKAug 27 2024
R-4.4-winOKAug 27 2024
R-4.4-macOKAug 27 2024
R-4.3-winOKAug 27 2024
R-4.3-macOKAug 27 2024

Exports:bpt_bstrpbpt_loglbpt_rpdlloggamma_bstrpgamma_loglgamma_rploglogis_bstrploglogis_loglloglogis_rplognorm_bstrplognorm_rplowerTmarpmarp_bstrpmarp_confintpercent_confintpllogpoisson_bstrppoisson_rpstudent_confintupperTweibull_bstrpweibull_loglweibull_rp

Dependencies:gtoolsstatmodVGAM

Readme and manuals

Help Manual

Help pageTopics
A function to generate (double) bootstrap samples and fit BPT renewal modelbpt_bstrp
A function to calculate the log-likelihood of BPT modelbpt_logl
A function to fit BPT renewal modelbpt_rp
Density function of Log-Logistics modeldllog
A function to generate (double) bootstrap samples and fit Gamma renewal modelgamma_bstrp
A function to calculate the log-likelihood of Gamma modelgamma_logl
A function to fit Gamma renewal modelgamma_rp
A function to generate (double) bootstrap samples and fit Log-Logistic renewal modelloglogis_bstrp
A function to calculate the log-likelihood of Log-Logistics modelloglogis_logl
A function to fit Log-Logistics renewal modelloglogis_rp
A function to generate (double) bootstrap samples and fit Log-Normal renewal modellognorm_bstrp
A function to fit Log-Normal renewal modellognorm_rp
An utility function to calculate upper limit of T statisticlowerT
A function to apply model-averaged renewal processmarp
A function to fit model-averaged renewal processmarp_bstrp
A function to apply model-averaged renewal processmarp_confint
A function to calculate percentile bootstrap confidence intervalpercent_confint
Probability function of Log-Logistics modelpllog
A function to generate (double) bootstrap samples and fit Poisson renewal modelpoisson_bstrp
A function to fit Poisson renewal modelpoisson_rp
A function to calculate Studentized bootstrap confidence intervalstudent_confint
An utility function to calculate lower limit of T statisticupperT
A function to generate (double) bootstrap samples and fit Weibull renewal modelweibull_bstrp
A function to calculate the log-likelihood of Weibull modelweibull_logl
A function to fit Weibull renewal model #' @import weibull_loglweibull_rp