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countSTAR
View on CRAN: Click
here
Download and install countSTAR package within the R console
Install from CRAN:
install.packages("countSTAR")
Install from Github:
library("remotes")
install_github("cran/countSTAR")
Install by package version:
library("remotes")
install_version("countSTAR", "1.0.2")
Attach the package and use:
library("countSTAR")
Maintained by
Brian King
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-04-10
Latest Update: 2023-06-30
Description:
For Bayesian and classical inference and prediction with count-valued data,
Simultaneous Transformation and Rounding (STAR) Models provide a flexible, interpretable,
and easy-to-use approach. STAR models the observed count data using a rounded
continuous data model and incorporates a transformation for greater flexibility.
Implicitly, STAR formalizes the commonly-applied yet incoherent procedure of
(i) transforming count-valued data and subsequently
(ii) modeling the transformed data using Gaussian models.
STAR is well-defined for count-valued data, which is reflected in predictive accuracy,
and is designed to account for zero-inflation, bounded or censored data, and over- or underdispersion.
Importantly, STAR is easy to combine with existing MCMC or point estimation
methods for continuous data, which allows seamless adaptation of continuous data
models (such as linear regressions, additive models, BART, random forests,
and gradient boosting machines) for count-valued data. The package also includes several
methods for modeling count time series data, namely via warped Dynamic Linear Models.
For more details and background on these methodologies, see the works of
Kowal and Canale (2020) ,
Kowal and Wu (2022) ,
King and Kowal (2022) , and
Kowal and Wu (2023) .
How to cite:
Brian King (2023). countSTAR: Flexible Modeling of Count Data. R package version 1.0.2, https://cran.r-project.org/web/packages/countSTAR
Previous versions and publish date:
1.0.1 (2023-04-10 16:30)
Other packages that cited countSTAR R package
View countSTAR citation profile
Other R packages that countSTAR depends,
imports, suggests or enhances
Functions, R codes and Examples using
the countSTAR R package
Some associated functions: BrentMethod . a_j . bam_star . bart_star . bart_star_ispline . blm_star . blm_star_bnpgibbs . blm_star_exact . computeTimeRemaining . confint.lmstar . credBands . ergMean . expectation2_gRcpp . expectation_gRcpp . expectation_identity . expectation_log . expectation_sqrt . g_bc . g_bnp . g_cdf . g_inv_approx . g_inv_bc . gbm_star . genEM_star . genMCMC_star . genMCMC_star_ispline . getEffSize . init_bam_orthog . init_bam_thin . init_lm_gprior . init_lm_hs . init_lm_ridge . init_params_mean . interval_gRcpp . invlogit . lm_star . logLikePointRcpp . logLikeRcpp . logit . plot_coef . plot_fitted . plot_pmf . pmaxRcpp . pminRcpp . predict.lmstar . pvals . randomForest_star . roaches . round_floor . rtruncnormRcpp . sampleFastGaussian . sample_bam_orthog . sample_bam_thin . sample_lm_gprior . sample_lm_hs . sample_lm_ridge . sample_params_mean . simBaS . simulate_nb_friedman . simulate_nb_lm . splineBasis . spline_star . spline_star_exact . truncnorm_mom . uni.slice . update_struct . warpDLM .
Some associated R codes: RcppExports.R . STAR_Bayesian.R . STAR_frequentist.R . data.R . helper_functions.R . internal_functions.R . warpDLM.R . Full countSTAR package functions and examples
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