Showing 11 of total 11 results (show query)
cubiczebra
TPMplt:Tool-Kit for Dynamic Materials Model and Thermal Processing Maps
Provides a simple approach for constructing dynamic materials modeling suggested by Prasad and Gegel (1984) <doi:10.1007/BF02664902>. It can easily generate various processing-maps based on this model as well. The calculation result in this package contains full materials constants, information about power dissipation efficiency factor, and rheological properties, can be exported completely also, through which further analysis and customized plots will be applicable as well.
Maintained by Chen Zhang. Last updated 6 months ago.
2 stars 4.76 score 29 scripts
asmahani
MfUSampler:Multivariate-from-Univariate (MfU) MCMC Sampler
Convenience functions for multivariate MCMC using univariate samplers including: slice sampler with stepout and shrinkage (Neal (2003) <DOI:10.1214/aos/1056562461>), adaptive rejection sampler (Gilks and Wild (1992) <DOI:10.2307/2347565>), adaptive rejection Metropolis (Gilks et al (1995) <DOI:10.2307/2986138>), and univariate Metropolis with Gaussian proposal.
Maintained by Alireza S. Mahani. Last updated 2 years ago.
3.08 score 20 scripts 2 dependents
convfunctimeseries
NTS:Nonlinear Time Series Analysis
Simulation, estimation, prediction procedure, and model identification methods for nonlinear time series analysis, including threshold autoregressive models, Markov-switching models, convolutional functional autoregressive models, nonlinearity tests, Kalman filters and various sequential Monte Carlo methods. More examples and details about this package can be found in the book "Nonlinear Time Series Analysis" by Ruey S. Tsay and Rong Chen, John Wiley & Sons, 2018 (ISBN: 978-1-119-26407-1).
Maintained by Xialu Liu. Last updated 2 years ago.
2 stars 2.94 score 48 scripts
gabrielgrandemagne
CureDepCens:Dependent Censoring Regression Models with Cure Fraction
Cure dependent censoring regression models for long-term survival multivariate data. These models are based on extensions of the frailty models, capable to accommodating the cure fraction and the dependence between failure and censoring times, with Weibull and piecewise exponential marginal distributions. Theoretical details regarding the models implemented in the package can be found in Schneider et al. (2022) <doi:10.1007/s10651-022-00549-0>.
Maintained by Silvana Schneider. Last updated 2 years ago.
2.74 score 11 scriptsgabrielgrandemagne
DepCens:Dependent Censoring Regression Models
Dependent censoring regression models for survival multivariate data. These models are based on extensions of the frailty models, capable to accommodating the dependence between failure and censoring times, with Weibull and piecewise exponential marginal distributions. Theoretical details regarding the models implemented in the package can be found in Schneider et al. (2019) <doi:10.1002/bimj.201800391>.
Maintained by Silvana Schneider. Last updated 2 years ago.
1 stars 2.70 score 7 scripts
asmahani
DBR:Discrete Beta Regression
Bayesian Beta Regression, adapted for bounded discrete responses, commonly seen in survey responses. Estimation is done via Markov Chain Monte Carlo sampling, using a Gibbs wrapper around univariate slice sampler (Neal (2003) <DOI:10.1214/aos/1056562461>), as implemented in the R package MfUSampler (Mahani and Sharabiani (2017) <DOI: 10.18637/jss.v078.c01>).
Maintained by Alireza Mahani. Last updated 2 years ago.
2.08 score 12 scripts
cran
longitudinalANAL:Longitudinal Data Analysis
Regression analysis of mixed sparse synchronous and asynchronous longitudinal covariates. Please cite the manuscripts corresponding to this package: Sun, Z. et al. (2023) <arXiv:2305.17715> and Liu, C. et al. (2023) <arXiv:2305.17662>.
Maintained by Zhuowei Sun. Last updated 1 years ago.
1.00 scoreasmahani
BSGW:Bayesian Survival Model with Lasso Shrinkage Using Generalized Weibull Regression
Bayesian survival model using Weibull regression on both scale and shape parameters. Dependence of shape parameter on covariates permits deviation from proportional-hazard assumption, leading to dynamic - i.e. non-constant with time - hazard ratios between subjects. Bayesian Lasso shrinkage in the form of two Laplace priors - one for scale and one for shape coefficients - allows for many covariates to be included. Cross-validation helper functions can be used to tune the shrinkage parameters. Monte Carlo Markov Chain (MCMC) sampling using a Gibbs wrapper around Radford Neal's univariate slice sampler (R package MfUSampler) is used for coefficient estimation.
Maintained by Alireza S. Mahani. Last updated 2 years ago.
1 stars 1.00 score 8 scripts
rabarata
exdqlm:Extended Dynamic Quantile Linear Models
Routines for Bayesian estimation and analysis of dynamic quantile linear models utilizing the extended asymmetric Laplace error distribution, also known as extended dynamic quantile linear models (exDQLM) described in Barata et al (2020) <doi:10.1214/21-AOAS1497>.
Maintained by Raquel Barata. Last updated 2 years ago.
1 stars 1.00 score
