Software products developed as part of our research are almost always made available as part of online supplementary materials through journal websites where the research is published.
In some cases, official R packages have also been released on the Comprehensive R Archive Network (CRAN).
We do not provide personal software support.
Version updates, if any, will be made available through this website
or, for official R releases, the CRAN.
Measurement Error Problems
- Multivariate Density Deconvolution for Continuous Data by Stochastic Rotation of Replicates:
R codes implementing Bayesian semiparametric multivariate density deconvolution methods for continuous data
(that corrects for the measurement error by stochastically rotating and then stretching or contracting the replicates toward the latent true values)
developed in Roy and Sarkar (2023+)
are available as part of the Supplementary Materials from the journal website.
- Roy, A. and Sarkar, A. (2023+).
Bayesian semiparametric multivariate density deconvolution via stochastic rotation of replicates.
To appear in Computational Statistics and Data Analysis.
[link]
- Covariate Informed Multivariate Density Deconvolution for Continuous Data:
R codes implementing Bayesian semiparametric covariate informed multivariate copula density deconvolution methods for continuous data
developed in Sarkar, et al. (2022)
are available as part of the Supplementary Materials from the journal website.
R codes for covariate informed ordinary copula density estimation (that is, without measurement errors), a useful by-product of the method, are also included.
- Sarkar, A. (2021).
Bayesian semiparametric covariate informed multivariate density deconvolution.
Journal of Computtaional and Graphical Statistics.
[link]
- Multivariate Copula Density Deconvolution for (Continuous and) Zero-inflated Data:
R codes implementing Bayesian semiparametric copula density deconvolution methods
developed in Sarkar, et al. (2021)
are available as part of the Supplementary Materials from the journal website.
- Sarkar, A., Pati, D., Mallick, B. K., and Carroll, R. J. (2021).
Bayesian semiparametric copula density deconvolution for zero-inflated data in nutritional epidemiology.
Journal of the American Statistical Association, 116, 1075-1087.
[link]
- Multivariate Density Deconvolution for Continuous Data:
R codes implementing Bayesian semiparametric multivariate density deconvolution methods
developed in Sarkar, et al. (2018)
are available as part of the Supplementary Materials from the journal website.
- Sarkar, A., Pati, D., Chakraborty, A., Mallick, B. K., and Carroll, R. J. (2018).
Bayesian semiparametric multivariate density deconvolution.
Journal of the American Statistical Association, 113, 401-416.
[link]
- Regression with Errors-in-Covariates:
R codes implementing Bayesian semiparametric density deconvolution methods developed in Sarkar, et al. (2014b)
are available as part of the Supplementary Materials from the journal website.
- Sarkar, A., Mallick, B. K. and Carroll, R. J. (2014b).
Bayesian semiparametric regression in the presence of conditionally heteroscedastic measurement and regression errors.
Biometrics, 70, 823-834.
[link]
- Univariate Density Deconvolution:
R codes implementing Bayesian semiparametric density deconvolution methods developed in Sarkar, et al. (2014a)
are available as part of the Supplementary Materials from the journal website.
-
Sarkar, A., Mallick, B. K., Staudenmayer, J., Pati, D. and Carroll, R. J. (2014).
Bayesian Semiparametric Density Deconvolution in the Presence of Conditionally Heteroscedastic Measurement Errors.
Journal of Computational and Graphical Statistics, 24, 1101-1125.
[link]
(Higher Order) (Nonhomogeneous) (Hidden) Markov (Renewal) (Mixed) Models
- R package BMRMM implementing Bayesian semiparametric Markov (renewal) mixed models
for joint analysis of categorical sequences and associated duration times (which can be state duration times or interstate interval times depending on the application)
developed in Sarkar, et al. (2018) and Wu, et. al. (2023+) is available on CRAN.
[link]
- Wu, Y., Jarvis, E. D. and Sarkar, A. (2023+).
Bayesian Markov renewal mixed models for analyzing vocalization syntax.
To appear in Biostatistics.
[link]
- Sarkar, A., Chabout, J., Macopson, J. J., Jarvis, E. D. and Dunson, D. B. (2018).
Bayesian semiparametric mixed effects Markov models with application to vocalization syntax.
Journal of the American Statistical Association, 113, 1515-1527.
[link]
- Syntax Decoder:
R codes implementing Monte Carlo tests for assessing local and global differences in transition probability matrices.
These codes were originally developed for Chabout, et al. (2016) to assess differences in rodent vocalizaton patterns under different experimental conditions
and can be downloaded from the journal website as part of the Supplementary Materials.
- Chabout, J., Sarkar, A.*, Patel, S.*, Raiden, T., Dunson, D. B., Fisher, S. E. and Jarvis, E. D. (2016).
A Foxp2 mutation implicated in human speech deficits alters sequencing of ultrasonic vocalizations in adult male mice.
[* equal contributions]
Frontiers in Behavioral Neuroscience, 10, 1-18.
[link]
- MEMC Syntax Decoder:
Matlab codes implementing mixed effects Markov models for collections of categorical sequences with exogenous categorical predictors.
The methodology has been developed to provide a sophisticated inferential framework for syntax analysis in mouse vocalization expriments.
The codes are available as part of the Supplementary Materials from the journal website.
- Sarkar, A., Chabout, J., Macopson, J. J., Jarvis, E. D. and Dunson, D. B. (2018).
Bayesian semiparametric mixed effects Markov models with application to vocalization syntax.
Journal of the American Statistical Association, 113, 1515-1527.
[link]
(Locally Varying) Longitudinal (Functional) Mixed Drift-Diffusion and Related Categorical Probability Models
- R package lddmm implementing Bayesian semiparametric longitudinal drift-diffusion mixed models
for joint analysis of response categories and associated response times
developed in Paulon, et al. (2021) is available on CRAN.
[link]
While the paper focused mainly on scenarios with all observed responses with uncensored response times, our codes also support missing responses due to censored response times.
Some additional flexibilities in modeling the boundary parameters have also been incorporated in a recent update on CRAN.-
Paulon, G., Llanos, F., G., Chandrasekaran, C. and Sarkar, A. (2021)
Bayesian semiparametric longitudinal drift-diffusion mixed models for tone learning in adults.
Journal of the American Statistical Association, 116, 1114-1127.
[link]
- Functional Logistic Mixed Models:
R codes implementing functional logistic mixed models
developed in Paulon, et al. (2019)
are available as part of the Supplementary Materials from the journal website.
-
Paulon, G., Reetzke, R., Chandrasekaran, C. and Sarkar, A. (2019).
Functional logistic mixed effects models for learn- ing curves from longitudinal binary data.
Journal of Speech, Language and Hearing Research, 62, 543-553.
[link]