Recursive Partitioning Based Multivariate Adaptive Regression
Models, Classification Trees, Survival Trees
Heping Zhang, Yihao Lu, Haixu Ma, Mingliang Ma, Yidong Zhou, Qi Yu
Implements recursive partitioning based, nonparametric methods for high
dimensional regression and classification. Depending on the aims of data analysis as well as the
structures of the data, macs provides three major functions: multivariate adaptive regression
models, classification trees and survival trees. A list of references for this package is, Zhang, H. (1997) <doi:10.1080/10618600.1997.10474728>, Zhang, H. et al. (1999) <ISBN:978-1-4757-3027-2>, Zhang, H. et al. (2014) <doi:10.1002/gepi.21843>.

Provides methods and classes for adding m-activation ("multiplicative activation") layers to MLR or multivariate logistic regression models.  M-activation layers created in this library detect and add input interaction (polynomial) effects into a predictive model.  M-activation can detect high-order interactions -- a traditionally non-trivial challenge.  Details concerning application, methodology, and relevant survey literature can be found in this library's vignette, "About."

Mediation Analysis for Count and Zero-Inflated Count Data
sandwich, survival, stats, mediation, emplik, BB, mathjaxr
Performs causal mediation analysis for count and zero-inflated
count data without or with a post-treatment confounder; calculates power
to detect prespecified causal mediation effects, direct effects, and
total effects; performs sensitivity analysis when there is a treatment-
induced mediator-outcome confounder as described by Cheng, J., Cheng, N.F.,
Guo, Z., Gregorich, S., Ismail, A.I., Gansky, S.A. (2018)
<doi:10.1177/0962280216686131>. Implements Instrumental Variable (IV)
method to estimate the controlled (natural) direct and mediation effects,
and compute the bootstrap Confidence Intervals as described by Guo, Z.,
Small, D.S., Gansky, S.A., Cheng, J. (2018) <doi:10.1111/rssc.12233>.

Performs causal mediation analysis for count and zero-inflated
count data without or with a post-treatment confounder; calculates power
to detect prespecified causal mediation effects, direct effects, and
total effects; performs sensitivity analysis when there is a treatment-
induced mediator-outcome confounder as described by Cheng, J., Cheng, N.F.,
Guo, Z., Gregorich, S., Ismail, A.I., Gansky, S.A. (2018)
<doi:10.1177/0962280216686131>. Implements Instrumental Variable (IV)
method to estimate the controlled (natural) direct and mediation effects,
and compute the bootstrap Confidence Intervals as described by Guo, Z.,
Small, D.S., Gansky, S.A., Cheng, J. (2018) <doi:10.1111/rssc.12233>. This
software was made possible by Grant R03DE028410 from the The National
Institute of Dental and Craniofacial Research, a component of the National
Institutes of Health.