Tuo Zhao, Xingguo Li, Han Liu, and Kathryn Roeder
The package SAM targets at high dimensional predictive
modeling (regression and classification) for complex data
analysis. SAM is short for sparse additive modeling, and adopts
the computationally efficient basis spline technique. We solve
the optimization problems by various computational algorithms
including the block coordinate descent algorithm, fast
iterative soft-thresholding algorithm, and newton method. The
computation is further accelerated by warm-start and active-set
tricks.

Haoming Jiang, Yukun Ma, Xinyu Fei, Tuo Zhao, Xingguo Li, Han Liu, and Kathryn Roeder
Computationally efficient tools for high dimensional predictive
modeling (regression and classification). SAM is short for sparse
additive modeling, and adopts the computationally efficient basis
spline technique. We solve  the optimization problems by various
computational algorithms including the block coordinate descent
algorithm, fast iterative soft-thresholding algorithm, and newton method.
The computation is further accelerated by warm-start and active-set tricks.

Haoming Jiang, Yukun Ma, Han Liu, Kathryn Roeder, Xingguo Li, and Tuo Zhao

Selection and Misclassification Bias Adjustment for Logistic
Regression Models
c(
person("Alexander", "Rix", email = "alexrix@umich.edu", role = "cre"),
person("Lauren", "Beesley", email = "lbeesley@umich.edu", role = "aut")
)
Health research using data from electronic health records (EHR) has gained
popularity, but misclassification of EHR-derived disease status and lack of
representativeness of the study sample can result in substantial bias in
effect estimates and can impact power and type I error for association
tests. Here, the assumed target of inference is the relationship between
binary disease status and predictors modeled using a logistic regression
model. 'SAMBA' implements several methods for obtaining bias-corrected
point estimates along with valid standard errors as proposed in Beesley and
Mukherjee (2020) <doi:10.1101/2019.12.26.19015859>, currently under review.
Alexander Rix [cre],
Lauren Beesley [aut]