Maja Pohar Perme [aut],
Damjan Manevski [aut, cre]
Contains functions for analysing relative survival data, including nonparametric estimators of net (marginal relative) survival, relative survival ratio, crude mortality, methods for fitting  and checking additive and multiplicative regression models, transformation approach, methods for dealing with population mortality tables. Work has been described in Pohar Perme, Pavlic (2018) <doi:10.18637/jss.v087.i08>.

Calculate endogenous network effects in event sequences and fit relational event models (REM): Using network event sequences (where each tie between a sender and a target in a network is time-stamped), REMs can measure how networks form and evolve over time. Endogenous patterns such as popularity effects, inertia, similarities, cycles or triads can be calculated and analyzed over time.

Regularized Multivariate Regression for Identifying Master Predictors
Jie Peng <jie@wald.ucdavis.edu>, Pei Wang <pwang@fhcrc.org>, Ji Zhu <jizhu@umich.edu>.
remMap is developed for fitting multivariate response regression models under the high-dimension-low-sample-size setting

Regularized Multivariate Regression for Identifying Master
Predictors

c(
person(given = "Brinley N.",
family = "Zabriskie",
role = c("aut", "cre"),
email = "zabriskie@stat.byu.edu",
comment = c(ORCID = "0000-0002-2621-7000")),
person(given = "Benjamin",
family = "Kinard",
role = c("aut"),
email = "benjaminjameskinard@gmail.com"),
person(given = "Chris",
family = "Sypherd",
role = c("aut"),
email = "sypherdchris@gmail.com"),
person(given = "Ryan",
family = "Whetten",
role = c("aut"),
email = "whettenrmw@gmail.com"),
person(given = "Madeleine",
family = "Hays",
role = c("ctb"),
email = "madeleine.hays7@gmail.com"))
The rema package implements a permutation-based approach for binary
meta-analyses of 2x2 tables, founded on conditional logistic regression,
that provides more reliable statistical tests when heterogeneity is
observed in rare event data (Zabriskie et al. 2021 <doi:10.1002/sim.9142>).
To adjust for the effect of heterogeneity, this method conditions on the
sufficient statistic of a proxy for the heterogeneity effect as opposed to
estimating the heterogeneity variance. While this results in the model not
strictly falling under the random-effects framework, it is akin to a
random-effects approach in that it assumes differences in variability due
to treatment. Further, this method does not rely on large-sample
approximations or continuity corrections for rare event data. This method
uses the permutational distribution of the test statistic instead of
asymptotic approximations for inference. The number of observed events
drives the computation complexity for creating this permutational
distribution. Accordingly, for this method to be computationally feasible,
it should only be applied to meta-analyses with a relatively low number of
observed events. To create this permutational distribution, a network
algorithm, based on the work of Mehta et al. (1992) <doi:10.2307/1390598>
and Corcoran et al. (2001) <doi:10.1111/j.0006-341x.2001.00941.x>, is
employed using C++ and integrated into the package.
Brinley N. Zabriskie [aut, cre]
(<https://orcid.org/0000-0002-2621-7000>),
Benjamin Kinard [aut],
Chris Sypherd [aut],
Ryan Whetten [aut],
Madeleine Hays [ctb]