Analysis of DNA mixtures involving relatives by computation of likelihood ratios that account for dropout and drop-in, mutations, silent alleles and population 
  substructure. This is useful in kinship cases, like non-invasive prenatal paternity testing, where deductions about individuals' relationships rely on DNA mixtures, 
  and in criminal cases where the contributors to a mixed DNA stain may be related. Relationships are represented 
  by pedigrees and can include kinship between more than two individuals. The main function is relMix() and its graphical user interface relMixGUI(). 
  The implementation and method is described in Dorum et al. (2017) <doi:10.1007/s00414-016-1526-x>, Hernandis et al. (2019)
  <doi:10.1016/j.fsigss.2019.09.085> and Kaur et al. (2016) <doi:10.1007/s00414-015-1276-1>.

A set of tools to explore the behaviour statistics used for forensic DNA interpretation when close relatives are involved. The package also offers some useful tools for exploring other forensic DNA situations.

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.

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.