To determine the number of quantitative assays needed for a sample 
    of data using pooled testing methods, which include mini-pooling (MP), MP 
    with algorithm (MPA), and marker-assisted MPA (mMPA). To estimate the number 
    of assays needed, the package also provides a tool to conduct Monte Carlo (MC) 
    to simulate different orders in which the sample would be collected to form pools. 
    Using MC avoids the dependence of the estimated number of assays on any specific 
    ordering of the samples to form pools.

A generalization of principal component analysis for integrative
  analysis. The method finds principal components that describe single matrices
  or that are common to several matrices. The solutions are sparse. Rank of
  solutions is automatically selected using cross validation. The method is
  described in Kallus et al. (2019) <arXiv:1911.04927>.

Compute similarities and distances between marked point processes.

Simple helpers for matrix multiplication on data.frames. These allow for more
    concise code during low level mathematical operations, and help ensure code is more
    easily read, understood, and serviced.

Researchers doing a mixed-methods analysis (nested analysis as
    developed by Lieberman (2005) <doi:10.1017/S0003055405051762>) can
    use the package for the classification of cases and case selection using
    results of a linear regression. One can designate cases 
    as typical, deviant, extreme and pathway case and use different case 
    selection strategies for the choice of a case belonging to one of
    these types.

Mixed models for repeated measures (MMRM) are a popular
    choice for analyzing longitudinal continuous outcomes in randomized
    clinical trials and beyond; see Cnaan, Laird and Slasor (1997)
    <doi:10.1002/(SICI)1097-0258(19971030)16:20%3C2349::AID-SIM667%3E3.0.CO;2-E>
    for a tutorial and Mallinckrodt, Lane, Schnell, Peng and Mancuso
    (2008) <doi:10.1177/009286150804200402> for a review. This package
    implements MMRM based on the marginal linear model without random
    effects using Template Model Builder ('TMB') which enables fast and
    robust model fitting. Users can specify a variety of covariance
    matrices, weight observations, fit models with restricted or standard
    maximum likelihood inference, perform hypothesis testing with
    Satterthwaite or Kenward-Roger adjustment, and extract least square
    means estimates by using 'emmeans'.