Chris M Manuel, Samiran Sinha, and Suojin Wang
Applying the methodology from Manuel et al., estimates for the
parameters for matched case control data with a mismeasured exposure are calculated
through the use of user supplied instrumental variables (Submitted).

Analysis of Matched Case Control Data with a Mismeasured
Exposure that is Accompanied by Instrumental Variables
Applying the methodology from Manuel et al. to estimate parameters using a matched
case control data with a mismeasured exposure variable that is accompanied by
instrumental variables (Submitted).

The Matthews correlation coefficient (MCC) score is calculated (Matthews BW  (1975) <DOI:10.1016/0005-2795(75)90109-9>).

Maximum Likelihood Estimation for Generalized Linear Mixed
Models
Maximum likelihood estimation for generalized linear mixed models via Monte Carlo EM.

Maximum likelihood estimation for generalized linear mixed models via Monte Carlo EM.
For a description of the algorithm see Brian S. Caffo, Wolfgang Jank and Galin L. Jones (2005)
<DOI:10.1111/j.1467-9868.2005.00499.x>.

Ben Sherwood [aut, cre], Brad Price [aut]
R (>= 3.0.0), glmnet, flexclust, Matrix, parallel, faraway,
methods, stats
Fits the Multivariate Cluster Elastic Net (MCEN) presented in Price & Sherwood (2018) <arXiv:1707.03530>. The MCEN model simultaneously estimates regression coefficients and a clustering of the responses for a multivariate response model. Currently accommodates the Gaussian and binomial likelihood.

Machine Coded Genetic Algorithms for the Real Value Optimization
Problems
Machine Coded Genetic Algorithms (mcga) builds genetic
algorithms with machine coded chromosomes for real value
optimizitation problems. Chromosomes are not encoded/decoded,
the byte representation of double values are used for the
genetic operations such as crossing-over and mutation.
Computers already decode and encode the variables using a
finite set of alphabet. This makes mcga extremely fast even the
number of parameters is high.

Machine coded genetic algorihms for real-valued optimization
problems
Machine coded genetic algorithm (MCGA) is a fast tool for
real-valued optimization problems. It uses the byte
representation of variables rather than real-values. It
performs the classical crossover operations (uniform) on these
byte representations. Mutation operator is also similar to
classical mutation operator, which is to say, it changes a
randomly selected byte value of a chromosome by +1 or -1 with
probability 1/2. In MCGAs there is no need for
encoding-decoding process and the classical operators are
directly applicable on real-values. It is fast and can handle a
wide range of a search space with high precision. Using a
256-unary alphabet is the main disadvantage of this algorithm
but a moderate size population is convenient for many problems.