Processing and analysis of high throughput (Roche 454)
sequences generated from multiple loci and multiple biological
samples. Sequences are assigned to their locus and sample of
origin, aligned and trimmed. Where possible, genotypes are
called and variants mapped to known alleles.

Independent Component Analysis using Maximum Likelihood
An R code implementation of the maximum likelihood (fixed point) algorithm of Hyvaerinen, Karhuna and Oja for independent component analysis.

An R code implementation of the maximum likelihood (fixed
point) algorithm of Hyvaerinen, Karhuna, and Oja for
independent component analysis.

Missing Data Imputation with Automated Machine Learning
Using automated machine learning, the package fine-tunes an Elastic
Net or Gradient Boosting Machine model for imputing the missing
observations of each variable. This procedure has been implemented for the
first time by this package and is expected to outperform other packages for
imputing missing data that do not fine-tune their models.

Multiple Imputation with Automated Machine Learning
h2o (>= 3.34.0.0), mice, missRanger, memuse, md.log (>= 0.2.0)

Using automated machine learning, the package fine-tunes an Elastic
Net (default) or Gradient Boosting, Random Forest, Deep Learning, Extreme Gradient Boosting,
or Stacked Ensemble machine learning model for imputing the missing
observations of each variable. This procedure has been implemented for the
first time by this package and is expected to outperform other packages for
imputing missing data that do not fine-tune their models. The main idea is
to allow the model to set its own parameters for imputing each variable
instead of setting fixed predefined parameters to impute all variables
of the dataset.

Machine learning algorithms have been used for performing
single missing data imputation and most recently, multiple imputations.
However, this is the first attempt for using automated machine learning algorithms
for performing both
single and multiple imputation. Automated machine learning is a procedure for
fine-tuning the model automatic, performing a random search for a model that
results in less error, without overfitting the data. The main idea is
to allow the model to set its own parameters for imputing each variable seperately
instead of setting fixed predefined parameters to impute all variables
of the dataset.
Using automated machine learning, the package fine-tunes an Elastic
Net (default) or Gradient Boosting, Random Forest, Deep Learning, Extreme Gradient Boosting,
or Stacked Ensemble machine learning model (from one or a combination of other
supported algorithms) for imputing the missing
observations. This procedure has been implemented for the
first time by this package and is expected to outperform other packages for
imputing missing data that do not fine-tune their models. The multiple imputation
is implemented via bootstrapping without letting the duplicated observations to
harm the cross-validation procedure, which is the way imputed variables are evaluated.
Most notably, the package implements automated procedure for handling imputing imbalanced
data (class rarity problem), which happens when a factor variable has a level that is far more
prevalent than the other(s). This is known to result in biased predictions, hence, biased
imputation of missing data. However, the autobalancing procedure ensures that instead of
focusing on maximizing accuracy (classification error) in imputing factor variables,
a fairer procedure and imputation method is practiced.