# ORPHANED

#### 24 packages on CRAN

Fitting possibly high dimensional penalized regression models. The penalty structure can be any combination of an L1 penalty (lasso and fused lasso), an L2 penalty (ridge) and a positivity constraint on the regression coefficients. The supported regression models are linear, logistic and Poisson regression and the Cox Proportional Hazards model. Cross-validation routines allow optimization of the tuning parameters.

A set of tools for normalizing, diagnostics and visualization of NanoString nCounter data.

Useful when reading the book above mentioned, in the documentation referred to as `the book'.

Provides a toolkit-independent API for building interactive GUIs. At least one of the 'gWidgetsXXX packages', such as gWidgetstcltk, needs to be installed. Some icons are on loan from the scigraphica project <http://scigraphica.sourceforge.net>.

Contains linear and nonlinear regression methods based on Partial Least Squares and Penalization Techniques. Model parameters are selected via cross-validation, and confidence intervals ans tests for the regression coefficients can be conducted via jackknifing.

This package provides a minimalistic functionality necessary to apply Gaussian Process in R. They provide a selection of functionalities of GPML Matlab library.

Finds the maximum likelihood estimate of the mean vector and variance-covariance matrix for multivariate normal data with missing values.

Shrunken Centroids Regularized Discriminant Analysis for the classification purpose in high dimensional data.

Sensitivity indices with dependent correlated inputs, using a method based on PLS regression.

Quantification of the effect of geographic versus environmental isolation on genetic differentiation

The package implements efficient ways to evaluate and solve equations of the form Ax=b, where A is a kronecker product of matrices. Functions to solve least squares problems of this type are also included.

Functions for implementing species dispersal into projections of species distribution models (e.g. under climate change scenarios).

Finds the k nearest neighbours for every point in a given dataset in O(N log N) time using Arya and Mount's ANN library (v1.1.3). There is support for approximate as well as exact searches, fixed radius searches and 'bd' as well as 'kd' trees. The distance is computed using the L1 (Manhattan, taxicab) metric. Please see package 'RANN' for the same functionality using the L2 (Euclidean) metric.

Implements the largeVis algorithm (see Tang, et al. (2016) <DOI:10.1145/2872427.2883041>) for visualizing very large high-dimensional datasets. Also very fast search for approximate nearest neighbors; outlier detection; and optimized implementations of the HDBSCAN*, DBSCAN and OPTICS clustering algorithms; plotting functions for visualizing the above.

Method for protein quantification based on identified and quantified peptides. protiq can be used for absolute and relative protein quantification. Input peptide abundance scores can come from various sources, including SRM transition areas and intensities or spectral counts derived from shotgun experiments. The package is still being extended to also include the model for protein identification, MIPGEM, presented in Gerster, S., Qeli, E., Ahrens, C.H. and Buehlmann, P. (2010). Protein and gene model inference based on statistical modeling in k-partite graphs. Proceedings of the National Academy of Sciences 107(27):12101-12106.

The package implements the model-based kernel machine method for detecting gene-centric gene-gene interactions of Li and Cui (2012).

This package contains a database of city, state, latitude, and longitude information for U.S. ZIP codes from the CivicSpace Database (August 2004) augmented by Daniel Coven's federalgovernmentzipcodes.us web site (updated January 22, 2012). Previous versions of this package (before 1.0) were based solely on the CivicSpace data, so an original version of the CivicSpace database is also included.

This package proposes a model-based clustering algorithm for multivariate functional data. The parametric mixture model, based on the assumption of normality of the principal components resulting from a multivariate functional PCA, is estimated by an EM-like algorithm. The main advantage of the proposed algorithm is its ability to take into account the dependence among curves.

Functions to prepare files needed for running BUGS in batch-mode, and running BUGS from R. Support for Linux and Windows systems with OpenBugs is emphasized.