* Adjacency-Constrained Clustering of a Block-Diagonal Similarity Matrix* (

**adjclust**)

Implements a constrained version of hierarchical agglomerative clustering, in which each observation is associated to a position, and only adjacent clusters can be merged. Typical application fields in bioinformatics include Genome-Wide Association Studies or Hi-C data analysis, where the similarity between items is a decreasing function of their genomic distance. Taking advantage of this feature, the implemented algorithm is time and memory efficient. This algorithm is described in Chapter 4 of Alia Dehman (2015) <https://…/tel-01288568v1>.

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**Initialization Algorithms for Partitioning Cluster Analysis****inaparc**)

Partitioning clustering algorithms divide data sets into k subsets or partitions which are so-called clusters. They require some initialization procedures for starting to partition the data sets. Initialization of cluster prototypes is one of such kind of procedures for most of the partitioning algorithms. Cluster prototypes are the data elements, i.e. centroids or medoids, representing the clusters in a data set. In order to initialize cluster prototypes, the package ‘inaparc’ contains a set of the functions that are the implementations of several linear time-complexity and loglinear time-complexity methods in addition to some novel techniques. Initialization of fuzzy membership degrees matrices is another important task for starting the probabilistic and possibilistic partitioning algorithms. In order to initialize membership degrees matrices required by these algorithms, a number of functions based on some traditional and novel initialization techniques are also available in the package ‘inaparc’.

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**Relative Importance PCA Regression****RelimpPCR**)

Performs Principal Components Analysis (also known as PCA) dimensionality reduction in the context of a linear regression. In most cases, PCA dimensionality reduction is performed independent of the response variable for a regression. This captures the majority of the variance of the model’s predictors, but may not actually be the optimal dimensionality reduction solution for a regression against the response variable. An alternative method, optimized for a regression against the response variable, is to use both PCA and a relative importance measure. This package applies PCA to a given data frame of predictors, and then calculates the relative importance of each PCA factor against the response variable. It outputs ordered factors that are optimized for model fit. By performing dimensionality reduction with this method, an individual can achieve a the same r-squared value as performing just PCA, but with fewer PCA factors. References: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani (2013) <http://…/>.

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**Event History Analysis****eha**)

Sampling of risk sets in Cox regression, selections in the Lexis diagram, bootstrapping. Parametric proportional hazards fitting with left truncation and right censoring for common families of distributions, piecewise constant hazards, and discrete models. AFT regression for left truncated and right censored data.

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**Interactive, Complex Heatmaps****iheatmapr**)

Make complex, interactive heatmaps. ‘iheatmapr’ includes a modular system for iteratively building up complex heatmaps, as well as the iheatmap() function for making relatively standard heatmaps.

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**Bayes Factors for Hierarchical Linear Models with Continuous Predictors****BayesRS**)

Runs hierarchical linear Bayesian models. Samples from the posterior distributions of model parameters in JAGS (Just Another Gibbs Sampler; Plummer, 2003, <http://…/> ). Computes Bayes factors for group parameters of interest with the Savage-Dickey density ratio (Wetzels, Raaijmakers, Jakab, Wagenmakers, 2009, <doi:10.3758/PBR.16.4.752>).