Contains functions that do something convenient (e.g. create BMI categories), functions for calculating moving-window statistics efficiently, and functions for generating various figures (e.g. histograms with fitted probability mass/density function).

A set of control charts for batch processes based on the VAR model. The package contains the implementation of T2.var and W.var control charts based on VAR model coefficients using the couple vectors theory. In each time-instant the VAR coefficients are estimated from a historical in-control dataset and a decision rule is made for online classifying of a new batch data. Those charts allow efficient online monitoring since the very first time-instant. The offline version is available too. In order to evaluate the chart's performance, this package contains functions to generate batch data for offline and online monitoring.See in Danilo Marcondes Filho and Marcio Valk (2020) <doi:10.1016/j.ejor.2019.12.038>.

Edit and validate taxonomic data in compliance with Darwin Core
    standards (Darwin Core 'Taxon' class <https://dwc.tdwg.org/terms/#taxon>).

Solving large scale distance weighted discrimination.
  The main algorithm is a symmetric Gauss-Seidel based alternating direction method of multipliers (ADMM) method. See Lam, X.Y., Marron, J.S., Sun, D.F., and Toh, K.C. (2018) <arXiv:1604.05473> for more details.

The 'DWD' provides gridded radar data for Germany in binary format. 
  'dwdradar' reads these files and enables a fast conversion into numerical format.

Infers networks with hubs using degree weighted Lasso method.

This linear model solution is useful when both predictor and response have associated uncertainty. The doubly weights linear model solution is invariant on which quantity is used as predictor or response. Based on the results by Reed(1989) <doi:10.1119/1.15963> and Ripley & Thompson(1987) <doi:10.1039/AN9871200377>.

The rapid development of single-cell transcriptomic technologies 
    has helped uncover the cellular heterogeneity within cell populations. 
    However, bulk RNA-seq continues to be the main workhorse for quantifying 
    gene expression levels due to technical simplicity and low cost. To most 
    effectively extract information from bulk data given the new knowledge 
    gained from single-cell methods, we have developed a novel algorithm to 
    estimate the cell-type composition of bulk data from a single-cell 
    RNA-seq-derived cell-type signature. Comparison with existing methods using 
    various real RNA-seq data sets indicates that our new approach is more 
    accurate and comprehensive than previous methods, especially for the 
    estimation of rare cell types. More importantly,our method can detect 
    cell-type composition changes in response to external perturbations, 
    thereby providing a valuable, cost-effective method for dissecting the 
    cell-type-specific effects of drug treatments or condition changes. 
    As such, our method is applicable to a wide range of biological and 
    clinical investigations. Dampened weighted least squares ('DWLS') is an 
    estimation method for gene expression deconvolution, in which the cell-type 
    composition of a bulk RNA-seq data set is computationally inferred. 
    This method corrects common biases towards cell types that are 
    characterized by highly expressed genes and/or are highly prevalent, to 
    provide accurate detection across diverse cell types. See: 
    <https://www.nature.com/articles/s41467-019-10802-z.pdf> for more 
    information about the development of 'DWLS' and the methods behind our 
    functions.