Implementation of methods for minimizing ill-conditioned problems. Currently only includes regularized (quasi-)newton optimization (Kanzow and Steck et al. (2023), <doi:10.1007/s12532-023-00238-4>).

Makes it possible to create an internally consistent
    repository consisting of selected packages from CRAN-like repositories.
    The user specifies a set of desired packages, and 'miniCRAN' recursively
    reads the dependency tree for these packages, then downloads only this
    subset. The user can then install packages from this repository directly,
    rather than from CRAN.  This is useful in production settings, e.g. server
    behind a firewall, or remote locations with slow (or zero) Internet access.

Create minimal, responsive, and style-agnostic HTML documents with
    the lightweight CSS frameworks such as 'sakura', 'Water.css', and 'spcss'.
    Powerful features include table of contents floating as a sidebar,
    folding codes and results, and more.

Quick and simple Tcl/Tk Graphical User Interface 
  to call functions. Also comprises a very simple experimental 
  GUI framework.

Logistic-normal Multinomial (LNM) models are common in problems with multivariate count data. This package gives a simple implementation with a 30 line 'Stan' script. This lightweight implementation makes it an easy starting point for other projects, in particular for downstream tasks that require analysis of "compositional" data. It can be applied whenever a multinomial probability parameter is thought to depend linearly on inputs in a transformed, log ratio space. Additional utilities make it easy to inspect, create predictions, and draw samples using the fitted models. More about the LNM can be found in Xia et al. (2013) "A Logistic Normal Multinomial Regression Model for Microbiome Compositional Data Analysis" <doi:10.1111/biom.12079> and Sankaran and Holmes (2023) "Generative Models: An Interdisciplinary Perspective" <doi:10.1146/annurev-statistics-033121-110134>.

Normally building a GODB is fairly complicated, involving downloading 
 multiple database files and using these to build e.g. a 'mySQL' database. 
 Accessing this database is also complicated, involving an intimate knowledge
 of the database in order to construct reliable queries.
 Here we have a more modest goal, generating GOGOA3, which is a stripped down
 version of the GODB that 
 was originally restricted to human genes as designated by the HUGO Gene Nomenclature
 Committee (HGNC) (see <https://geneontology.org/>). I have now added about two dozen
 additional species, namely all species represented on the Gene Ontology
 download page <https://current.geneontology.org/products/pages/downloads.html>.
 This covers most of the model organisms that are commonly used in bio-medical
 and basic research (assuming that anyone still has a grant to do such research).
 This can be built in a matter of seconds from 2 easily
 downloaded files (see <https://current.geneontology.org/products/pages/downloads.html>
 and <https://geneontology.org/docs/download-ontology/>), and it can be queried by e.g.
 w<-which(GOGOA3[,"HGNC"] %in% hgncList) where GOGOA3
 is a matrix representing the minimalist GODB and hgncList is a list of
 gene identifiers. This database will be used in my upcoming package 'GoMiner'
 which is based on my previous publication (see Zeeberg, B.R., Feng, W., Wang, G. et al. (2003)<doi:10.1186/gb-2003-4-4-r28>).
 Relevant .RData files are available from GitHub (<https://github.com/barryzee/GO/tree/main/databases>).