Rank-based tests for enrichment of KOG (euKaryotic Orthologous Groups) classes with up- or down-regulated genes based on a continuous measure. The meta-analysis is based on correlation of KOG delta-ranks across datasets (delta-rank is the difference between mean rank of genes belonging to a KOG class and mean rank of all other genes). With binary measure (1 or 0 to indicate significant and non-significant genes), one-tailed Fisher's exact test for over-representation of each KOG class among significant genes will be performed.

Functions to train self-organising maps (SOMs). Also interrogation of the maps and prediction using trained maps are supported. The name of the package refers to Teuvo Kohonen, the inventor of the SOM.

Provides utilities for 'Kokudo Suuchi', the GIS data service of the Japanese government.
    See <https://nlftp.mlit.go.jp/index.html> for more information.

Write beautiful yet customizable letters in R Markdown and
  directly obtain the finished PDF. Smooth generation of PDFs is realized by
  'rmarkdown', the 'pandoc-letter' template and the 'KOMA-Script' letter class.
  'KOMA-Script' provides enhanced replacements for the standard 'LaTeX' classes
  with emphasis on typography and versatility. 'KOMA-Script' is particularly
  useful for international writers as it handles various paper formats well, 
  provides layouts for many common window envelope types (e.g. German, US, 
  French, Japanese) and lets you define your own layouts. The package comes 
  with a default letter layout based on 'DIN 5008B'.

Statistical methods that quantify the conditions necessary to
    alter inferences, also known as sensitivity analysis, are becoming
    increasingly important to a variety of quantitative sciences. A series
    of recent works, including Frank (2000)
    <doi:10.1177/0049124100029002001> and Frank et al.  (2013)
    <doi:10.3102/0162373713493129> extend previous sensitivity analyses by
    considering the characteristics of omitted variables or unobserved
    cases that would change an inference if such variables or cases were
    observed. These analyses generate statements such as "an omitted
    variable would have to be correlated at xx with the predictor of
    interest (e.g., treatment) and outcome to invalidate an inference of a
    treatment effect". Or "one would have to replace pp percent of the
    observed data with null hypothesis cases to invalidate the inference".
    We implement these recent developments of sensitivity analysis and
    provide modules to calculate these two robustness indices and generate
    such statements in R. In particular, the functions konfound(),
    pkonfound() and mkonfound() allow users to calculate the robustness of
    inferences for a user's own model, a single published study and
    multiple studies respectively.