Imports: tuneR, seewave, gam, data.table, RSQLite, stringr, purrr,
methods, signal, tidyr, ggplot2, manipulate, knitr, rstudioapi,

methods, signal, tidyr, ggplot2, manipulate, knitr, xml2,

Packaged: 2020-12-15 22:01:50 UTC; Taiki.Sakai

Title: Preferential Attachment Based Common Neighbor Distribution
Description: PANDA (Preferential Attachment based common Neighbor Distribution derived Associations) is designed to perform the following tasks in PPI networks: (1) identify significantly functionally associated protein pairs, (2) predict GO terms and KEGG pathways for proteins, (3) make a cluster of proteins based on the significant protein pairs, (4) identify subclusters whose members are enriched in KEGG pathways. For other types of biological networks, (1) and (3) can still be performed.

Description: This package contains a methodology that makes use of the factor
structure of large dimensional panels to understand the nature of
nonstationarity inherent in data. This is referred to as PANIC - Panel
Analysis of Nonstationarity in Idiosyncratic and Common Components. PANIC
(2004) includes valid pooling methods that allow panel tests to be
constructed. PANIC (2004) can detect whether the nonstationarity in a
series is pervasive, or variable specific, or both. PANIC (2010) includes
two new tests on the idiosyncratic component that estimates the pooled
autoregressive coefficient and sample moment, respectively. The PANIC model
approximates the number of factors based on Bai and Ng (2002)

(2004) includes valid pooling methods that allow panel unit root tests
tests to be constructed. PANIC (2004) can detect whether the
nonstationarity in a series is pervasive, or variable specific, or both.
PANIC (2010) includes the Panel Modified Sargan-Bhargava test, three models
for a Moon and Perron style test, and a bias correction for the
idiosyncratic unit root test of PANIC (2004). The PANIC model approximates
the number of factors based on Bai and Ng (2002).

Description: A methodology that makes use of the factor structure of large
dimensional panels to understand the nature of nonstationarity inherent
in data. This is referred to as PANIC, Panel Analysis of Nonstationarity
valid pooling methods that allow panel tests to be constructed.
PANIC (2004) can detect whether the nonstationarity in a series is
pervasive, or variable specific, or both.
autoregressive coefficient and sample moment, respectively. The PANIC
model approximates the number of factors based on