Set of functions for analysis of Principal Coordinates of Phylogenetic Structure (PCPS).

Calculates the amplification efficiency and curves from real-time 
  quantitative PCR (Polymerase Chain Reaction) data. Estimates the relative 
  expression from PCR data using the double delta CT and the standard curve 
  methods Livak & Schmittgen (2001) <doi:10.1006/meth.2001.1262>. Tests for 
  statistical significance using two-group tests and linear regression 
  Yuan et al. (2006) <doi: 10.1186/1471-2105-7-85>.

A collection of functions and data sets that support teaching
  a quantitative finance MS level course on Portfolio Construction and Risk
  Analysis, and the writing of a textbook for such a course.  The package is
  unique in providing several real-world data sets that may be used for problem
  assignments and student projects.  The data sets include cross-sections of
  stock data from the Center for Research on Security Prices, LLC (CRSP),
  corresponding factor exposures data from S&P Global, and several SP500 data
  sets.

Given k populations (can be in thousands), what is the probability that a given subset of size t contains the true top t populations?  This package finds this probability and offers three tuning parameters (G, d, L) to relax the definition.

A function to estimate
        panel-corrected standard errors. Data may contain balanced or
        unbalanced panels.

Parallel Constraint Satisfaction (PCS) models are an increasingly
    common class of models in Psychology, with applications to reading and word
    recognition (McClelland & Rumelhart, 1981), judgment and decision making
    (Glöckner & Betsch, 2008; Glöckner, Hilbig, & Jekel, 2014), and several
    other fields (e.g. Read, Vanman, & Miller, 1997). In each of these fields,
    they provide a quantitative model of psychological phenomena, with precise
    predictions regarding choice probabilities, decision times, and often the degree
    of confidence. This package provides the necessary functions to create and
    simulate basic Parallel Constraint Satisfaction networks within R.