Download and generate summaries for the rodent,
    plant, ant, and weather data from the Portal Project. Portal is a
    long-term (and ongoing) experimental monitoring site in the Chihuahua
    desert. The raw data files can be found at
    <https://github.com/weecology/portaldata>.

Contains common univariate and multivariate portmanteau test statistics for time series models. These tests are based on using asymptotic distributions such as chi-square distribution and based on using the Monte Carlo significance tests. Also, it can be used to simulate from univariate and multivariate seasonal time series models.

Classes for analysing and implementing equity portfolios,
    including routines for generating tradelists and calculating
    exposures to user-specified risk factors.

Simplify your portfolio optimization process by applying a contemporary modeling way to model and solve your portfolio problems. While most approaches and packages are rather complicated this one tries to simplify things and is agnostic regarding risk measures as well as optimization solvers. Some of the methods implemented are described by Konno and Yamazaki (1991) <doi:10.1287/mnsc.37.5.519>, Rockafellar and Uryasev (2001) <doi:10.21314/JOR.2000.038> and Markowitz (1952) <doi:10.1111/j.1540-6261.1952.tb01525.x>.

Portfolio optimization and analysis routines and graphics.

Automated backtesting of multiple portfolios over multiple 
    datasets of stock prices in a rolling-window fashion. Intended for 
    researchers and practitioners to backtest a set of different portfolios, 
    as well as by a course instructor to assess the students in their portfolio 
    design in a fully automated and convenient manner, with results conveniently 
    formatted in tables and plots. Each portfolio design is easily defined as a
    function that takes as input a window of the stock prices and outputs the 
    portfolio weights. Multiple portfolios can be easily specified as a list 
    of functions or as files in a folder. Multiple datasets can be conveniently 
    extracted randomly from different markets, different time periods, and 
    different subsets of the stock universe. The results can be later assessed 
    and ranked with tables based on a number of performance criteria (e.g., 
    expected return, volatility, Sharpe ratio, drawdown, turnover rate, return 
    on investment, computational time, etc.), as well as plotted in a number of 
    ways with nice barplots and boxplots.