Package for time value of money calculation, time series analysis and computational finance.

Parse and create Darwin Core (<http://rs.tdwg.org/dwc/>) Simple
    and Archives. Functionality includes reading and parsing all the
    files in a Darwin Core Archive, including the datasets and metadata;
    read and parse simple Darwin Core files; and validation of Darwin
    Core Archives.

Estimation and regularization for covariance matrix of asset
    returns. For covariance matrix estimation, three major types of factor
    models are included: macroeconomic factor model, fundamental factor model and
    statistical factor model. For covariance matrix regularization, four regularized
    estimators are included: banding, tapering, hard-thresholding and soft-
    thresholding. The tuning parameters of these regularized estimators are selected
    via cross-validation.

Creates a HTML widget which displays the results of searching
    for a pattern in files in a given folder. The results can be viewed in 
    the 'RStudio' viewer pane, included in a 'R Markdown' document or in a 
    'Shiny' application. Also provides a 'Shiny' application allowing to run 
    this widget and to navigate in the files found by the search. Instead of
    creating a HTML widget, it is also possible to get the results of the 
    search in a 'tibble'. The search is performed by the 'grep' command-line 
    utility.

Creates a HTML widget which displays the results of searching for a pattern in files in a given 'git' repository, including all its branches. The results can also be returned in a dataframe.

The heterogeneous treatment effect estimation procedure 
        proposed by Imai and Ratkovic (2013)<DOI: 10.1214/12-AOAS593>.  
        The proposed method is applicable, for
        example, when selecting a small number of most (or least)
        efficacious treatments from a large number of alternative
        treatments as well as when identifying subsets of the
        population who benefit (or are harmed by) a treatment of
        interest. The method adapts the Support Vector Machine
        classifier by placing separate LASSO constraints over the
        pre-treatment parameters and causal heterogeneity parameters of
        interest. This allows for the qualitative distinction between
        causal and other parameters, thereby making the variable
        selection suitable for the exploration of causal heterogeneity. 	
        The package also contains a class of functions, CausalANOVA, 
        which estimates the average marginal interaction effects (AMIEs)
        by a regularized ANOVA as proposed by Egami and Imai (2019)<DOI:10.1080/01621459.2018.1476246>. 
        It contains a variety of regularization techniques to facilitate 
	analysis of large factorial experiments.