Methods for detecting structural breaks and estimating break
    locations for linear multiple regression models under general linear
    restrictions on the coefficient vector. Restrictions can be within
    regimes, across regimes, or both, and are supported in two forms: an
    affine parameterization (Form A: delta = S*theta + s) and explicit
    linear constraints (Form B: R*delta = r). Provides break date
    estimation with confidence intervals, a restricted sup-F test for the
    null of no structural change, simulation of critical values by Monte
    Carlo, and a bootstrap restart procedure to reduce the risk of
    convergence to spurious local optima. Also implements a generalized
    regression tree (linear model tree) procedure where each leaf contains
    a linear regression model rather than a local average. Reference:
    Perron, P., and Qu, Z. (2006). 'Estimating Restricted Structural
    Change Models.' Journal of Econometrics, 134(2), 373-399.
    <doi:10.1016/j.jeconom.2005.06.030>.

Provides a fast calculation of the Blyth-Still-Casella confidence interval. The implementation follows the 'StatXact' 9 manual (Cytel 2010) and "Refining Binomial Confidence Intervals" by George Casella (1986) <doi:10.2307/3314658>.

Implementation of the RPC-JSON API for Bitcoin and utility functions for address creation and content analysis of the blockchain.

The regression-based (RB) approach is a method to test the missing data mechanism.
			This package contains two functions that test the type of missing data (Missing Completely 
			At Random vs Missing At Random) on the basis of the RB approach. The first function applies 
			the RB approach independently on each variable with missing data, using the completely 
			observed variables only. The second function tests the missing data mechanism globally 
			(on all variables with missing data) with the use of all available information. The 
			algorithm is adapted both to continuous and categorical data.

Tu & Zhou (1999) <doi:10.1002/(SICI)1097-0258(19991030)18:20%3C2749::AID-SIM195%3E3.0.CO;2-C> showed that comparing the means of populations whose data-generating distributions are non-negative with excess zero observations is a problem of great importance in the analysis of medical cost data. In the same study, Tu & Zhou discuss that it can be difficult to control type-I error rates of general-purpose statistical tests for comparing the means of these particular data sets. This package allows users to perform a modified bootstrap-based t-test that aims to better control type-I error rates in these situations.