Obtain least-squares means for linear, generalized linear, 
    and mixed models. Compute contrasts or linear functions of 
    least-squares means, and comparisons of slopes. 
    Plots and compact letter displays. Least-squares means were proposed in
    Harvey, W (1960) "Least-squares analysis of data with unequal subclass numbers",
    Tech Report ARS-20-8, USDA National Agricultural Library, and discussed
    further in Searle, Speed, and Milliken (1980) "Population marginal means 
    in the linear model: An alternative to least squares means", 
    The American Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>.
    NOTE: lsmeans now relies primarily on code in the 'emmeans' package.
    'lsmeans' will be archived in the near future.

In Latent Space Item Response Models, subjects and items are embedded in a multidimensional Euclidean latent space. 
	As such, interactions among persons, items, and person-item combinations can be revealed that are unmodelled in 
	more conventional item response theory models. This package implements the methods from Molenaar & Jeon (in press) and can be used to fit Latent Space Item Response Models 
	to data using joint maximum likelihood estimation. The package can handle binary data, ordinal data, and data with mixed scales. 
	The package incorporates facilities for data simulation, rotation of the latent space, and K-fold cross-validation to select the 
	number of dimensions of the latent space.

The package compiles functions for calculating prices of American put options with Least Squares Monte Carlo method. The option types are plain vanilla American put, Asian American put, and Quanto American put. The pricing algorithms include variance reduction techniques such as Antithetic Variates and Control Variates. Additional functions are given to derive "price surfaces" at different volatilities and strikes, create 3-D plots, quickly generate Geometric Brownian motion, and calculate prices of European options with Black & Scholes analytical solution.