Cache R Expressions, Taking Their Dependencies into Account
person('Ivan', 'Krylov', email = 'krylov.r00t@gmail.com',
role = c('aut', 'cre'))
Hash an expression with its dependencies and store its
value, reloading it from a file as long as both the expression and
its dependencies stay the same.

Functions to compute coefficients measuring the dependence of two or more than two variables. The functions can be deployed to gain information about functional dependencies of the variables with emphasis on monotone functions. The statistics describe how well one response variable can be approximated by a monotone function of other variables. In regression analysis the variable selection is an important issue. In this framework the functions could be useful tools in modeling the regression function. Detailed explanations on the subject can be found in papers Liebscher (2014) <doi:10.2478/demo-2014-0004>; Liebscher (2017) <doi:10.1515/demo-2017-0012>; Liebscher (2019, submitted).

Inference for parametric and nonparametric models for dependent
truncation data
Takeshi EMURA, Graduate Institute of Statistics, National
Central University, Taiwan
The package performs parametric and nonparametric
inferences for models with dependent truncation data.

Inference for parametric and semiparametric models based on
dependent truncation data
The package performs parametric and semiparametric
inferences for models based on dependent truncation data

Statistical inference for parametric and semiparametric models
based on dependently truncated data
The package performs parametric and semiparametric inferences for models based on dependently truncated data. Semi-parametric approaches are based on Chaieb, Rivest and Abdous (2006 Biometrika) and Emura, Wang and Hung (2011 Statistica Sinica). Parametric approaches are based on Emura and Konno (2012 Statistical Papers).

Statistical Inference for Parametric and Semiparametric Models
Based on Dependently Truncated Data
Suppose that one can observe bivariate random variables (X, Y) only when X<=Y holds. Data (X_j, Y_j), subject to X_j<=Y_j, for all j=1,...,n, are called truncated data. Parametric approach (Emura & Konno 2012 Stat Papers), semiparametric approach (Chaieb et al. 2006 Biometrika; Emura et al. 2011 Sinica), and the nonparametric maximum likelihood approach (Emura & Wang 2012 JMVA) are implemented for statistical inference on (X, Y), when X and Y are dependent. Also included is truncated data on the number of deaths at each year (1963-1980) for Japanese male centenarians (Emura and Murotani 2015).