Title: Statistical Matching or Data Fusion
Description: Integration of two data sources referred to the same target population which share a number of variables. Some functions can also be used to impute missing values in data sets through hot deck imputation methods. Methods to perform statistical matching when dealing  with data from complex sample surveys are available too.

Depends: R (>= 2.7.0), proxy, survey, lpSolve, ggplot2
Suggests: Hmisc, MASS, mipfp, R.rsp, clue, RANN,

Suggests: Hmisc, MASS, mipfp, clue, RANN,

Title: Easy Data Manipulation, Data Quality and Statistical Checks
Description: Offers useful functions to perform day-to-day data manipulation
operations, data quality checks and post modelling statistical checks.
One can effortlessly change class of a number of variables to factor,
remove duplicate observations from the data, create deciles of a
variable, perform data quality checks for continuous (integer or numeric),
categorical (factor) and date variables, and compute goodness of fit
measures such as auc for statistical models. The functions are consistent
for objects of class 'data.frame' and 'data.table', which is an enhanced
'data.frame' implemented in the package 'data.table'.

Title: Statistical Methods for Ranking Data
Description: Functions and datasets to support Statistical Methods for Ranking Data by Mayer Alvo and Philip L.H. Yu(2014)

Description: Functions and datasets to support Statistical Methods for Ranking Data by Mayer Alvo and Philip L.H. Yu(2014). In this packages, smaller ranks stand for more preferred items.
Imports: stats, graphics, datasets, Rcpp (>= 0.11.5), MASS, utils,

Description: Functions and datasets to support Mayer Alvo and Philip L.H. Yu (2014). "Statistical Methods for Ranking Data", Springer. In this packages, smaller ranks stand for more preferred items.

Title: Statistical Performance Measures to Evaluate Covariance Matrix
Description: Statistical performance measures used in the econometric literature to evaluate conditional covariance/correlation matrix estimates (MSE, MAE, Euclidean distance, Frobenius distance, Stein distance, asymmetric loss function, eigenvalue loss function and the loss function defined in Eq. (4.6) of Engle et al. (2016) <doi:10.2139/ssrn.2814555>). Additionally, compute Eq. (3.1) and (4.2) of Li et al. (2016) <doi:10.1080/07350015.2015.1092975> to compare the factor loading matrix. The statistical performance measures implemented have been previously used in, for instance, Laurent et al. (2012) <doi:10.1002/jae.1248>,  Amendola et al. (2015) <doi:10.1002/for.2322> and  Becker et al. (2015) <doi:10.1016/j.ijforecast.2013.11.007>.
Note: The author acknowledges financial support from Sao Paulo Research
Packaged: 2017-04-14 17:27:22 UTC; ctruciosm