Provides functions to generate censored samples of type I, II and III, from any random sample generator. It also supplies the option to create left and right censorship. Along with this, the generation of samples with interval censoring is in the  testing phase, with two options of fixed length intervals and random lengths.

To facilitate using 'cereal' with Rcpp.
    'cereal' is a header-only C++11 serialization library.
    'cereal' takes arbitrary data types and reversibly turns them into
    different representations, such as compact binary encodings, XML,
    or JSON. 'cereal' was designed to be fast, light-weight, and easy
    to extend - it has no external dependencies and can be easily
    bundled with other code or used standalone. Please see
    <http://uscilab.github.io/cereal> for more information.

Set of analytical procedures based on advanced data analysis in support of Brazil's public sector external control activity.

Download and open manifest files provided by the Copernicus Global Land Service data <https://land.copernicus.eu/global/>. The manifest files are available at: <https://land.copernicus.vgt.vito.be/manifest/>. Also see: <https://land.copernicus.eu/global/access/>. Before you can download the data, you will first need to register to create a username and password.

A simple data science challenge system using R Markdown and 'Dropbox' <https://www.dropbox.com/>.
    It requires no network configuration, does not depend on external platforms
    like e.g. 'Kaggle' <https://www.kaggle.com/> and can be easily installed on a personal computer.

Data exploration and prediction with focus on high dimensional data and chemometrics.
    The package was initially designed about partial least squares regression and discrimination models and variants, in particular locally weighted PLS models (LWPLS). Then, it has been expanded to many other methods for analyzing high dimensional data.
    The name 'rchemo' comes from the fact that the package is orientated to chemometrics, but most of the provided methods are fully generic to other domains.
    Functions such as transform(), predict(), coef() and summary() are available. Tuning the predictive models is facilitated by generic functions gridscore() (validation dataset) and gridcv() (cross-validation). Faster versions are also available for models based on latent variables (LVs) (gridscorelv() and gridcvlv()) and ridge regularization (gridscorelb() and gridcvlb()).