Assist novice developers when preparing a single package or a set of integrated packages to submit to CRAN. Automate the following individual or batch processing: check local source packages;  build local .tar.gz source files; install packages from local .tar.gz files; detect conflicts between function names in the environment.

Assist novice developers when preparing a single package or a set of integrated packages to submit to CRAN. Provide additional resources to facilitate the automation of the following individual or batch processing: check local source packages;  build local .tar.gz source files; install packages from local .tar.gz files; detect conflicts between function names in the environment. The additional resources include determining the identity and ordering of the packages to process when updating an imported package.

Provides bridge equations with optional autoregressive terms for
    nowcasting low-frequency macroeconomic variables (e.g. quarterly GDP) from
    higher-frequency indicators (e.g. monthly retail sales). Handles the
    ragged-edge problem where different indicators have different publication
    lags via mixed-frequency alignment. Includes pseudo-real-time evaluation
    with expanding or rolling windows, and the Diebold-Mariano test for
    comparing forecast accuracy following Harvey, Leybourne, and Newbold
    (1997) <doi:10.1016/S0169-2070(96)00719-4>. No API calls; designed to
    work with data from any source.

Nowcasting using the Chain-Ladder method. Supports both non-cumulative delay-based estimation and model-based completeness fitting (e.g., using logistic or Gompertz curves) to predict final counts from partially reported data.

The noweb system for source code, implemented in R.

Nonparametric (and semiparametric) kernel methods that seamlessly handle a mix of continuous, unordered, and ordered factor data types. We would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC, <https://www.nserc-crsng.gc.ca/>), the Social Sciences and Humanities Research Council of Canada (SSHRC, <https://www.sshrc-crsh.gc.ca/>), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, <https://sharcnet.ca/>). We would also like to acknowledge the contributions of the GNU GSL authors. In particular, we adapt the GNU GSL B-spline routine gsl_bspline.c adding automated support for quantile knots (in addition to uniform knots), providing missing functionality for derivatives, and for extending the splines beyond their endpoints.