Converting Weekly Data to Monthly Data (wktmo)
Converts weekly data to monthly data. Users can use three types of week formats: ISO week, epidemiology week (epi week) and calendar date.

Create Doxygen Documentation for Source Code (rdoxygen)
Create doxygen documentation for source code in R packages. Includes a RStudio Addin, that allows to trigger the doxygenize process.

Attribute Sampling Plan with Exact Hypergeometric Probabilities using Chebyshev Polynomials (hypersampleplan)
Implements an algorithm for efficient and exact calculation of hypergeometric and binomial probabilities using Chebyshev polynomials, while other algorithm use an approximation when N is large. A useful applications is also considered in this package for the construction of attribute sampling plans which is an important field of statistical quality control. The quantile, and the confidence limit for the attribute sampling plan are also implemented in this package. The hypergeometric distribution can be represented in terms of Chebyshev polynomials. This representation is particularly useful in the calculation of exact values of hypergeometric variables.

Inference and Prediction in an Illness-Death Model (survidm)
Newly developed methods for the estimation of several probabilities in an illness-death model. The package can be used to obtain nonparametric and semiparametric estimates for: transition probabilities, occupation probabilities, cumulative incidence function and the sojourn time distributions. Several auxiliary functions are also provided which can be used for marginal estimation of the survival functions.

Compute Node Overlap and Segregation in Ecological Networks (nos)
Calculate NOS (node overlap and segregation) and the associated metrics described in Strona and Veech (2015) <DOI:10.1111/2041-210X.12395> and Strona et al. (2017; In Press, DOI to be provided in subsequent package version). The functions provided in the package enable assessment of structural patterns ranging from complete node segregation to perfect nestedness in a variety of network types. In addition, they provide a measure of network modularity.