The development of post-processing functionality for simulated snow profiles 
    by the snow and avalanche community is often done in 'python'. This package
    aims to make some of these tools accessible to 'R' users. Currently integrated 
    modules contain functions to calculate dry snow layer instabilities in support 
    of avalache hazard assessments following the publications of Richter, 
    Schweizer, Rotach, and Van Herwijnen (2019) <doi:10.5194/tc-13-3353-2019>, and 
    Mayer, Van Herwijnen, Techel, and Schweizer (2022) <doi:10.5194/tc-2022-34>.

Implements the basic elements of the multi-model
    inference paradigm for up to twenty species-area relationship models (SAR), using simple
    R list-objects and functions, as in Triantis et al. 2012 <DOI:10.1111/j.1365-2699.2011.02652.x>.
    The package is scalable and users can easily create their own model and data objects. Additional
    SAR related functions are provided.

A toolkit for Partially Observed Markov Decision Processes (POMDP). Provides
    bindings to C++ libraries implementing the algorithm SARSOP (Successive Approximations
    of the Reachable Space under Optimal Policies) and described in Kurniawati et al (2008),
    <doi:10.15607/RSS.2008.IV.009>.  This package also provides a high-level interface
    for generating, solving and simulating POMDP problems and their solutions.

Documentation and prototypes for the earliest (circa 2010) open-source effort to reverse engineer the sas7bdat file format. The package includes a prototype reader for sas7bdat files. However, newer packages may contain more robust readers for sas7bdat files.

Using any importation code designed for 'SAS' users to read ASCII files into 'sas7bdat' files, this package parses through the INPUT block of a '.sas' syntax file to design the parameters needed for a 'read.fwf()' function call.  This allows the user to specify the location of the ASCII (often a '.dat') file and the location of the 'SAS' syntax file, and then load the data frame directly into R in just one step.