This package has been deprecated and will not be updated. 
    New users should use the package 'gprofiler2' (<https://CRAN.R-project.org/package=gprofiler2>)
    for up-to-date data and improved functionality.
    Functional enrichment analysis, gene identifier conversion and
    mapping homologous genes across related organisms via the 'g:Profiler' toolkit
    (<https://biit.cs.ut.ee/gprofiler/>).

A toolset for functional enrichment analysis and visualization, gene/protein/SNP identifier conversion and mapping orthologous genes across species via 'g:Profiler' (<https://biit.cs.ut.ee/gprofiler/>). 
    The main tools are: 
    (1) 'g:GOSt' - functional enrichment analysis and visualization of gene lists; 
    (2) 'g:Convert' - gene/protein/transcript identifier conversion across various namespaces;
    (3) 'g:Orth' - orthology search across species;
    (4) 'g:SNPense' - mapping SNP rs identifiers to chromosome positions, genes and variant effects.
    This package is an R interface corresponding to the 2019 update of 'g:Profiler' and provides access to 'g:Profiler' for versions 'e94_eg41_p11' and higher. See the package 'gProfileR' for accessing older versions from the 'g:Profiler' toolset.

General P-splines are non-uniform B-splines penalized by a general difference penalty, proposed by Li and Cao (2022) <arXiv:2201.06808>. Constructible on arbitrary knots, they extend the standard P-splines of Eilers and Marx (1996) <doi:10.1214/ss/1038425655>. They are also related to the O-splines of O'Sullivan (1986) <doi:10.1214/ss/1177013525> via a sandwich formula that links a general difference penalty to a derivative penalty. The package includes routines for setting up and handling difference and derivative penalties. It also fits P-splines and O-splines to (x, y) data (optionally weighted) for a grid of smoothing parameter values in the automatic search intervals of Li and Cao (2023) <doi:10.1007/s11222-022-10178-z>. It aims to facilitate other packages to implement P-splines or O-splines as a smoothing tool in their model estimation framework.