The sparse vector field consensus 
		(SparseVFC) algorithm (Ma et al., 2013 <doi:10.1016/j.patcog.2013.05.017>) for robust vector 
		field learning. Largely translated from the Matlab functions in <https://github.com/jiayi-ma/VFC>.

Fast 'SVMlight' reader and writer. 
    'SVMlight' is most commonly used format for storing 
    sparse matrices (possibly with some target variable) on disk.
    For additional information about 'SVMlight' format see <http://svmlight.joachims.org/>.

Fast Multiplication and Marginalization of Sparse Tables.

Statistical pattern recognition and dating using archaeological artefacts assemblages.
    Package of statistical tools for archaeology.
    hclustcompro(perioclust): Bellanger Lise, Coulon Arthur, Husi Philippe (2021, ISBN:978-3-030-60103-4).
    mapclust: Bellanger Lise, Coulon Arthur, Husi Philippe (2021) <doi:10.1016/j.jas.2021.105431>.
    seriograph: Desachy Bruno (2004) <doi:10.3406/pica.2004.2396>.
    cerardat: Bellanger Lise, Husi Philippe (2012) <doi:10.1016/j.jas.2011.06.031>.

This is an implementation of the algorithm described in Section 3 of Hosszejni and Frühwirth-Schnatter (2022) <doi:10.48550/arXiv.2211.00671>. The algorithm is used to verify that the counting rule CR(r,1) holds for the sparsity pattern of the transpose of a factor loading matrix. As detailed in Section 2 of the same paper, if CR(r,1) holds, then the idiosyncratic variances are generically identified. If CR(r,1) does not hold, then we do not know whether the idiosyncratic variances are identified or not.

The Stratified-Petersen Analysis System (SPAS) is designed
    to estimate abundance in two-sample capture-recapture experiments 
    where the capture and recaptures are stratified. This is a generalization
    of the simple Lincoln-Petersen estimator.
    Strata may be defined in time or in space or both, 
    and the s strata in which marking takes place 
    may differ from the t strata in which recoveries take place.
    When s=t, SPAS reduces to the method described by 
    Darroch (1961) <doi:10.2307/2332748>.
    When s<t, SPAS implements the methods described in
    Plante, Rivest, and Tremblay (1988) <doi:10.2307/2533994>.
    Schwarz and Taylor (1998) <doi:10.1139/f97-238> describe
    the use of SPAS in estimating return of salmon stratified by
    time and geography. 
    A related package, BTSPAS, deals with temporal stratification where 
    a spline is used to model the distribution of the population 
    over time as it passes the second capture location.
    This is the R-version of the (now obsolete) standalone Windows 
    program available at <https://home.cs.umanitoba.ca/~popan/spas/spas_home.html>.