Provides several methods to integrate functions over the unit
 sphere and ball in n-dimensional Euclidean space.  Routines for converting to/from
 multivariate polar/spherical coordinates are also provided.

Functions for fitting Cliff-Ord-type spatial autoregressive models with and without heteroskedastic innovations using Generalized Method of Moments estimation are provided. Some support is available for fitting spatial HAC models, and for fitting with non-spatial endogeneous variables using instrumental variables.

Implementation of uniformity tests on the circle and
    (hyper)sphere. The main function of the package is unif_test(), which
    conveniently collects more than 35 tests for assessing uniformity on
    S^{p-1} = {x in R^p : ||x|| = 1}, p >= 2. The test statistics are 
    implemented in the unif_stat() function, which allows computing several
    statistics for different samples within a single call, thus facilitating
    Monte Carlo experiments. Furthermore, the unif_stat_MC() function allows
    parallelizing them in a simple way. The asymptotic null distributions of
    the statistics are available through the function unif_stat_distr(). The
    core of 'sphunif' is coded in C++ by relying on the 'Rcpp' package.
    The package also provides several novel datasets and gives the replicability
    for the data applications/simulations in García-Portugués et al. (2021)
    <doi:10.1007/978-3-030-69944-4_12>, García-Portugués et al. (2023)
    <doi:10.3150/21-BEJ1454>, Fernández-de-Marcos and
    García-Portugués (2024) <doi:10.1016/j.spl.2024.110218>, and
    García-Portugués et al. (2025) <doi:10.1080/01621459.2025.2566414>.

A set of functions allowing to implement the 'SpiceFP' approach
  which is iterative. It involves transformation of functional predictors into
  several candidate explanatory matrices (based on contingency tables), to which
  relative edge matrices with contiguity constraints are associated. Generalized
  Fused Lasso regression are performed in order to identify the best candidate
  matrix, the best class intervals and related coefficients at each iteration.
  The approach is stopped when the maximal number of iterations is reached or
  when retained coefficients are zeros. Supplementary functions allow to get
  coefficients of any candidate matrix or mean of coefficients of many candidates.
  The methods in this package are describing in Girault Gnanguenon Guesse, 
  Patrice Loisel, Bénedicte Fontez, Thierry Simonneau, Nadine Hilgert (2021)
  "An exploratory penalized regression to identify combined effects of functional
  variables -Application to agri-environmental issues" 
  <https://hal.archives-ouvertes.fr/hal-03298977>.