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"dixon_coles") to
stan_foot(), static and dynamic, and to
mle_foot()."neg_bin") to
mle_foot().stan_foot()dynamic_par to choose the evolution variance: a
common variance (common_sd, Owen, 2011) or a variance
inflation after the summer break (kl_variance and
periods_per_season, Koopman and Lit, 2015).dynamic_weight for the weighted dynamic models with
commensurate priors (Macrì Demartino, Egidi and Torelli, 2026), with
spike and slab hyperpriors set through dynamic_par$spike
and dynamic_par$slab.print.stanFoot() with teams keeps all the
global parameters and shows the team names in the team-indexed
parameters.foot_prob() supports the "dixon_coles" and
"neg_bin" MLE models.mle_foot() to the
model-specific parameters and move the MLE simulations to the internal
simulate_goals_mle().y_rep and y_prev of the bivariate
Poisson models from the bivariate Poisson distribution.draw_dist for
the draws.diff_y_prev in the bivariate Poisson and
diagonal-inflated bivariate Poisson models.mle_foot() to count only the
free parameters of each model.foot_prob() for MLE models, which used
object$home instead of
object$home_effect.compare_foot() with NA rows in a
probability matrix, which misaligned the outcomes of the following
elements of source.ranking in stan_foot() to the teams
by name instead of by position, with informative errors for missing or
duplicated teams.pp_foot(type = "aggregated") for models fitted with
predict > 0.foot_abilities() for the six MLE models.italy, priors,
btd_foot(), compare_foot(),
plot_logStrength() and of norm_method and
rho in stan_foot().rstan interface to CmdStanR
interface.VI, pathfinder and
laplace algorithms.instantiate.mle_foot().stan_foot().ggplot2 edits on foot_rank(),
foot_abilities() and pp_foot().These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.