% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/seqcomp-package.R
\name{seqcomp-package}
\alias{seqcomp-package}
\alias{seqcomp}
\title{seqcomp: Sequential Comparison of Probabilistic Forecasts}
\description{
\code{seqcomp} provides tools for comparing probabilistic forecasters
sequentially, following the anytime-valid framework of Choe and Ramdas
(2024).
}
\details{
The package is built around the score difference

\deqn{\hat{\delta}_t = S(p_t, y_t) - S(q_t, y_t),}

where scores are positively oriented, so larger values are better. Positive
score differences favour forecaster \code{p}; negative score differences favour
forecaster \code{q}.
}
\section{Main workflow}{

For most applications, start with \code{\link[=compare_forecasts]{compare_forecasts()}}. It computes
pointwise scores, running mean score differences, confidence sequences, and
e-processes in one call.
}

\section{Scoring rules}{

The package includes positively oriented scoring rules such as
\code{\link[=brier_score]{brier_score()}}, \code{\link[=log_score]{log_score()}}, \code{\link[=spherical_score]{spherical_score()}}, \code{\link[=tick_loss]{tick_loss()}},
\code{\link[=qlike_score]{qlike_score()}}, \code{\link[=winkler_score]{winkler_score()}}, \code{\link[=crps_normal]{crps_normal()}}, \code{\link[=crps_empirical]{crps_empirical()}},
and \code{\link[=crps_std]{crps_std()}}.
}

\section{Confidence sequences}{

Use \code{\link[=cs_hoeffding]{cs_hoeffding()}} for Hoeffding-style confidence sequences,
\code{\link[=cs_bernstein]{cs_bernstein()}} for empirical Bernstein confidence sequences, and
\code{\link[=cs_asymptotic]{cs_asymptotic()}} for asymptotic confidence sequences when finite-sample
boundedness is not available.
}

\section{E-processes}{

Use \code{\link[=eprocess]{eprocess()}} for the main sub-exponential mixture e-process and
\code{\link[=eprocess_rejections]{eprocess_rejections()}} to extract first rejection times. For multi-step
forecasts, see \code{\link[=eprocess_lag]{eprocess_lag()}}. For predictable time-varying bounds, see
\code{\link[=eprocess_predictable]{eprocess_predictable()}}.
}

\section{Winkler scores}{

For binary probability forecasts with unbounded base scores, use
\code{\link[=winkler_score]{winkler_score()}}, \code{\link[=winkler_cs]{winkler_cs()}}, \code{\link[=winkler_etest]{winkler_etest()}}, or
\code{\link[=winkler_compare]{winkler_compare()}}.
}

\references{
Choe, Y. J. and Ramdas, A. (2024). Comparing Sequential Forecasters.
Operations Research, 72(4), 1368-1387.

Howard, S. R., Ramdas, A., McAuliffe, J. and Sekhon, J. (2021).
Time-uniform, nonparametric, nonasymptotic confidence sequences.
The Annals of Statistics, 49(2).
}
