Parametric and nonparametric statistics for single-case design. Regarding nonparametric statistics, the index suggested by Parker, Vannest, Davis and Sauber (2011) <doi:10.1016/j.beth.2010.08.006> was included. It combines both nonoverlap and trend to estimate the effect size of a treatment in a single case design.

The Causal Effects for a Causal Loglinear Model
Fitting a causal loglinear model and calculating the causal effects for a causal loglinear model with the multiplicative interaction or without the multiplicative interaction, obtaining the natural direct, indirect and the total effect. It calculates also the cell effect, which is a new interaction effect.

This package contains the functions to compute standardized
effect sizes for experiments.

This package contains the functions to compute the standardized
effect sizes for experiments (Cohen d, Hedges g, Cliff delta).
Using highly efficient algorithms.

This package contains the functions to compute the standardized
effect sizes for experiments (Cohen d, Hedges g, Cliff delta, Vargha and Delaney A).
The computation algorithms have been optimized to allow efficient computation even
with very large data sets.

c(person("Marco", "Torchiano", role = c("aut", "cre"),
email="marco.torchiano@polito.it"))
A collection of functions to compute the standardized
effect sizes for experiments (Cohen d, Hedges g, Cliff delta, Vargha-Delaney A).
The computation algorithms have been optimized to allow efficient computation even
with very large data sets.

Efficient Fitting of Linear and Generalized Linear Models
c(
person(given = "Mauricio",
family = "Vargas",
role = c("aut", "cre"),
email = "mvargas@dcc.uchile.cl",
comment = c(ORCID = "0000-0003-1017-7574")),
person(given = "Alexey",
family = "Kravchenko",
role = c("ths"),
email = "kravchenkoa@un.org"),
person(given = "Constanza",
family = "Prado",
role = c("ths"),
email = "mcprado@uc.cl"),
person(given = "Yoto",
family = "Yotov",
role = c("ths"),
email = "yvy23@drexel.edu"),
person(family = "The United Nations",
role = c("dtc", "cph", "fnd")
)
)
Efficient Fitting of Linear and Generalized Linear Models by using
just base R. As an alternative to lm() and glm(), this package provides elm()
and eglm(), with a significant speedup when the number of
observations is larger than the number of parameters to estimate, as it
reduces the NxP model matrix to a PxP matrix. The best computational
performance is obtained when R is linked against 'OpenBLAS', 'Intel MKL' or
other optimized 'BLAS' library. This implementation aims at being compatible
with 'broom' and 'sandwich' packages for summary statistics and clustering by
providing S3 methods.
testthat (>= 3.0.0), covr, sandwich, MASS, tibble, broom
Mauricio Vargas [aut, cre] (<https://orcid.org/0000-0003-1017-7574>),
Alexey Kravchenko [ths],
Constanza Prado [ths],
Yoto Yotov [ths],
The United Nations [dtc, cph, fnd]