Design and Analysis of Locally or Globally Efficient Adaptive
Designs
person("Kosuke", "Kashiwabara", email = "kashiwabara-tky@umin.ac.jp", role = c("aut", "cre"))
Provides the functions for planning and conducting a
clinical trial with adaptive sample size determination. Maximal statistical
efficiency will be exploited even when dramatic or multiple adaptations
are made. Such a trial consists of adaptive determination of sample size
at an interim analysis and implementation of frequentist statistical test at the
interim and final analysis with a prefixed significance level. The required
assumptions for the stage-wise test statistics are independent and stationary
increments and normality. Predetermination of adaptation rule is not required.

Perform first- and second-order multi-scale analyses
derived from Ripley's K-function, for univariate, multivariate
and marked mapped data in rectangular, circular or irregular
shaped sampling windows, with test of statitical significance
based on Monte Carlo simulations.

Perform first- and second-order multi-scale analyses
derived from Ripley's K-function, for univariate, multivariate
and marked mapped data in rectangular, circular or irregular
shaped sampling windows, with test of statistical significance
based on Monte Carlo simulations.

Perform first- and second-order multi-scale analyses derived from Ripley's K-function, for univariate,
multivariate and marked mapped data in rectangular, circular or irregular shaped sampling windows, with test of
statitical significance based on Monte Carlo simulations.

Perform first- and second-order multi-scale analyses derived from Ripley K-function, for univariate,
multivariate and marked mapped data in rectangular, circular or irregular shaped sampling windows, with test of
statistical significance based on Monte Carlo simulations.

Perform first- and second-order multi-scale analyses derived from Ripley K-function, for univariate,
multivariate and marked mapped data in rectangular, circular or irregular shaped sampling windows, with tests of
statistical significance based on Monte Carlo simulations.