Small collection of functions for dyadic binary/dichotomous sequence analyses, e.g. transforming sequences into time-to-event data, implementation of Bakeman & Gottman's (1997) approach of aggregated logit-models, and simulating expected number of low/zero frequencies for state-transition tables. Further functions will be added in future releases. References: Bakeman, R., & Gottman, J. M. (1997) <DOI:10.1017/cbo9780511527685>.

Functions for dyadic binary/dichotomous sequence analyses are implemented in this contribution.
The focus is on estimating actor-partner-interaction models using various approaches, for
instances the approach of Bakeman & Gottman's (1997) <DOI:10.1017/cbo9780511527685>,
generalized multi-level models, and basic Markov models. Moreover, coefficients of one model
can be translated into those of the other models. Finally, simulation-based power analyses are
provided.

Quadratic Programming using Cyclic Projections
Solves quadratic programming problems using Richard L. Dykstra's cyclic projection algorithm. Routine allows for a combination of equality and inequality constraints. See Dykstra (1983) <doi:10.1080/01621459.1983.10477029> for details.

non-parametric denoising and clustering method of noisy images
both indexed by time and space
Yves Rozenholc, Christophe Pouzat, Tiffany Lieury
The aim of the method is first to denoise each temporal
sequence using both the spatial and temporal information
contained in the data, and then to cluster them according to
their dynamic features. The method is based on the assumption
that a finite sequence of noisy images both indexed by time and
space is composed of noisy versions of only a limited amount of
dynamic features.

Two-stage method for the denoising and clustering of stack
of noisy images acquired over time, based on the simple
assumption that a finite sequence of noisy images both indexed
by time and space is composed of noisy versions of only a
limited amount of dynamic features. The aim of the method is
first to denoise signals using both the spatial and temporal
information contained in the data, and then cluster the
denoised signals depending on their dynamic features. Two
signals are considered to have similar features if their
difference does not significantly deviate from zero. By
comparing difference signals, no assumption is therefore made
on the shape of the theoretical signals. In order for the
method to be applicable to experimental data, the data should
be normally distributed (or at least follow a symmetric
distribution) with a constant variance. Also the number of
observations n must be of the form n=d^2. Moreover, the method
is based on the implicit assumption that, for a given data set,
almost each dynamic feature is present in two or more pixels.
The use of the method can be time consumming depending on the
size of the data-array (for example about 2 hours for
130x175x60 data set on a PC dual core processor 2.80 GHz, RAM 4
Go, Ubuntu Maverick 64 bits, R 2.12.1).