A fold change rank based method is presented to search for genes with changing
expression and to detect recurrent chromosomal copy number aberrations. This
method may be useful for high-throughput biological data (micro-array, sequencing, ...).
Probabilities are associated with genes or probes in the data set and there is no
problem of multiple tests when using this method. For array-based comparative genomic
hybridization data, segmentation results are obtained by merging the significant
probes detected.

Easy-to-use, very fast implementation of various functional bases. Easily used together with other packages.
A functional basis is a collection of basis functions [\phi_1, ..., \phi_n] that can represent a smooth function, i.e. $f(t) = \sum c_k \phi_k(t)$.
As of version 1.0, this package includes B-splines, Fourier bases and polynomials.

Easy-to-use, very fast implementation of various functional bases. Easily used together with other packages.
A functional basis is a collection of basis functions [\phi_1, ..., \phi_n] that can represent a smooth function, i.e. $f(t) = \sum c_k \phi_k(t)$.
First- and second-order derivatives are also included. These are the mathematically correct ones, no approximations applied.
As of version 1.0, this package includes B-splines, Fourier bases and polynomials.

Easy-to-use, very fast implementation of various functional bases. Easily used together with other packages.
A functional basis is a collection of basis functions [\phi_1, ..., \phi_n] that can represent a smooth function, i.e. $f(t) = \sum c_k \phi_k(t)$.
First- and second-order derivatives are also included. These are the mathematically correct ones, no approximations applied.
As of version 1.1, this package includes B-splines, Fourier bases and polynomials.

Automatically suggests a correction when a typo occurs.

Analysis of Variance for Univariate and Multivariate Functional
Data
Performs analysis of variance testing procedures for univariate and multivariate functional data (Cuesta-Albertos and Febrero-Bande (2010) <doi:10.1007/s11749-010-0185-3>, Gorecki and Smaga (2015) <doi:10.1007/s00180-015-0555-0>, Gorecki and Smaga (2017) <doi:10.1080/02664763.2016.1247791>, Zhang et al. (2016) <arXiv:1309.7376v1>).
fda, doParallel, ggplot2, doBy, MASS, magic, parallel, foreach