Performs parallel analysis (Timmerman & Lorenzo-Seva, 2011 
    <doi:10.1037/a0023353>) and hull method (Lorenzo-Seva, Timmerman, & Kiers, 
    2011 <doi:10.1080/00273171.2011.564527>) for assessing the dimensionality
    of a set of variables using minimum rank factor analysis (see ten Berge & 
    Kiers, 1991 <doi:10.1007/BF02294464> for more information). The package 
    also includes the option to compute minimum rank factor analysis by 
    itself, as well as the greater lower bound calculation.

Provides functions to perform exploratory factor analysis (EFA) procedures and compare their solutions. The goal is to provide state-of-the-art factor retention methods and a high degree of flexibility in the EFA procedures. This way, for example, implementations from R 'psych' and 'SPSS' can be compared. Moreover, functions for Schmid-Leiman transformation and the computation of omegas are provided. To speed up the analyses, some of the iterative procedures, like principal axis factoring (PAF), are implemented in C++.

A number of utility function for exploratory factor analysis are included in this package. In particular, it computes standard errors for parameter estimates and factor correlations under a variety of conditions.

An implementation of European Forestry Dynamics Model (EFDM) and
    an estimation algorithm for the transition probabilities.
    The EFDM is a large-scale forest model that simulates the development of
    the forest and estimates volume of wood harvested for any given forested
    area. This estimate can be broken down by, for example, species, site
    quality, management regime and ownership category.
    See Packalen et al. (2015) <doi:10.2788/153990>.

Enhanced False Discovery Rate (EFDR) is a tool to detect anomalies
    in an image. The image is first transformed into the wavelet domain in
    order to decorrelate any noise components, following which the coefficients
    at each resolution are standardised. Statistical tests (in a multiple
    hypothesis testing setting) are then carried out to find the anomalies. The
    power of EFDR exceeds that of standard FDR, which would carry out tests on
    every wavelet coefficient: EFDR choose which wavelets to test based on a
    criterion described in Shen et al. (2002). The package also provides
    elementary tools to interpolate spatially irregular data onto a grid of the
    required size. The work is based on Shen, X., Huang, H.-C., and Cressie, N.
    'Nonparametric hypothesis testing for a spatial signal.' Journal of the
    American Statistical Association 97.460 (2002): 1122-1140.