Package: DEMEtics
Type: Package
Title: Calculation of Gst and D values characterizing the genetic
differentiation between populations.
Version: 0.8.1
Date: 2010-02-11
Author: Alexander Jueterbock, Philipp Kraemer, Gabriele Gerlach and
Jana Deppermann
Maintainer: Alexander Jueterbock, <Alexander-Jueterbock@web.de>
Depends: R (>= 2.8.0)
Description: Out of a table comprising allelic data of at least one
locus for several individuals and from at least two
populations, measurements of genetic differentiation (i.e. D,
Dest, Dest.Chao, Gst and Gst.est (Jost, 2008)) can be
calculated. P-values and 95% confidence intervals are
calculated according to Manly (1997) using a bootstrap method.
Thereby, for each locus, alleles are reallocated over
populations if they are all in Hardy Weinberg Equilibrium
(HWE). If not, the genotypes are reallocated, since alleles can
not be assumed to be inherited independently from each other
(Goudet, 1996). The p-values state how much the empirical found
allocation of alleles over populations differs from an
allocation that could arise by chance alone. In the case that
more than two populations are compared pairwise, the p-values
are adjusted besides using bonferroni correction in several
other ways due to the multiple comparison from one data set.
License: GPL (>= 2)
Literature: Goudet, J., M. Raymond, et al. (1996). Testing
differentiation in diploid populations. Genetics 144(4):
1933-1940. Jost, L. (2008). Gst and its relatives do not
measure differentiation. Molecular Ecology 17(18): 4015-4026.
Manly, B. F. J. Randomization, bootstrap and Monte Carlo
methods in biology. Chapman & Hall, 1997. Nei M., Chesser R.
Estimation of fixation indices and gene diversities. Annals of
Human Genetics, 1983, 47, 253-259. Wright, S. P. Adjusted
P-Values for simultaneous inference. Biometrics, 1992, 48,
1005-1013.
LazyLoad: yes
Packaged: 2010-08-15 12:33:44 UTC; alexj
Repository: CRAN
Date/Publication: 2010-08-16 13:44:56

Title: DEvelopment (of Multi-Linear QSPR/QSAR) MOdels VAlidated using
Description: Tool for the development of multi-linear QSPR/QSAR models (Quantitative structure-property/activity relationship). Theses models are used in chemistry, biology and pharmacy to find a relationship between the structure of a molecule and its property (such as activity, toxicology but also physical properties). The various functions of this package allows: selection of descriptors based of variances, intercorrelation and user expertise; selection of the best multi-linear regression in terms of correlation and robustness; methods of internal validation (Leave-One-Out, Leave-Many-Out, Y-scrambling) and external using test sets.