Maintainer: Rafael A. Irizarry <rafael_irizarry@dfci.harvard.edu>

Description: Dynamic slicing is a method designed for dependency detection between a categorical variable and a continuous variable. It could be applied for non-parametric hypothesis testing and gene set enrichment analysis.

Depends: R (>= 2.10), stats, utils, Rcpp (>= 0.11.1), ggplot2 (>=

Maintainer: David L. Miller <dave@ninepointeightone.net>
Title: Density surface modelling of distance sampling data
Author: David L. Miller, Eric Rexstad, Louise Burt, Mark V. Bravington,
Description: This library implements density surface modelling of line
transect data, based on the methods of Hedley et al. (2004).
Some recent developments in the literature have also be
incorporated.  Outputs are point and interval estimates of
population abundance and density.  Please note that this
version of dsm WILL NOT work with the Windows package DISTANCE.
For a version that works with DISTANCE, please go to
and S.T. Buckland.  2004. "Spatial distance sampling methods"
pp 48-70 in Advanced Distance Sampling, Buckland, S.T. et al.
Depends: R (>= 2.10.0), mgcv (>= 1.7), mrds,ggplot2,Distance
Collate: 'dsm.R' 'predict.dsm.R' 'make.soapgrid.R' 'zzz.R'

Title: Density surface modelling (dsm) of distance sampling data
population abundance and density.  Hedley, S.L., S.T. Buckland
and D.L. Borchers.  2004. "Spatial distance sampling methods"

transect data, based on the methods of Hedley et al. (2004). Some
recent developments in the literature have also be incorporated.
Outputs are point and interval estimates of population abundance
Please note that this version of dsm WILL NOT work with the Windows
package DISTANCE. For a version that works with DISTANCE, please go
For the latest version of dsm, please use the github version at the URL
Miller, D. L., M. L. Burt, E. Rexstad and L. Thomas. 2013. Spatial
models for distance sampling data: recent developments and future
directions. Methods in Ecology and Evolution
Hedley, S.L.,  S.T. Buckland and D.L. Borchers. 2004. "Spatial
distance sampling methods" pp 48-70 in Advanced Distance Sampling,
Buckland, S.T. et al. (eds). Oxford University Press.
Depends: R (>= 2.10.0), mgcv (>= 1.7),nlme,ggplot2,mrds