Datasets and functions that facilitate data analysis labs in data science courses and workshops.

Datasets and functions that can be used for data analysis practice, homework and projects in data science courses and workshops.

Datasets and functions that can be used for data analysis practice, homework and projects in data science courses and workshops. 26 datasets are available for case studies in data visualization, statistical inference, modeling, linear regression, data wrangling and machine learning.

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.

Density surface modelling of distance sampling data
David L. Miller, Eric Rexstad, Louise Burt, Mark V. Bravington,
Sharon Hedley.
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
https://github.com/lenthomas/dsm-distance-6.1.  Hedley, S.L.
and S.T. Buckland.  2004. "Spatial distance sampling methods"
pp 48-70 in Advanced Distance Sampling, Buckland, S.T. et al.
(eds). Oxford University Press.
R (>= 2.10.0), mgcv (>= 1.7), mrds,ggplot2,Distance
'dsm.R' 'predict.dsm.R' 'make.soapgrid.R' 'zzz.R'
'dsm.var.movblk.R' 'dsm.var.prop.R' 'trim.var.R'
'generate.ds.uncertainty.R' 'generate.mb.sample.R'
'block.info.per.su.R' 'dsm-package.R' 'latlong2km.R'
'ddf.model.description.R' 'print.summary.dsm.var.R'
'summary.dsm.var.R' 'print.dsm.R' 'print.dsm.var.R'
'plot.dsm.var.R' 'dsm.var.gam.R' 'make.data.R' 'summary.dsm.R'
'plot.dsm.R' 'matrixnotposdef.handler.R'

Density surface modelling (dsm) of distance sampling data
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.  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.