Provided by: tcllib_1.21+dfsg-1_all 
NAME
math::statistics - Basic statistical functions and procedures
SYNOPSIS
package require Tcl 8.5
package require math::statistics 1
::math::statistics::mean data
::math::statistics::min data
::math::statistics::max data
::math::statistics::number data
::math::statistics::stdev data
::math::statistics::var data
::math::statistics::pstdev data
::math::statistics::pvar data
::math::statistics::median data
::math::statistics::basic-stats data
::math::statistics::histogram limits values ?weights?
::math::statistics::histogram-alt limits values ?weights?
::math::statistics::corr data1 data2
::math::statistics::interval-mean-stdev data confidence
::math::statistics::t-test-mean data est_mean est_stdev alpha
::math::statistics::test-normal data significance
::math::statistics::lillieforsFit data
::math::statistics::test-Duckworth list1 list2 significance
::math::statistics::test-anova-F alpha args
::math::statistics::test-Tukey-range alpha args
::math::statistics::test-Dunnett alpha control args
::math::statistics::quantiles data confidence
::math::statistics::quantiles limits counts confidence
::math::statistics::autocorr data
::math::statistics::crosscorr data1 data2
::math::statistics::mean-histogram-limits mean stdev number
::math::statistics::minmax-histogram-limits min max number
::math::statistics::linear-model xdata ydata intercept
::math::statistics::linear-residuals xdata ydata intercept
::math::statistics::test-2x2 n11 n21 n12 n22
::math::statistics::print-2x2 n11 n21 n12 n22
::math::statistics::control-xbar data ?nsamples?
::math::statistics::control-Rchart data ?nsamples?
::math::statistics::test-xbar control data
::math::statistics::test-Rchart control data
::math::statistics::test-Kruskal-Wallis confidence args
::math::statistics::analyse-Kruskal-Wallis args
::math::statistics::test-Levene groups
::math::statistics::test-Brown-Forsythe groups
::math::statistics::group-rank args
::math::statistics::test-Wilcoxon sample_a sample_b
::math::statistics::spearman-rank sample_a sample_b
::math::statistics::spearman-rank-extended sample_a sample_b
::math::statistics::kernel-density data opt -option value ...
::math::statistics::bootstrap data sampleSize ?numberSamples?
::math::statistics::wasserstein-distance prob1 prob2
::math::statistics::kl-divergence prob1 prob2
::math::statistics::logistic-model xdata ydata
::math::statistics::logistic-probability coeffs x
::math::statistics::tstat dof ?alpha?
::math::statistics::mv-wls wt1 weights_and_values
::math::statistics::mv-ols values
::math::statistics::pdf-normal mean stdev value
::math::statistics::pdf-lognormal mean stdev value
::math::statistics::pdf-exponential mean value
::math::statistics::pdf-uniform xmin xmax value
::math::statistics::pdf-triangular xmin xmax value
::math::statistics::pdf-symmetric-triangular xmin xmax value
::math::statistics::pdf-gamma alpha beta value
::math::statistics::pdf-poisson mu k
::math::statistics::pdf-chisquare df value
::math::statistics::pdf-student-t df value
::math::statistics::pdf-gamma a b value
::math::statistics::pdf-beta a b value
::math::statistics::pdf-weibull scale shape value
::math::statistics::pdf-gumbel location scale value
::math::statistics::pdf-pareto scale shape value
::math::statistics::pdf-cauchy location scale value
::math::statistics::pdf-laplace location scale value
::math::statistics::pdf-kumaraswamy a b value
::math::statistics::pdf-negative-binomial r p value
::math::statistics::cdf-normal mean stdev value
::math::statistics::cdf-lognormal mean stdev value
::math::statistics::cdf-exponential mean value
::math::statistics::cdf-uniform xmin xmax value
::math::statistics::cdf-triangular xmin xmax value
::math::statistics::cdf-symmetric-triangular xmin xmax value
::math::statistics::cdf-students-t degrees value
::math::statistics::cdf-gamma alpha beta value
::math::statistics::cdf-poisson mu k
::math::statistics::cdf-beta a b value
::math::statistics::cdf-weibull scale shape value
::math::statistics::cdf-gumbel location scale value
::math::statistics::cdf-pareto scale shape value
::math::statistics::cdf-cauchy location scale value
::math::statistics::cdf-F nf1 nf2 value
::math::statistics::cdf-laplace location scale value
::math::statistics::cdf-kumaraswamy a b value
::math::statistics::cdf-negative-binomial r p value
::math::statistics::empirical-distribution values
::math::statistics::random-normal mean stdev number
::math::statistics::random-lognormal mean stdev number
::math::statistics::random-exponential mean number
::math::statistics::random-uniform xmin xmax number
::math::statistics::random-triangular xmin xmax number
::math::statistics::random-symmetric-triangular xmin xmax number
::math::statistics::random-gamma alpha beta number
::math::statistics::random-poisson mu number
::math::statistics::random-chisquare df number
::math::statistics::random-student-t df number
::math::statistics::random-beta a b number
::math::statistics::random-weibull scale shape number
::math::statistics::random-gumbel location scale number
::math::statistics::random-pareto scale shape number
::math::statistics::random-cauchy location scale number
::math::statistics::random-laplace location scale number
::math::statistics::random-kumaraswamy a b number
::math::statistics::random-negative-binomial r p number
::math::statistics::histogram-uniform xmin xmax limits number
::math::statistics::incompleteGamma x p ?tol?
::math::statistics::incompleteBeta a b x ?tol?
::math::statistics::estimate-pareto values
::math::statistics::estimate-exponential values
::math::statistics::estimate-laplace values
::math::statistics::estimante-negative-binomial r values
::math::statistics::filter varname data expression
::math::statistics::map varname data expression
::math::statistics::samplescount varname list expression
::math::statistics::subdivide
::math::statistics::plot-scale canvas xmin xmax ymin ymax
::math::statistics::plot-xydata canvas xdata ydata tag
::math::statistics::plot-xyline canvas xdata ydata tag
::math::statistics::plot-tdata canvas tdata tag
::math::statistics::plot-tline canvas tdata tag
::math::statistics::plot-histogram canvas counts limits tag
_________________________________________________________________________________________________
DESCRIPTION
The math::statistics package contains functions and procedures for basic statistical data
analysis, such as:
• Descriptive statistical parameters (mean, minimum, maximum, standard deviation)
• Estimates of the distribution in the form of histograms and quantiles
• Basic testing of hypotheses
• Probability and cumulative density functions
It is meant to help in developing data analysis applications or doing ad hoc data
analysis, it is not in itself a full application, nor is it intended to rival with full
(non-)commercial statistical packages.
The purpose of this document is to describe the implemented procedures and provide some
examples of their usage. As there is ample literature on the algorithms involved, we refer
to relevant text books for more explanations. The package contains a fairly large number
of public procedures. They can be distinguished in three sets: general procedures,
procedures that deal with specific statistical distributions, list procedures to select or
transform data and simple plotting procedures (these require Tk). Note: The data that
need to be analyzed are always contained in a simple list. Missing values are represented
as empty list elements. Note: With version 1.0.1 a mistake in the procs pdf-lognormal,
cdf-lognormal and random-lognormal has been corrected. In previous versions the argument
for the standard deviation was actually used as if it was the variance.
GENERAL PROCEDURES
The general statistical procedures are:
::math::statistics::mean data
Determine the mean value of the given list of data.
list data
- List of data
::math::statistics::min data
Determine the minimum value of the given list of data.
list data
- List of data
::math::statistics::max data
Determine the maximum value of the given list of data.
list data
- List of data
::math::statistics::number data
Determine the number of non-missing data in the given list
list data
- List of data
::math::statistics::stdev data
Determine the sample standard deviation of the data in the given list
list data
- List of data
::math::statistics::var data
Determine the sample variance of the data in the given list
list data
- List of data
::math::statistics::pstdev data
Determine the population standard deviation of the data in the given list
list data
- List of data
::math::statistics::pvar data
Determine the population variance of the data in the given list
list data
- List of data
::math::statistics::median data
Determine the median of the data in the given list (Note that this requires sorting
the data, which may be a costly operation)
list data
- List of data
::math::statistics::basic-stats data
Determine a list of all the descriptive parameters: mean, minimum, maximum, number
of data, sample standard deviation, sample variance, population standard deviation
and population variance.
(This routine is called whenever either or all of the basic statistical parameters
are required. Hence all calculations are done and the relevant values are
returned.)
list data
- List of data
::math::statistics::histogram limits values ?weights?
Determine histogram information for the given list of data. Returns a list
consisting of the number of values that fall into each interval. (The first
interval consists of all values lower than the first limit, the last interval
consists of all values greater than the last limit. There is one more interval
than there are limits.)
Optionally, you can use weights to influence the histogram.
list limits
- List of upper limits (in ascending order) for the intervals of the
histogram.
list values
- List of data
list weights
- List of weights, one weight per value
::math::statistics::histogram-alt limits values ?weights?
Alternative implementation of the histogram procedure: the open end of the
intervals is at the lower bound instead of the upper bound.
list limits
- List of upper limits (in ascending order) for the intervals of the
histogram.
list values
- List of data
list weights
- List of weights, one weight per value
::math::statistics::corr data1 data2
Determine the correlation coefficient between two sets of data.
list data1
- First list of data
list data2
- Second list of data
::math::statistics::interval-mean-stdev data confidence
Return the interval containing the mean value and one containing the standard
deviation with a certain level of confidence (assuming a normal distribution)
list data
- List of raw data values (small sample)
float confidence
- Confidence level (0.95 or 0.99 for instance)
::math::statistics::t-test-mean data est_mean est_stdev alpha
Test whether the mean value of a sample is in accordance with the estimated normal
distribution with a certain probability. Returns 1 if the test succeeds or 0 if
the mean is unlikely to fit the given distribution.
list data
- List of raw data values (small sample)
float est_mean
- Estimated mean of the distribution
float est_stdev
- Estimated stdev of the distribution
float alpha
- Probability level (0.95 or 0.99 for instance)
::math::statistics::test-normal data significance
Test whether the given data follow a normal distribution with a certain level of
significance. Returns 1 if the data are normally distributed within the level of
significance, returns 0 if not. The underlying test is the Lilliefors test. Smaller
values of the significance mean a stricter testing.
list data
- List of raw data values
float significance
- Significance level (one of 0.01, 0.05, 0.10, 0.15 or 0.20). For
compatibility reasons the values "1-significance", 0.80, 0.85, 0.90, 0.95 or
0.99 are also accepted.
Compatibility issue: the original implementation and documentation used the term
"confidence" and used a value 1-significance (see ticket 2812473fff). This has been
corrected as of version 0.9.3.
::math::statistics::lillieforsFit data
Returns the goodness of fit to a normal distribution according to Lilliefors. The
higher the number, the more likely the data are indeed normally distributed. The
test requires at least five data points.
list data
- List of raw data values
::math::statistics::test-Duckworth list1 list2 significance
Determine if two data sets have the same median according to the Tukey-Duckworth
test. The procedure returns 0 if the medians are unequal, 1 if they are equal, -1
if the test can not be conducted (the smallest value must be in a different set
than the greatest value). # # Arguments: # list1 Values in the first
data set # list2 Values in the second data set # significance
Significance level (either 0.05, 0.01 or 0.001) # # Returns: Test whether the given
data follow a normal distribution with a certain level of significance. Returns 1
if the data are normally distributed within the level of significance, returns 0 if
not. The underlying test is the Lilliefors test. Smaller values of the significance
mean a stricter testing.
list list1
- First list of data
list list2
- Second list of data
float significance
- Significance level (either 0.05, 0.01 or 0.001)
::math::statistics::test-anova-F alpha args
Determine if two or more groups with normally distributed data have the same means.
The procedure returns 0 if the means are likely unequal, 1 if they are. This is a
one-way ANOVA test. The groups may also be stored in a nested list: The procedure
returns a list of the comparison results for each pair of groups. Each element of
this list contains: the index of the first group and that of the second group,
whether the means are likely to be different (1) or not (0) and the confidence
interval the conclusion is based on. The groups may also be stored in a nested
list:
test-anova-F 0.05 $A $B $C
#
# Or equivalently:
#
test-anova-F 0.05 [list $A $B $C]
float alpha
- Significance level
list args
- Two or more groups of data to be checked
::math::statistics::test-Tukey-range alpha args
Determine if two or more groups with normally distributed data have the same means,
using Tukey's range test. It is complementary to the ANOVA test. The procedure
returns a list of the comparison results for each pair of groups. Each element of
this list contains: the index of the first group and that of the second group,
whether the means are likely to be different (1) or not (0) and the confidence
interval the conclusion is based on. The groups may also be stored in a nested
list, just as with the ANOVA test.
float alpha
- Significance level - either 0.05 or 0.01
list args
- Two or more groups of data to be checked
::math::statistics::test-Dunnett alpha control args
Determine if one or more groups with normally distributed data have the same means
as the group of control data, using Dunnett's test. It is complementary to the
ANOVA test. The procedure returns a list of the comparison results for each group
with the control group. Each element of this list contains: whether the means are
likely to be different (1) or not (0) and the confidence interval the conclusion is
based on. The groups may also be stored in a nested list, just as with the ANOVA
test.
Note: some care is required if there is only one group to compare the control with:
test-Dunnett-F 0.05 $control [list $A]
Otherwise the group A is split up into groups of one element - this is due to an
ambiguity.
float alpha
- Significance level - either 0.05 or 0.01
list args
- One or more groups of data to be checked
::math::statistics::quantiles data confidence
Return the quantiles for a given set of data
list data
- List of raw data values
float confidence
- Confidence level (0.95 or 0.99 for instance) or a list of confidence
levels.
::math::statistics::quantiles limits counts confidence
Return the quantiles based on histogram information (alternative to the call with
two arguments)
list limits
- List of upper limits from histogram
list counts
- List of counts for for each interval in histogram
float confidence
- Confidence level (0.95 or 0.99 for instance) or a list of confidence
levels.
::math::statistics::autocorr data
Return the autocorrelation function as a list of values (assuming equidistance
between samples, about 1/2 of the number of raw data)
The correlation is determined in such a way that the first value is always 1 and
all others are equal to or smaller than 1. The number of values involved will
diminish as the "time" (the index in the list of returned values) increases
list data
- Raw data for which the autocorrelation must be determined
::math::statistics::crosscorr data1 data2
Return the cross-correlation function as a list of values (assuming equidistance
between samples, about 1/2 of the number of raw data)
The correlation is determined in such a way that the values can never exceed 1 in
magnitude. The number of values involved will diminish as the "time" (the index in
the list of returned values) increases.
list data1
- First list of data
list data2
- Second list of data
::math::statistics::mean-histogram-limits mean stdev number
Determine reasonable limits based on mean and standard deviation for a histogram
Convenience function - the result is suitable for the histogram function.
float mean
- Mean of the data
float stdev
- Standard deviation
int number
- Number of limits to generate (defaults to 8)
::math::statistics::minmax-histogram-limits min max number
Determine reasonable limits based on a minimum and maximum for a histogram
Convenience function - the result is suitable for the histogram function.
float min
- Expected minimum
float max
- Expected maximum
int number
- Number of limits to generate (defaults to 8)
::math::statistics::linear-model xdata ydata intercept
Determine the coefficients for a linear regression between two series of data (the
model: Y = A + B*X). Returns a list of parameters describing the fit
list xdata
- List of independent data
list ydata
- List of dependent data to be fitted
boolean intercept
- (Optional) compute the intercept (1, default) or fit to a line through the
origin (0)
The result consists of the following list:
• (Estimate of) Intercept A
• (Estimate of) Slope B
• Standard deviation of Y relative to fit
• Correlation coefficient R2
• Number of degrees of freedom df
• Standard error of the intercept A
• Significance level of A
• Standard error of the slope B
• Significance level of B
::math::statistics::linear-residuals xdata ydata intercept
Determine the difference between actual data and predicted from the linear model.
Returns a list of the differences between the actual data and the predicted values.
list xdata
- List of independent data
list ydata
- List of dependent data to be fitted
boolean intercept
- (Optional) compute the intercept (1, default) or fit to a line through the
origin (0)
::math::statistics::test-2x2 n11 n21 n12 n22
Determine if two set of samples, each from a binomial distribution, differ
significantly or not (implying a different parameter).
Returns the "chi-square" value, which can be used to the determine the
significance.
int n11
- Number of outcomes with the first value from the first sample.
int n21
- Number of outcomes with the first value from the second sample.
int n12
- Number of outcomes with the second value from the first sample.
int n22
- Number of outcomes with the second value from the second sample.
::math::statistics::print-2x2 n11 n21 n12 n22
Determine if two set of samples, each from a binomial distribution, differ
significantly or not (implying a different parameter).
Returns a short report, useful in an interactive session.
int n11
- Number of outcomes with the first value from the first sample.
int n21
- Number of outcomes with the first value from the second sample.
int n12
- Number of outcomes with the second value from the first sample.
int n22
- Number of outcomes with the second value from the second sample.
::math::statistics::control-xbar data ?nsamples?
Determine the control limits for an xbar chart. The number of data in each
subsample defaults to 4. At least 20 subsamples are required.
Returns the mean, the lower limit, the upper limit and the number of data per
subsample.
list data
- List of observed data
int nsamples
- Number of data per subsample
::math::statistics::control-Rchart data ?nsamples?
Determine the control limits for an R chart. The number of data in each subsample
(nsamples) defaults to 4. At least 20 subsamples are required.
Returns the mean range, the lower limit, the upper limit and the number of data per
subsample.
list data
- List of observed data
int nsamples
- Number of data per subsample
::math::statistics::test-xbar control data
Determine if the data exceed the control limits for the xbar chart.
Returns a list of subsamples (their indices) that indeed violate the limits.
list control
- Control limits as returned by the "control-xbar" procedure
list data
- List of observed data
::math::statistics::test-Rchart control data
Determine if the data exceed the control limits for the R chart.
Returns a list of subsamples (their indices) that indeed violate the limits.
list control
- Control limits as returned by the "control-Rchart" procedure
list data
- List of observed data
::math::statistics::test-Kruskal-Wallis confidence args
Check if the population medians of two or more groups are equal with a given
confidence level, using the Kruskal-Wallis test.
float confidence
- Confidence level to be used (0-1)
list args
- Two or more lists of data
::math::statistics::analyse-Kruskal-Wallis args
Compute the statistical parameters for the Kruskal-Wallis test. Returns the
Kruskal-Wallis statistic and the probability that that value would occur assuming
the medians of the populations are equal.
list args
- Two or more lists of data
::math::statistics::test-Levene groups
Compute the Levene statistic to determine if groups of data have the same variance
(are homoscadastic) or not. The data are organised in groups. This version uses the
mean of the data as the measure to determine the deviations. The statistic is
equivalent to an F statistic with degrees of freedom k-1 and N-k, k being the
number of groups and N the total number of data.
list groups
- List of groups of data
::math::statistics::test-Brown-Forsythe groups
Compute the Brown-Forsythe statistic to determine if groups of data have the same
variance (are homoscadastic) or not. Like the Levene test, but this version uses
the median of the data.
list groups
- List of groups of data
::math::statistics::group-rank args
Rank the groups of data with respect to the complete set. Returns a list
consisting of the group ID, the value and the rank (possibly a rational number, in
case of ties) for each data item.
list args
- Two or more lists of data
::math::statistics::test-Wilcoxon sample_a sample_b
Compute the Wilcoxon test statistic to determine if two samples have the same
median or not. (The statistic can be regarded as standard normal, if the sample
sizes are both larger than 10.) Returns the value of this statistic.
list sample_a
- List of data comprising the first sample
list sample_b
- List of data comprising the second sample
::math::statistics::spearman-rank sample_a sample_b
Return the Spearman rank correlation as an alternative to the ordinary (Pearson's)
correlation coefficient. The two samples should have the same number of data.
list sample_a
- First list of data
list sample_b
- Second list of data
::math::statistics::spearman-rank-extended sample_a sample_b
Return the Spearman rank correlation as an alternative to the ordinary (Pearson's)
correlation coefficient as well as additional data. The two samples should have the
same number of data. The procedure returns the correlation coefficient, the number
of data pairs used and the z-score, an approximately standard normal statistic,
indicating the significance of the correlation.
list sample_a
- First list of data
list sample_b
- Second list of data
::math::statistics::kernel-density data opt -option value ...
Return the density function based on kernel density estimation. The procedure is
controlled by a small set of options, each of which is given a reasonable default.
The return value consists of three lists: the centres of the bins, the associated
probability density and a list of computational parameters (begin and end of the
interval, mean and standard deviation and the used bandwidth). The computational
parameters can be used for further analysis.
list data
- The data to be examined
list args
- Option-value pairs:
-weights weights
Per data point the weight (default: 1 for all data)
-bandwidth value
Bandwidth to be used for the estimation (default: determined from
standard deviation)
-number value
Number of bins to be returned (default: 100)
-interval {begin end}
Begin and end of the interval for which the density is returned
(default: mean +/- 3*standard deviation)
-kernel function
Kernel to be used (One of: gaussian, cosine, epanechnikov, uniform,
triangular, biweight, logistic; default: gaussian)
::math::statistics::bootstrap data sampleSize ?numberSamples?
Create a subsample or subsamples from a given list of data. The data in the samples
are chosen from this list - multiples may occur. If there is only one subsample,
the sample itself is returned (as a list of "sampleSize" values), otherwise a list
of samples is returned.
list data
List of values to chose from
int sampleSize
Number of values per sample
int numberSamples
Number of samples (default: 1)
::math::statistics::wasserstein-distance prob1 prob2
Compute the Wasserstein distance or earth mover's distance for two equidstantly
spaced histograms or probability densities. The histograms need not to be
normalised to sum to one, but they must have the same number of entries.
Note: the histograms are assumed to be based on the same equidistant intervals. As
the bounds are not passed, the value is expressed in the length of the intervals.
list prob1
List of values for the first histogram/probability density
list prob2
List of values for the second histogram/probability density
::math::statistics::kl-divergence prob1 prob2
Compute the Kullback-Leibler (KL) divergence for two equidstantly spaced histograms
or probability densities. The histograms need not to be normalised to sum to one,
but they must have the same number of entries.
Note: the histograms are assumed to be based on the same equidistant intervals. As
the bounds are not passed, the value is expressed in the length of the intervals.
Note also that the KL divergence is not symmetric and that the second histogram
should not contain zeroes in places where the first histogram has non-zero values.
list prob1
List of values for the first histogram/probability density
list prob2
List of values for the second histogram/probability density
::math::statistics::logistic-model xdata ydata
Estimate the coefficients of the logistic model that fits the data best. The data
consist of independent x-values and the outcome 0 or 1 for each of the x-values.
The result can be used to estimate the probability that a certain x-value gives 1.
list xdata
List of values for which the success (1) or failure (0) is known
list ydata
List of successes or failures corresponding to each value in xdata.
::math::statistics::logistic-probability coeffs x
Calculate the probability of success for the value x given the coefficients of the
logistic model.
list coeffs
List of coefficients as determine by the logistic-model command
float x
X-value for which the probability needs to be determined
MULTIVARIATE LINEAR REGRESSION
Besides the linear regression with a single independent variable, the statistics package
provides two procedures for doing ordinary least squares (OLS) and weighted least squares
(WLS) linear regression with several variables. They were written by Eric Kemp-Benedict.
In addition to these two, it provides a procedure (tstat) for calculating the value of the
t-statistic for the specified number of degrees of freedom that is required to demonstrate
a given level of significance.
Note: These procedures depend on the math::linearalgebra package.
Description of the procedures
::math::statistics::tstat dof ?alpha?
Returns the value of the t-distribution t* satisfying
P(t*) = 1 - alpha/2
P(-t*) = alpha/2
for the number of degrees of freedom dof.
Given a sample of normally-distributed data x, with an estimate xbar for the mean
and sbar for the standard deviation, the alpha confidence interval for the estimate
of the mean can be calculated as
( xbar - t* sbar , xbar + t* sbar)
The return values from this procedure can be compared to an estimated t-statistic
to determine whether the estimated value of a parameter is significantly different
from zero at the given confidence level.
int dof
Number of degrees of freedom
float alpha
Confidence level of the t-distribution. Defaults to 0.05.
::math::statistics::mv-wls wt1 weights_and_values
Carries out a weighted least squares linear regression for the data points
provided, with weights assigned to each point.
The linear model is of the form
y = b0 + b1 * x1 + b2 * x2 ... + bN * xN + error
and each point satisfies
yi = b0 + b1 * xi1 + b2 * xi2 + ... + bN * xiN + Residual_i
The procedure returns a list with the following elements:
• The r-squared statistic
• The adjusted r-squared statistic
• A list containing the estimated coefficients b1, ... bN, b0 (The constant b0
comes last in the list.)
• A list containing the standard errors of the coefficients
• A list containing the 95% confidence bounds of the coefficients, with each
set of bounds returned as a list with two values
Arguments:
list weights_and_values
A list consisting of: the weight for the first observation, the data for the
first observation (as a sublist), the weight for the second observation (as
a sublist) and so on. The sublists of data are organised as lists of the
value of the dependent variable y and the independent variables x1, x2 to
xN.
::math::statistics::mv-ols values
Carries out an ordinary least squares linear regression for the data points
provided.
This procedure simply calls ::mvlinreg::wls with the weights set to 1.0, and
returns the same information.
Example of the use:
# Store the value of the unicode value for the "+/-" character
set pm "\u00B1"
# Provide some data
set data {{ -.67 14.18 60.03 -7.5 }
{ 36.97 15.52 34.24 14.61 }
{-29.57 21.85 83.36 -7. }
{-16.9 11.79 51.67 -6.56 }
{ 14.09 16.24 36.97 -12.84}
{ 31.52 20.93 45.99 -25.4 }
{ 24.05 20.69 50.27 17.27}
{ 22.23 16.91 45.07 -4.3 }
{ 40.79 20.49 38.92 -.73 }
{-10.35 17.24 58.77 18.78}}
# Call the ols routine
set results [::math::statistics::mv-ols $data]
# Pretty-print the results
puts "R-squared: [lindex $results 0]"
puts "Adj R-squared: [lindex $results 1]"
puts "Coefficients $pm s.e. -- \[95% confidence interval\]:"
foreach val [lindex $results 2] se [lindex $results 3] bounds [lindex $results 4] {
set lb [lindex $bounds 0]
set ub [lindex $bounds 1]
puts " $val $pm $se -- \[$lb to $ub\]"
}
STATISTICAL DISTRIBUTIONS
In the literature a large number of probability distributions can be found. The statistics
package supports:
• The normal or Gaussian distribution as well as the log-normal distribution
• The uniform distribution - equal probability for all data within a given interval
• The exponential distribution - useful as a model for certain extreme-value
distributions.
• The gamma distribution - based on the incomplete Gamma integral
• The beta distribution
• The chi-square distribution
• The student's T distribution
• The Poisson distribution
• The Pareto distribution
• The Gumbel distribution
• The Weibull distribution
• The Cauchy distribution
• The F distribution (only the cumulative density function)
• PM - binomial.
In principle for each distribution one has procedures for:
• The probability density (pdf-*)
• The cumulative density (cdf-*)
• Quantiles for the given distribution (quantiles-*)
• Histograms for the given distribution (histogram-*)
• List of random values with the given distribution (random-*)
The following procedures have been implemented:
::math::statistics::pdf-normal mean stdev value
Return the probability of a given value for a normal distribution with given mean
and standard deviation.
float mean
- Mean value of the distribution
float stdev
- Standard deviation of the distribution
float value
- Value for which the probability is required
::math::statistics::pdf-lognormal mean stdev value
Return the probability of a given value for a log-normal distribution with given
mean and standard deviation.
float mean
- Mean value of the distribution
float stdev
- Standard deviation of the distribution
float value
- Value for which the probability is required
::math::statistics::pdf-exponential mean value
Return the probability of a given value for an exponential distribution with given
mean.
float mean
- Mean value of the distribution
float value
- Value for which the probability is required
::math::statistics::pdf-uniform xmin xmax value
Return the probability of a given value for a uniform distribution with given
extremes.
float xmin
- Minimum value of the distribution
float xmin
- Maximum value of the distribution
float value
- Value for which the probability is required
::math::statistics::pdf-triangular xmin xmax value
Return the probability of a given value for a triangular distribution with given
extremes. If the argument min is lower than the argument max, then smaller values
have higher probability and vice versa. In the first case the probability density
function is of the form f(x) = 2(1-x) and the other case it is of the form f(x) =
2x.
float xmin
- Minimum value of the distribution
float xmin
- Maximum value of the distribution
float value
- Value for which the probability is required
::math::statistics::pdf-symmetric-triangular xmin xmax value
Return the probability of a given value for a symmetric triangular distribution
with given extremes.
float xmin
- Minimum value of the distribution
float xmin
- Maximum value of the distribution
float value
- Value for which the probability is required
::math::statistics::pdf-gamma alpha beta value
Return the probability of a given value for a Gamma distribution with given shape
and rate parameters
float alpha
- Shape parameter
float beta
- Rate parameter
float value
- Value for which the probability is required
::math::statistics::pdf-poisson mu k
Return the probability of a given number of occurrences in the same interval (k)
for a Poisson distribution with given mean (mu)
float mu
- Mean number of occurrences
int k - Number of occurences
::math::statistics::pdf-chisquare df value
Return the probability of a given value for a chi square distribution with given
degrees of freedom
float df
- Degrees of freedom
float value
- Value for which the probability is required
::math::statistics::pdf-student-t df value
Return the probability of a given value for a Student's t distribution with given
degrees of freedom
float df
- Degrees of freedom
float value
- Value for which the probability is required
::math::statistics::pdf-gamma a b value
Return the probability of a given value for a Gamma distribution with given shape
and rate parameters
float a
- Shape parameter
float b
- Rate parameter
float value
- Value for which the probability is required
::math::statistics::pdf-beta a b value
Return the probability of a given value for a Beta distribution with given shape
parameters
float a
- First shape parameter
float b
- Second shape parameter
float value
- Value for which the probability is required
::math::statistics::pdf-weibull scale shape value
Return the probability of a given value for a Weibull distribution with given scale
and shape parameters
float location
- Scale parameter
float scale
- Shape parameter
float value
- Value for which the probability is required
::math::statistics::pdf-gumbel location scale value
Return the probability of a given value for a Gumbel distribution with given
location and shape parameters
float location
- Location parameter
float scale
- Shape parameter
float value
- Value for which the probability is required
::math::statistics::pdf-pareto scale shape value
Return the probability of a given value for a Pareto distribution with given scale
and shape parameters
float scale
- Scale parameter
float shape
- Shape parameter
float value
- Value for which the probability is required
::math::statistics::pdf-cauchy location scale value
Return the probability of a given value for a Cauchy distribution with given
location and shape parameters. Note that the Cauchy distribution has no finite
higher-order moments.
float location
- Location parameter
float scale
- Shape parameter
float value
- Value for which the probability is required
::math::statistics::pdf-laplace location scale value
Return the probability of a given value for a Laplace distribution with given
location and shape parameters. The Laplace distribution consists of two exponential
functions, is peaked and has heavier tails than the normal distribution.
float location
- Location parameter (mean)
float scale
- Shape parameter
float value
- Value for which the probability is required
::math::statistics::pdf-kumaraswamy a b value
Return the probability of a given value for a Kumaraswamy distribution with given
parameters a and b. The Kumaraswamy distribution is related to the Beta
distribution, but has a tractable cumulative distribution function.
float a
- Parameter a
float b
- Parameter b
float value
- Value for which the probability is required
::math::statistics::pdf-negative-binomial r p value
Return the probability of a given value for a negative binomial distribution with
an allowed number of failures and the probability of success.
int r - Allowed number of failures (at least 1)
float p
- Probability of success
int value
- Number of successes for which the probability is to be returned
::math::statistics::cdf-normal mean stdev value
Return the cumulative probability of a given value for a normal distribution with
given mean and standard deviation, that is the probability for values up to the
given one.
float mean
- Mean value of the distribution
float stdev
- Standard deviation of the distribution
float value
- Value for which the probability is required
::math::statistics::cdf-lognormal mean stdev value
Return the cumulative probability of a given value for a log-normal distribution
with given mean and standard deviation, that is the probability for values up to
the given one.
float mean
- Mean value of the distribution
float stdev
- Standard deviation of the distribution
float value
- Value for which the probability is required
::math::statistics::cdf-exponential mean value
Return the cumulative probability of a given value for an exponential distribution
with given mean.
float mean
- Mean value of the distribution
float value
- Value for which the probability is required
::math::statistics::cdf-uniform xmin xmax value
Return the cumulative probability of a given value for a uniform distribution with
given extremes.
float xmin
- Minimum value of the distribution
float xmin
- Maximum value of the distribution
float value
- Value for which the probability is required
::math::statistics::cdf-triangular xmin xmax value
Return the cumulative probability of a given value for a triangular distribution
with given extremes. If xmin < xmax, then lower values have a higher probability
and vice versa, see also pdf-triangular
float xmin
- Minimum value of the distribution
float xmin
- Maximum value of the distribution
float value
- Value for which the probability is required
::math::statistics::cdf-symmetric-triangular xmin xmax value
Return the cumulative probability of a given value for a symmetric triangular
distribution with given extremes.
float xmin
- Minimum value of the distribution
float xmin
- Maximum value of the distribution
float value
- Value for which the probability is required
::math::statistics::cdf-students-t degrees value
Return the cumulative probability of a given value for a Student's t distribution
with given number of degrees.
int degrees
- Number of degrees of freedom
float value
- Value for which the probability is required
::math::statistics::cdf-gamma alpha beta value
Return the cumulative probability of a given value for a Gamma distribution with
given shape and rate parameters.
float alpha
- Shape parameter
float beta
- Rate parameter
float value
- Value for which the cumulative probability is required
::math::statistics::cdf-poisson mu k
Return the cumulative probability of a given number of occurrences in the same
interval (k) for a Poisson distribution with given mean (mu).
float mu
- Mean number of occurrences
int k - Number of occurences
::math::statistics::cdf-beta a b value
Return the cumulative probability of a given value for a Beta distribution with
given shape parameters
float a
- First shape parameter
float b
- Second shape parameter
float value
- Value for which the probability is required
::math::statistics::cdf-weibull scale shape value
Return the cumulative probability of a given value for a Weibull distribution with
given scale and shape parameters.
float scale
- Scale parameter
float shape
- Shape parameter
float value
- Value for which the probability is required
::math::statistics::cdf-gumbel location scale value
Return the cumulative probability of a given value for a Gumbel distribution with
given location and scale parameters.
float location
- Location parameter
float scale
- Scale parameter
float value
- Value for which the probability is required
::math::statistics::cdf-pareto scale shape value
Return the cumulative probability of a given value for a Pareto distribution with
given scale and shape parameters
float scale
- Scale parameter
float shape
- Shape parameter
float value
- Value for which the probability is required
::math::statistics::cdf-cauchy location scale value
Return the cumulative probability of a given value for a Cauchy distribution with
given location and scale parameters.
float location
- Location parameter
float scale
- Scale parameter
float value
- Value for which the probability is required
::math::statistics::cdf-F nf1 nf2 value
Return the cumulative probability of a given value for an F distribution with nf1
and nf2 degrees of freedom.
float nf1
- Degrees of freedom for the numerator
float nf2
- Degrees of freedom for the denominator
float value
- Value for which the probability is required
::math::statistics::cdf-laplace location scale value
Return the cumulative probability of a given value for a Laplace distribution with
given location and shape parameters. The Laplace distribution consists of two
exponential functions, is peaked and has heavier tails than the normal
distribution.
float location
- Location parameter (mean)
float scale
- Shape parameter
float value
- Value for which the probability is required
::math::statistics::cdf-kumaraswamy a b value
Return the cumulative probability of a given value for a Kumaraswamy distribution
with given parameters a and b. The Kumaraswamy distribution is related to the Beta
distribution, but has a tractable cumulative distribution function.
float a
- Parameter a
float b
- Parameter b
float value
- Value for which the probability is required
::math::statistics::cdf-negative-binomial r p value
Return the cumulative probability of a given value for a negative binomial
distribution with an allowed number of failures and the probability of success.
int r - Allowed number of failures (at least 1)
float p
- Probability of success
int value
- Greatest number of successes
::math::statistics::empirical-distribution values
Return a list of values and their empirical probability. The values are sorted in
increasing order. (The implementation follows the description at the corresponding
Wikipedia page)
list values
- List of data to be examined
::math::statistics::random-normal mean stdev number
Return a list of "number" random values satisfying a normal distribution with given
mean and standard deviation.
float mean
- Mean value of the distribution
float stdev
- Standard deviation of the distribution
int number
- Number of values to be returned
::math::statistics::random-lognormal mean stdev number
Return a list of "number" random values satisfying a log-normal distribution with
given mean and standard deviation.
float mean
- Mean value of the distribution
float stdev
- Standard deviation of the distribution
int number
- Number of values to be returned
::math::statistics::random-exponential mean number
Return a list of "number" random values satisfying an exponential distribution with
given mean.
float mean
- Mean value of the distribution
int number
- Number of values to be returned
::math::statistics::random-uniform xmin xmax number
Return a list of "number" random values satisfying a uniform distribution with
given extremes.
float xmin
- Minimum value of the distribution
float xmax
- Maximum value of the distribution
int number
- Number of values to be returned
::math::statistics::random-triangular xmin xmax number
Return a list of "number" random values satisfying a triangular distribution with
given extremes. If xmin < xmax, then lower values have a higher probability and
vice versa (see also pdf-triangular.
float xmin
- Minimum value of the distribution
float xmax
- Maximum value of the distribution
int number
- Number of values to be returned
::math::statistics::random-symmetric-triangular xmin xmax number
Return a list of "number" random values satisfying a symmetric triangular
distribution with given extremes.
float xmin
- Minimum value of the distribution
float xmax
- Maximum value of the distribution
int number
- Number of values to be returned
::math::statistics::random-gamma alpha beta number
Return a list of "number" random values satisfying a Gamma distribution with given
shape and rate parameters.
float alpha
- Shape parameter
float beta
- Rate parameter
int number
- Number of values to be returned
::math::statistics::random-poisson mu number
Return a list of "number" random values satisfying a Poisson distribution with
given mean.
float mu
- Mean of the distribution
int number
- Number of values to be returned
::math::statistics::random-chisquare df number
Return a list of "number" random values satisfying a chi square distribution with
given degrees of freedom.
float df
- Degrees of freedom
int number
- Number of values to be returned
::math::statistics::random-student-t df number
Return a list of "number" random values satisfying a Student's t distribution with
given degrees of freedom.
float df
- Degrees of freedom
int number
- Number of values to be returned
::math::statistics::random-beta a b number
Return a list of "number" random values satisfying a Beta distribution with given
shape parameters.
float a
- First shape parameter
float b
- Second shape parameter
int number
- Number of values to be returned
::math::statistics::random-weibull scale shape number
Return a list of "number" random values satisfying a Weibull distribution with
given scale and shape parameters.
float scale
- Scale parameter
float shape
- Shape parameter
int number
- Number of values to be returned
::math::statistics::random-gumbel location scale number
Return a list of "number" random values satisfying a Gumbel distribution with given
location and scale parameters.
float location
- Location parameter
float scale
- Scale parameter
int number
- Number of values to be returned
::math::statistics::random-pareto scale shape number
Return a list of "number" random values satisfying a Pareto distribution with given
scale and shape parameters.
float scale
- Scale parameter
float shape
- Shape parameter
int number
- Number of values to be returned
::math::statistics::random-cauchy location scale number
Return a list of "number" random values satisfying a Cauchy distribution with given
location and scale parameters.
float location
- Location parameter
float scale
- Scale parameter
int number
- Number of values to be returned
::math::statistics::random-laplace location scale number
Return a list of "number" random values satisfying a Laplace distribution with
given location and shape parameters. The Laplace distribution consists of two
exponential functions, is peaked and has heavier tails than the normal
distribution.
float location
- Location parameter (mean)
float scale
- Shape parameter
int number
- Number of values to be returned
::math::statistics::random-kumaraswamy a b number
Return a list of "number" random values satisying a Kumaraswamy distribution with
given parameters a and b. The Kumaraswamy distribution is related to the Beta
distribution, but has a tractable cumulative distribution function.
float a
- Parameter a
float b
- Parameter b
int number
- Number of values to be returned
::math::statistics::random-negative-binomial r p number
Return a list of "number" random values satisying a negative binomial distribution.
int r - Allowed number of failures (at least 1)
float p
- Probability of success
int number
- Number of values to be returned
::math::statistics::histogram-uniform xmin xmax limits number
Return the expected histogram for a uniform distribution.
float xmin
- Minimum value of the distribution
float xmax
- Maximum value of the distribution
list limits
- Upper limits for the buckets in the histogram
int number
- Total number of "observations" in the histogram
::math::statistics::incompleteGamma x p ?tol?
Evaluate the incomplete Gamma integral
1 / x p-1
P(p,x) = -------- | dt exp(-t) * t
Gamma(p) / 0
float x
- Value of x (limit of the integral)
float p
- Value of p in the integrand
float tol
- Required tolerance (default: 1.0e-9)
::math::statistics::incompleteBeta a b x ?tol?
Evaluate the incomplete Beta integral
float a
- First shape parameter
float b
- Second shape parameter
float x
- Value of x (limit of the integral)
float tol
- Required tolerance (default: 1.0e-9)
::math::statistics::estimate-pareto values
Estimate the parameters for the Pareto distribution that comes closest to the given
values. Returns the estimated scale and shape parameters, as well as the standard
error for the shape parameter.
list values
- List of values, assumed to be distributed according to a Pareto
distribution
::math::statistics::estimate-exponential values
Estimate the parameter for the exponential distribution that comes closest to the
given values. Returns an estimate of the one parameter and of the standard error.
list values
- List of values, assumed to be distributed according to an exponential
distribution
::math::statistics::estimate-laplace values
Estimate the parameters for the Laplace distribution that comes closest to the
given values. Returns an estimate of respectively the location and scale
parameters, based on maximum likelihood.
list values
- List of values, assumed to be distributed according to an exponential
distribution
::math::statistics::estimante-negative-binomial r values
Estimate the probability of success for the negative binomial distribution that
comes closest to the given values. The allowed number of failures must be given.
int r - Allowed number of failures (at least 1)
int number
- List of values, assumed to be distributed according to a negative binomial
distribution.
TO DO: more function descriptions to be added
DATA MANIPULATION
The data manipulation procedures act on lists or lists of lists:
::math::statistics::filter varname data expression
Return a list consisting of the data for which the logical expression is true (this
command works analogously to the command foreach).
string varname
- Name of the variable used in the expression
list data
- List of data
string expression
- Logical expression using the variable name
::math::statistics::map varname data expression
Return a list consisting of the data that are transformed via the expression.
string varname
- Name of the variable used in the expression
list data
- List of data
string expression
- Expression to be used to transform (map) the data
::math::statistics::samplescount varname list expression
Return a list consisting of the counts of all data in the sublists of the "list"
argument for which the expression is true.
string varname
- Name of the variable used in the expression
list data
- List of sublists, each containing the data
string expression
- Logical expression to test the data (defaults to "true").
::math::statistics::subdivide
Routine PM - not implemented yet
PLOT PROCEDURES
The following simple plotting procedures are available:
::math::statistics::plot-scale canvas xmin xmax ymin ymax
Set the scale for a plot in the given canvas. All plot routines expect this
function to be called first. There is no automatic scaling provided.
widget canvas
- Canvas widget to use
float xmin
- Minimum x value
float xmax
- Maximum x value
float ymin
- Minimum y value
float ymax
- Maximum y value
::math::statistics::plot-xydata canvas xdata ydata tag
Create a simple XY plot in the given canvas - the data are shown as a collection of
dots. The tag can be used to manipulate the appearance.
widget canvas
- Canvas widget to use
float xdata
- Series of independent data
float ydata
- Series of dependent data
string tag
- Tag to give to the plotted data (defaults to xyplot)
::math::statistics::plot-xyline canvas xdata ydata tag
Create a simple XY plot in the given canvas - the data are shown as a line through
the data points. The tag can be used to manipulate the appearance.
widget canvas
- Canvas widget to use
list xdata
- Series of independent data
list ydata
- Series of dependent data
string tag
- Tag to give to the plotted data (defaults to xyplot)
::math::statistics::plot-tdata canvas tdata tag
Create a simple XY plot in the given canvas - the data are shown as a collection of
dots. The horizontal coordinate is equal to the index. The tag can be used to
manipulate the appearance. This type of presentation is suitable for
autocorrelation functions for instance or for inspecting the time-dependent
behaviour.
widget canvas
- Canvas widget to use
list tdata
- Series of dependent data
string tag
- Tag to give to the plotted data (defaults to xyplot)
::math::statistics::plot-tline canvas tdata tag
Create a simple XY plot in the given canvas - the data are shown as a line. See
plot-tdata for an explanation.
widget canvas
- Canvas widget to use
list tdata
- Series of dependent data
string tag
- Tag to give to the plotted data (defaults to xyplot)
::math::statistics::plot-histogram canvas counts limits tag
Create a simple histogram in the given canvas
widget canvas
- Canvas widget to use
list counts
- Series of bucket counts
list limits
- Series of upper limits for the buckets
string tag
- Tag to give to the plotted data (defaults to xyplot)
THINGS TO DO
The following procedures are yet to be implemented:
• F-test-stdev
• interval-mean-stdev
• histogram-normal
• histogram-exponential
• test-histogram
• test-corr
• quantiles-*
• fourier-coeffs
• fourier-residuals
• onepar-function-fit
• onepar-function-residuals
• plot-linear-model
• subdivide
EXAMPLES
The code below is a small example of how you can examine a set of data:
# Simple example:
# - Generate data (as a cheap way of getting some)
# - Perform statistical analysis to describe the data
#
package require math::statistics
#
# Two auxiliary procs
#
proc pause {time} {
set wait 0
after [expr {$time*1000}] {set ::wait 1}
vwait wait
}
proc print-histogram {counts limits} {
foreach count $counts limit $limits {
if { $limit != {} } {
puts [format "<%12.4g\t%d" $limit $count]
set prev_limit $limit
} else {
puts [format ">%12.4g\t%d" $prev_limit $count]
}
}
}
#
# Our source of arbitrary data
#
proc generateData { data1 data2 } {
upvar 1 $data1 _data1
upvar 1 $data2 _data2
set d1 0.0
set d2 0.0
for { set i 0 } { $i < 100 } { incr i } {
set d1 [expr {10.0-2.0*cos(2.0*3.1415926*$i/24.0)+3.5*rand()}]
set d2 [expr {0.7*$d2+0.3*$d1+0.7*rand()}]
lappend _data1 $d1
lappend _data2 $d2
}
return {}
}
#
# The analysis session
#
package require Tk
console show
canvas .plot1
canvas .plot2
pack .plot1 .plot2 -fill both -side top
generateData data1 data2
puts "Basic statistics:"
set b1 [::math::statistics::basic-stats $data1]
set b2 [::math::statistics::basic-stats $data2]
foreach label {mean min max number stdev var} v1 $b1 v2 $b2 {
puts "$label\t$v1\t$v2"
}
puts "Plot the data as function of \"time\" and against each other"
::math::statistics::plot-scale .plot1 0 100 0 20
::math::statistics::plot-scale .plot2 0 20 0 20
::math::statistics::plot-tline .plot1 $data1
::math::statistics::plot-tline .plot1 $data2
::math::statistics::plot-xydata .plot2 $data1 $data2
puts "Correlation coefficient:"
puts [::math::statistics::corr $data1 $data2]
pause 2
puts "Plot histograms"
.plot2 delete all
::math::statistics::plot-scale .plot2 0 20 0 100
set limits [::math::statistics::minmax-histogram-limits 7 16]
set histogram_data [::math::statistics::histogram $limits $data1]
::math::statistics::plot-histogram .plot2 $histogram_data $limits
puts "First series:"
print-histogram $histogram_data $limits
pause 2
set limits [::math::statistics::minmax-histogram-limits 0 15 10]
set histogram_data [::math::statistics::histogram $limits $data2]
::math::statistics::plot-histogram .plot2 $histogram_data $limits d2
.plot2 itemconfigure d2 -fill red
puts "Second series:"
print-histogram $histogram_data $limits
puts "Autocorrelation function:"
set autoc [::math::statistics::autocorr $data1]
puts [::math::statistics::map $autoc {[format "%.2f" $x]}]
puts "Cross-correlation function:"
set crossc [::math::statistics::crosscorr $data1 $data2]
puts [::math::statistics::map $crossc {[format "%.2f" $x]}]
::math::statistics::plot-scale .plot1 0 100 -1 4
::math::statistics::plot-tline .plot1 $autoc "autoc"
::math::statistics::plot-tline .plot1 $crossc "crossc"
.plot1 itemconfigure autoc -fill green
.plot1 itemconfigure crossc -fill yellow
puts "Quantiles: 0.1, 0.2, 0.5, 0.8, 0.9"
puts "First: [::math::statistics::quantiles $data1 {0.1 0.2 0.5 0.8 0.9}]"
puts "Second: [::math::statistics::quantiles $data2 {0.1 0.2 0.5 0.8 0.9}]"
If you run this example, then the following should be clear:
• There is a strong correlation between two time series, as displayed by the raw data
and especially by the correlation functions.
• Both time series show a significant periodic component
• The histograms are not very useful in identifying the nature of the time series -
they do not show the periodic nature.
BUGS, IDEAS, FEEDBACK
This document, and the package it describes, will undoubtedly contain bugs and other
problems. Please report such in the category math :: statistics of the Tcllib Trackers
[http://core.tcl.tk/tcllib/reportlist]. Please also report any ideas for enhancements you
may have for either package and/or documentation.
When proposing code changes, please provide unified diffs, i.e the output of diff -u.
Note further that attachments are strongly preferred over inlined patches. Attachments can
be made by going to the Edit form of the ticket immediately after its creation, and then
using the left-most button in the secondary navigation bar.
KEYWORDS
data analysis, mathematics, statistics
CATEGORY
Mathematics