The Log Normal Probability Density Function

**LOG_NORMAL**
is a Python library which
can evaluate quantities associated with the log normal Probability
Density Function (PDF).

If X is a variable drawn from the log normal distribution, then correspondingly, the logarithm of X will have the normal distribution.

The computer code and data files described and made available on this web page are distributed under the GNU LGPL license.

**LOG_NORMAL** is available in
a C version and
a C++ version and
a FORTRAN90 version and
a MATLAB version and
a Python version.

NORMAL, a Python library which samples the normal distribution.

PDFLIB, a Python library which evaluates Probability Density Functions (PDF's) and produces random samples from them, including beta, binomial, chi, exponential, gamma, inverse chi, inverse gamma, multinomial, normal, scaled inverse chi, and uniform.

PROB, a Python library which evaluates, samples, inverts, and characterizes a number of Probability Density Functions (PDF's) and Cumulative Density Functions (CDF's), including anglit, arcsin, benford, birthday, bernoulli, beta_binomial, beta, binomial, bradford, burr, cardiod, cauchy, chi, chi squared, circular, cosine, deranged, dipole, dirichlet mixture, discrete, empirical, english sentence and word length, error, exponential, extreme values, f, fisk, folded normal, frechet, gamma, generalized logistic, geometric, gompertz, gumbel, half normal, hypergeometric, inverse gaussian, laplace, levy, logistic, log normal, log series, log uniform, lorentz, maxwell, multinomial, nakagami, negative binomial, normal, pareto, planck, poisson, power, quasigeometric, rayleigh, reciprocal, runs, sech, semicircular, student t, triangle, uniform, von mises, weibull, zipf.

TRUNCATED_NORMAL, a Python library which works with the truncated normal distribution over [A,B], or [A,+oo) or (-oo,B], returning the probability density function (PDF), the cumulative density function (CDF), the inverse CDF, the mean, the variance, and sample values.

UNIFORM, a Python library which samples the uniform distribution.

- log_normal.py the Lognormal Distribution.
- normal_01.py, the Normal distribution with mean 0 and variance 1.
- normal_01_cdf_values.py, returns some values of the Normal CDF with mean 0 and variance 1.
- normal.py, the Normal distribution.
- r8_uniform_01.py, returns a unit pseudorandom R8.
- r8poly_print.py, prints a polynomial;
- r8poly_value_horner.py, evaluates a polynomial using Horner's method;
- r8vec_max.py, returns the maximum entry in an R8VEC.
- r8vec_mean.py, returns the mean of an R8VEC.
- r8vec_min.py, returns the minimum entry in an R8VEC.
- r8vec_print.py, prints an R8VEC.
- r8vec_uniform_ab.py, returns an R8VEC whose entries are uniformly random between A and B.
- r8vec_variance.py, returns the variance of an R8VEC.
- timestamp.py returns the current YMDHMS date as a timestamp.

- log_normal_test.py, calls all the tests;
- log_normal_test.sh, runs all the tests;
- log_normal_test.txt, the output file.

You can go up one level to the Python source codes.