Statistics and Econometrics |
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TLNise: Two-Level Normal independent sampling estimation. Rcwish generates random draws from the Wishart and related distributions.
Codes in Fortran 90 and 77. Accompanies the book "GSLIB: Geostatistical Software Library and User's Guide" by Clayton Deutsch and André Journel.
Code to accompany paper "Rolling Your Own: Linear Model Hypothesis Testing and Power Calculations via the Singular Value Decomposition".
Program by Steve Verrill that performs a simulation to test how well corrected confidence intervals based on blocked and unblocked ANOVAs perform in a predictor sort sampling ANOVA. It also estimates coverages for confidence intervals based on an analysis of covariance, and for confidence intervals based on uncorrected blocked and unblocked ANOVAs.
Program to determine appropriate sample sizes, allocate specimens, and analyze results in the case in which a response predictor is used to sort the specimens prior to treatment.
Calculates nonparametric estimates of percentiles, associated confidence intervals, and tolerance limits of the percentiles from a data set. It can also give the order statistics needed for any sample size to create the same estimates.
Obtains a two-sided confidence interval on a coefficient of variation for data from a normal distribution.
Routines for i) the calculation of some linear algebra ii) non-linear minimization, iii) numerical integration and differentiation, iv) random number generation from a Normal distribution with mean zero and variance one, and v) the implementation of the Generalized Method of Moments statistical estimation procedure.
Program by Seock-Ho Kim for classical item analysis for tests that consist of multiple-choice or true-false items. In addition to item statistics for each item response, the program provides summary statistics of the total score, coefficient alpha, and test scoring results. The cross classification of quintile group by item response can be obtained optionally. The program can be obtained from the author.
Solves the Orthogonal Distance Regresson (ODR) problem, that is, to find parameter estimates that minimize the sum of the squares of the weighted orthogonal distances between each observed data point and the curve described by a nonlinear equation.
Computers /
Algorithms /
Pseudorandom_Numbers
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