Difference between revisions of "Example Models and Libraries - Table"

Latest revision as of 16:43, 18 January 2023

This page lists example models and libraries. You can download them from here or (in some cases) link to a page with more details. Feel free to include and upload your own models and libraries.

Model/Library	Download	Domain	Methods	Description	For more
Model	Marginal abatement home heating.ana	carbon price, energy efficiency, climate policy	graph methods, optimal allocation, budget constraint	Shows how to set up a Marginal Abatement graph in Analytica.	Marginal Abatement Graph
Model	Solar Panel Analysis.ana	renewable energy, photovoltaics, tax credits	net present value, internal rate of return, agile modeling	Explores whether it would it be cost effective to install solar panels on the roof of a house in San Jose, California.	Solar Panel Analysis
Model	Items within budget.ana			Given a set of items, with a priority and a cost for each, selects out the highest priority items that fit within the fixed budget.	Items within Budget function
Model	Grant exclusion.ana	business analysis		Tests a hypothesis about the distribution of an attribute of the marginal rejectee of a grant program, given the relevance of that attribute to award of the grant.	Grant Exclusion Model
Model	Project Priorities 5 0.ana	business models	cost analysis, net present value (NPV), uncertainty analysis	Evaluates a set of R&D projects (including uncertain R&D costs/revenues), uses multiattribute analysis to compare projects & generates the best portfolio given a R&D budget.	Project Planner
Model	Steel and aluminum tariff model.ana			Estimate of the net impact of the 2018 import tariffs on steel and aluminum on the US trade deficit.	Steel and Aluminum import tariff impact on US trade deficit
Model	Tax bracket interpolation 2021.ana			Computes amount of tax due from taxable income for a 2017 US Federal tax return.	Tax bracket interpolation
Model	Feasible Sampler.ana	feasibility		Implements a button that will sample a collection of chance variables, then reset the sample size and keep only those sample points that are "feasible".
Model	Cross-validation example.ana		cross-validation, overfitting, non-linear kernel functions	Fits a non-linear kernel function to the residual error, and uses cross-validation to determine how many kernel functions should be used.	Cross-Validation / Fitting Kernel Functions to Data
Model	Bootstrapping.ana		bootstrapping, sampling error, re-sampling	Bootstrapping; estimates sampling error by resampling the original data.	Statistical Bootstrapping
Model	Kernel Density Estimation.ana		kernel density estimation, kernel density smoothing	Demonstrates a very simple fixed-width kernel density estimator to estimate a "smooth" probability density.	Smooth PDF plots using Kernel Density Estimation
Model	Output and input columns.ana		data analysis	Presents an input table to a user, where one column is populated with computed output data, the other column with checkboxes for the user to select.	Output and Input Columns in Same Table
Model	Platform2018b.ana	offshore platforms, oil and gas, stakeholders, rigs to reefs, decision support	decision analysis, multi-attribute, sensitivity analysis	Determined how to decommission California's 27 offshore oil platforms.	From Controversy to Consensus: California's offshore oil platforms
Model	Comparing retirement account types.ana or Free 101 Compatible Version	401(k), IRA, retirement account, decision analysis, uncertainty	MultiTables, sensitivity analysis	Explores tradeoffs between different retirement account types.	Retirement plan type comparison
Model	Plane catching with UI 2020.ANA		decision theory, decision analysis, uncertainty, Monte Carlo simulation, value of information, EVPI, EVIU	Determines when you should leave home to catch an early morning plane departure.	Plane Catching Decision with Expected Value of Including Uncertainty
Model	Marginal Analysis for Control of SO2 Emissions.ana	environmental engineering	cost-benefit analysis, marginal analysis	Marginal analysis a.k.a. benefit/cost analysis to determine the policy alternative that leads us to the most economically efficient level of cleanup of acid rain from coal-burning electric-generating plants.	Marginal Analysis for Control of SO2 emissions
Model	Donor-Presenter Dashboard.ana		dynamic models, Markov processes	Implements a continuous-time Markov chain in Analytica's discrete-time dynamic simulation environment. It supports immigration to, and emigration from, every node.	Donor/Presenter Dashboard
Model	Photosynthesis Regulation.ana - main regulation pathways Photosystem.ana - rough sketch of genetic regulation	photosynthesis	dynamic models	A model of how photosynthesis is regulated inside a cyanobacteria. Simulates the concentration levels of key transport molecules along the chain.	Regulation of Photosynthesis
Model	Time-series-reindexing.ana		dynamic models, forecasting, time-series re-indexing	Examples of time-series re-indexing.	Time-series re-indexing
Model	Post Compression Model			Calculator for computing the maximum load that can be handled by a Douglas Fir - Larch post of a given size, grade, and composition in a construction setting.	Timber Post Compression Load Capacity
Model	Compression Post Load Capacity.ana		compression analysis	Computes the load that a Douglas-Fir Larch post can support in compression. Works for different timber types and grades and post sizes.	Compression Post Load Calculator
Model	Daylighting analyzer.ana	engineering	cost-benefits analysis	How to analyze lifecycle costs and savings from daylighting options in building design.	Daylighting Options in Building Design
Model	California Power Plants.ana	power plants	edit table, choice menu, pulldown menu, checkbox	Example showing how to use Choice menus and Checkbox inside an Edit table.	California Power Plants
Model	Requires Analytica Optimizer Electrical Transmission.ana	electrical engineering, power generation and transmission		Electrical network model that minimizes total cost of generation and transmission.	Electrical Generation and Transmission
Model	Time of use pricing.ana & MECOLS0620.xlsx (both files needed)	reading from spreadsheets, time-of-use pricing, electricity pricing		Time-of-use pricing is a rate tariff model used by utility companies that changes more during times when demand tends to exceed supply.	Time of Use pricing
Model	Color map.ana		computed cell formatting	A model which highlights Cell Formatting and Computed Cell Formats.	Color Map
Model	World cup.ana			Demonstrates the Poisson distribution to determine why France beat Croatia in 2018.	2018 World Cup Soccer final
Model	resnet18.zip	residual network, deep residual learning, image recognition		Show it an image, and it tries to recognize what it is an image of, classifying it among 1000 possible categories. It uses an 18-layer residual network.	Image recognition
Model	Month to quarter.ana		aggregation, level of detail, days, weeks, months, quarters, years	Shows how to transform an array from a finer-grain index (e.g., Month) onto a coarser index (e.g., Quarter). We generally refer to this as aggregation.	Transforming Dimensions by transform matrix, month to quarter
Model	Convolution.ana		signal analysis, systems analysis	Contains several examples of convolved functions.	Convolution
Model	Dependency Tracker.ana		dependency analysis	This module tracks dependencies through your model, updating the visual appearance of nodes so that you can quickly visualize the paths by which one variable influences another.	Dependency Tracker Module
Model	French-English.ana	multi-lingual models		Maintains a single influence diagram with Title and Description attributes in both English and French.	Multi-lingual Influence Diagram
Model	Parsing XML example.ana	data extraction, xml, DOM parsing		Demonstrates two methods for extracting data: Using a full XML DOM parser, or using regular expressions.	Extracting Data from an XML file
Model	Vector Math.ana		geospatial analysis, GIS, vector analysis	Functions used for computing geospatial coordinates and distances.	Vector Math
Model	Total Allowable w Optimizer.ana or Total Allowable w StepInterp.ana for those without Optimizer		population analysis, dynamic models, optimization analysis	Determines how many fish or animals can be caught (landed) annually so that the probability of the population declining X% in Y years (decline threshold) is less than Z% (risk tolerance).	Total Allowable Harvest
Model	Cereal Formulation1.ana	product formulation, cereal formulation		Cereal formulation/discrete mixed integer model that chooses product formulations to minimize total ingredient costs.	Linearizing a discrete NSP
Model	Neural Network.ana	feed-forward neural networks, non-linear regression	optimization analysis	Models set up to train a 2-layer feedforward sigmoid network to "learn" the concept represented by the data set(s), and then test how well it does across examples not appearing in the training set.	Neural Network
Model	Earthquake expenses.ana		risk analysis, cost analysis	An example of risk analysis with time-dependence and costs shifted over time.	Earthquake Expenses
Model	Best used with Analytica Optimizer Loan policy selection.ana	creditworthiness, credit rating, default risk	risk analysis	Helps a lender decide optimal credit rating threshold to require and what interest rate (above prime) to charge.	Loan Policy Selection
Model	Hubbard_and_Seiersen_cyberrisk.ana	cybersecurity risk	loss exceedance curve, simulation	Simulates loss exceedance curves for a set of cybersecurity events, the likelihood and probabilistic monetary impact of which have been characterized by system experts.	Inherent and Residual Risk Simulation
Model	Media:Red_State_Blue_State_plot.ana	map, states	graphing	Example containing the shape outlines for each of the 50 US states, along with a graph that uses color to depict something that varies by state.	Red or blue state
Model	COVID Model 2020--03-25.ana	covid, covid-19, coronavirus, corona, epidemic		A systems dynamics style SICR model of the COVID-19 outbreak within the state of Colorado.	COVID-19 State Simulator, a Systems Dynamics approach
Model	Corona Markov.ana	covid, covid-19, coronavirus, corona, epidemic, sensitivity analyses		Explores the progression of the COVID-19 coronavirus epidemic in the US, and to explore the effects of different levels of social isolation.	How social isolation impacts COVID-19 spread in the US - A Markov model approach
Model	Modelo Epidemiológoco para el Covid-19 con cuarentena.ana	covid, covid-19, coronavirus, corona, epidemic		Un modelo en cadena de Markov del impacto previsto de la enfermedad coronavirus COVID-19 en el Perú, y del impacto del aislamiento social.	Epidemiological model of COVID-19 for Perú, en español
Model	COVID-19 Triangle Suppression.ana	covid, covid-19, coronavirus, corona, epidemic		Models progression of the COVID-19 pandemic in the US, understanding the amount of time that is required for lock down measures when a suppression strategy is adopted.	A Triangle Suppression model of COVID-19
Model	Simple COVID-19.ana	covid, covid-19, coronavirus, corona, epidemic		Explores possible COVID-19 Coronavirus scenarios from the beginning of March, 2020 through the end of 2020 in the US.	COVID-19 Coronavirus SICR progression for 2020
Model	US COVID-19 Data.ana	covid, covid-19, coronavirus, corona, epidemic, death, infection		Reads in COVID-19 data from the New York Times and transforms it into a form that is convenient to work with in Analytica.	COVID-19 Case and Death data for US states and counties
Model	Voluntary vs mandatory testing.ana	covid, covid-19, coronavirus, corona, epidemic		Computes the rate of infection in scenarios when COVID-19 testing is required/optional,based on prevalence rates, test accuracies and voluntary testing rates.	Mandatory vs Voluntary testing policies
Model	Included with software			Takes typical bond purchase inputs (purchase price, par value, interest rate, and life to maturity) and calculates bond cash flows, current yield, and yield to maturity.	Bond Model
Model	Included with software			Example of a breakeven analysis of a set of revenue levels, when the fixed expenses are set at one amount and the variable expenses are a constant fraction of revenue.	Breakeven Analysis
Model	Included with software			Evaluates and compares the expected commercialization value of multiple proposed R&D projects.	Expected R&D Project Value
Model	Included with software				Financial Statement Templates
Model	Included with software			Explores a market for a new product, and the pricing and advertising budget decisions involved.	Market Model
Model	Included with software			Takes input data for activity paths required to complete a project, and calculates various outputs describing the critical path, timing, and costs for project completion.	Plan_Schedule_ Control
Model	Included with software			Evaluates and prioritizes a portfolio of projects based on either the estimated net present value or a multi-attribute score, based on strategy fit, staff development, the generation of public goodwill, and estimated net revenue.	Project Portfolio Planner
Model	Included with software				Sales Effectiveness
Model	Included with software				Subscriber Pricing
Model	Included with software			Demonstrates the use of a waterfall chart to visualize the components of an earnings stream from an asset.	Waterfall chart
Model	Included with software			Demonstrates how a distribution for an estimated statistic can be obtained by resampling repeatedly.	Bootstrapping
Model	Included with software			Example of scatter plots in Analytica.	Kmeans Clustering
Model	Included with software			Demonstrates the use of Logistic and Probit regression to fit a generalized linear model to breast cancer data.	Logistic regression prior selection
Model	Included with software				Moving Average Example
Model	Included with software				Multidimensional Scaling
Model	Included with software				Principle Components
Model	Included with software				Regression Examples
Model	Included with software			Example of how to decide where to throw a party. Shows how to model a two-branch decision tree in Analytica.	Two Branch Party Tree
Model	Included with software			Uses the beta distribution for the Bayesian update of beliefs about the probability that a coin will come up heads.	Beta Updating
Model	Included with software			Multi-project R&D evaluation models a typical R&D decision problem that might be faced by a biogenetic company.	Biotech R&D Portfolio
Model	Included with software				Diversification Illustration
Model	Included with software				Expected Value of Sampling Information (EVSI)
Model	Included with software				Gibbs Sampling in Bayesian Network
Model	Included with software			Models R&D decision analysis for investment strategy among several choices of powerplants for a low emissions vehicle (LEV).	LEV R&D Strategy
Model	Included with software			Demonstrates a marginal benefit/cost analysis to determine the policy alternative that leads to the most economically efficient level of cleanup.	Marginal Analysis for Control of SO2 Emissions
Model	Included with software			Example of a multi-attribute utility analysis for cars, showing how to analyze an array of cars across an array of attributes, where different drivers assign differing weights to the importance of each attribute.	Multi-attribute Utility Analysis
Model	Included with software				Newton-Raphson Method
Model	Included with software			Uses decision tree terminology to provide an example asymmetric decision tree in Analytica.	Nonsymmetric Tree
Model	Included with software				Party With Forecast
Model	Included with software				Plane catching decision with EVIU
Model	Included with software				Probability of Gaussian Region (Importance Sampling)
Model	Included with software			Calculates the required supply level to maximize profit when the profit function is asymmetric around the average demand value.	Supply and Demand
Model	Included with software				Tornado Diagrams
Model	Included with software			Represents a decision often faced in today’s world: which technology to purchase now, in the face of uncertain future products and prices.	Upgrade Decision
Model	Included with software			Forecasts the population of fish and the establishment of a contagious viral disease within the population over time.	Disease establishment
Model	Included with software			Levels staff efforts over time according to staff available, computing both the work done over time and idle time.	Leveling
Model	Included with software			Demonstrates how to simulate a Markov process using dynamic time. The example estimates the number of hospital patients over time, modeled as a Markov process.	Markov Chain
Model	Included with software				Mass-Spring-Damper
Model	Included with software			Models a mean-reverting price process, along with a trading strategy to "beat the market". It demonstrates the encoding of a Markov Decision Process.	Mean-reversion trading
Model	Included with software				Minimal edit distance
Model	Included with software				Optimal Path Dynamic Programming
Model	Included with software				Parking Space Selection
Model	Included with software				Projectile Motion
Model	Included with software				Tunnel through earth
Model	Included with software			Example of a dynamic variable that calculates growth over time, where Time is defined with unequal time steps. It is an example of exponential or linear growth or decay.	Unequal time steps
Model	Included with software			Curve fits noisy time-sequence data using an adaptive filter.	Adaptive Filter
Model	Included with software			Calculates the expected gain of an antenna looking at two different satellites.	Antenna Gain
Model	Included with software			Computes the load that a Douglas-Fir Large compression post can support.	Compression Post Load Capacity
Model	Included with software			Demonstration showing how to analyze life cycle costs and savings from daylighting options in building design.	Daylight Analyzer
Model	Included with software				Failure Analysis
Model	Included with software				Ideal Gas Law
Model	Included with software				Power dispatch
Model	Included with software				Regular polygon calculator
Model	Included with software				Find Words Game
Model	Included with software				Fractals everywhere
Model	Included with software				Probability assessment
Model	Included with software				Abstracted Subset
Model	Included with software				Assignment from Button
Model	Included with software			Calculates the auto-correlation coefficients of noisy time sequence data.	Autocorrelation
Model	Included with software				Choice and Determtables
Model	Included with software				Correlated Distributions
Model	Included with software				Correlated Normals
Model	Included with software				DBWrite Example
Model	Included with software				Discrete Sampling
Model	Included with software			Demonstrates how to extract a diagonal from a matrix.	Extracting Diagonal
Model	Included with software				Lookup Reindexing
Model	Included with software				Map images from internet
Model	Included with software				Sample Size Input Node
Model	Included with software				Sorting People by Height
Model	Included with software			Creates a subset array out of a larger array based on a decision criterion.	Subset of Array
Model	Included with software			Swaps a computed or one-dimensional table value with its index, thereby making the computed value an index.	Swapping y and x-index
Model	Included with software				Use of MDTable
Model	Included with software				Airline NLP
Model	Included with software				Asset allocation
Model	Included with software				Automobile Production
Model	Included with software				Beer Distribution LP
Model	Included with software				Big Mac Attack
Model	Included with software			Example of capital budgeting for four possible projects, where the objective is to decide which projects to choose in order to maximize the total return.	Capital Investment
Model	Included with software			Diagnosing an infeasible set of linear constraints.	Infeasible
Model	Included with software			Linear program to determine which product variants to produce and how labor should be allocated among the various production steps based on the workers’ skill sets.	Labor production allocation
Model	Included with software			Demonstration of a grouped-integer domain. Fill in a square with the digits 1 through n2 such that the rows and columns all sum to the same value.	Magic Square
Model	Included with software				NLP with Jacobian
Model	Included with software				Optimal can dimensions
Model	Included with software				Optimal Production Allocation
Model	Included with software			Allocate planes to origin/destination legs to maximize gross margin.	Plane allocation LP
Model	Included with software			1-D example illustrating local the problem of local optima.	Polynomial NLP
Model	Included with software				Problems with Local Optima
Model	Included with software				Production Planning LP
Model	Included with software			Example of a quadratically constrained optimization problem with a quadratic objective function.	Quadratic Constraints
Model	Included with software			Demonstrates how an nonlinear programming formulation can be used to solve a non- linear equation.	Solve using NLP
Model	Included with software			Find a solution to a Sudoku puzzle.	Sudoku with Optimizer
Model	Included with software			Find a minimal length tour through a set of cites.	Traveling salesman
Model	Included with software				Two Mines Model
Model	Included with software				Vacation plan with PWL tax
Model	Included with software			Projects cost assessments of damage resulting from large earthquakes over time. It demonstrates risk analysis with time-dependence and costs of events shifted over time.	Earthquake expense risk
Model	Included with software			Compares the value of various policies for restraints on occupants of automobiles.	Seat belt safety
Model	Included with software			Demonstrates risk/benefit analysis, in this case regarding the benefits of reducing the emissions of fictitious air pollutant TXC.	Txc
Model	Included with software				Rent vs Buy Model
Model	Included with software				Rent vs Buy Analysis
Model	Included with software				Car cost
Model	Included with software				Car cost model ch 4
Model	Included with software				Car cost model ch 5
Model	Included with software				Hares sub-module - act I
Model	Included with software				Foxes and hares sub-modules - act II
Model	Included with software				Foxes and hares act III
Model	Included with software			Demonstrate the building blocks for creating and editing variable definitions — expressions, standard operators, and mathematical functions.	Expression Examples
Model	Included with software				Input and Output Nodes
Model	Included with software			Examples in this model demonstrate the basics of working with multidimensional arrays.	Array Examples
Model	Included with software			Examples in this model demonstrate many more of Analytica’s built-in array functions.	Array Function Examples
Model	Included with software				Analyzing Unc & Sens
Model	Included with software				Continuous Distributions
Model	Included with software				Discrete Distributions
Model	Included with software			Dynamic model that finds the downward velocity and position of a dropped object over a six second time period.	Dynamic & Dependencies
Model	Included with software				Dynamic & Uncertainty
Model	Included with software			Simple dynamic model, with one variable that changes over time. This example finds the gasoline price for each of five years, assuming a 5% growth rate.	Dynamic Example 1
Model	Included with software			Slight increase in complexity over Dynamic Example 1. Instead of assuming a fixed inflation rate, this example, looks at the price with three different inflation rates for comparison.	Dynamic Example 2
Model	Included with software			Illustrates a dynamic loop involving simultaneous recurrences over two distinct indexes.	Dynamic on multiple indexes
Model	Included with software				Dynamic on non-Time index
Library	Included with software			Functions that convert between binary, octal, decimal integer and hexadecimal values.	Base conversion library
Library	Included with software				Bayes Function
Library	Included with software				Complex Library
Library	Included with software				Concatenation
Library	Included with software				Data Statistics Library
Library	Included with software				Distribution Densities
Library	Included with software			Contains various functions for defining standard distributions using different sets of parameters.	Distribution Variations
Library	Included with software				Expand Index
Library	Included with software				Financial Library
Library	Included with software			Provides functions for writing data to and from flat files, particularly between two- dimensional tables and comma-separated value (CSV) files.	Flat File Library
Library	Included with software				Garbage Bin Library
Library	Included with software				Generalized Regression
Library	Included with software				Linked List Library
Library	Included with software			Contains functions for creating several multivariate distributions.	Multivariate Distributions Library
Library	Included with software				ODBC-Library
Library	Included with software				Optimization functions
Library	Included with software			See which variables and functions are taking most of the computation time when running your model.	Performance Profiler
Library	Included with software				Structured Optimization Tools

@@ Line 1: / Line 1: @@
 __NOINDEX__
+[[Category: Examples]]
 This page lists example models and libraries. You can download them from here or (in some cases) link to a page with more details. Feel free to include and upload your own models and libraries.
 <div style="text-align: center;">
-{| class="wikitable sortable" style=align="center"
+{| class="wikitable sortable"
-|-
+! scope="col" style="min-width: 100px; max-width: 120px;" | Model/Library
-! Model/Library
+! scope="col" style="min-width: 100px; max-width: 120px;" |Download
-! style="inline-size: 150px; overflow-wrap: break-word;" |  Download
+! Domain
-! style="max-width: 50px;" |  Domain
+! Methods
 ! style="min-width: 300px;" |  Description
-! style="max-width: 50px;" |  Methods
+! For more
-! style="max-width: 50px;" |  For more
 |-
 | Model
-|[[media:Marginal abatement home heating.ana|Marginal abatement home heating.ana]]
+|[[Media:Marginal abatement home heating.ana|Marginal abatement home heating.ana]]
 |carbon price, energy efficiency, climate policy
+|graph methods, optimal allocation, budget constraint
 |<div style="text-align: left;">Shows how to set up a Marginal Abatement graph in Analytica.</div>
-|graph methods, optimal allocation, budget constraint
 |[[Marginal Abatement Graph]]
 |-
 | Model
-|[[media:Solar Panel Analysis.ana|Solar Panel Analysis.ana]]
+|[[Media:Solar Panel Analysis.ana|Solar Panel Analysis.ana]]
 |renewable energy, photovoltaics, tax credits
-|<div style="text-align: left;">This model explores whether it would it be cost effective to install solar panels on the roof of a house in San Jose, California.</div>
 |net present value, internal rate of return, agile modeling
+|<div style="text-align: left;">Explores whether it would it be cost effective to install solar panels on the roof of a house in San Jose, California.</div>
 |[[Solar Panel Analysis]]
 |-
 | Model
-|[[Media:Items_within_budget.ana|Items within budget.ana]]
+|[[Media:Items within budget.ana|Items within budget.ana]]
 |
-|<div style="text-align: left;">Given a set of items, with a priority and a cost for each, the function Items_within_budget function selects out the highest priority items that fit within the fixed budget. </div>
 |
+|<div style="text-align: left;">Given a set of items, with a priority and a cost for each, selects out the highest priority items that fit within the fixed budget. </div>
 |[[Items within Budget function]]
 |-
 | Model
-|[[Media:Grant_exclusion.ANA|Grant exclusion.ana]]
+|[[Media:Grant exclusion.ANA|Grant exclusion.ana]]
 |business analysis
-|<div style="text-align: left;">This model tests a hypothesis about the distribution of an attribute of the marginal rejectee of a grant program, given the relevance of that attribute to award of the grant.</div>
 |
+|<div style="text-align: left;">Tests a hypothesis about the distribution of an attribute of the marginal rejectee of a grant program, given the relevance of that attribute to award of the grant.</div>
 |[[Grant Exclusion Model]]
 |-
@@ Line 49: / Line 49: @@
 |[[Media:Project Priorities 5 0.ana|Project Priorities 5 0.ana]]
 |business models
-|<div style="text-align: left;">Evaluates a set of R&D projects, including uncertain R&D costs/revenues, uses multiattribute analysis to compare projects & generates the best portfolio given a R&D budget.</div>
 |cost analysis, net present value (NPV), uncertainty analysis
+|<div style="text-align: left;">Evaluates a set of R&D projects (including uncertain R&D costs/revenues), uses multiattribute analysis to compare projects & generates the best portfolio given a R&D budget.</div>
 |[[Project Planner]]
 |-
@@ Line 56: / Line 56: @@
 | Model
 |[[Media:Steel and aluminum tariff model.ana|Steel and aluminum tariff model.ana]]
+|
 |
 | <div style="text-align: left;">Estimate of the net impact of the 2018 import tariffs on steel and aluminum on the US trade deficit.</div>
-|
 |[[Steel and Aluminum import tariff impact on US trade deficit]]
 |-
 | Model
-|[[Media: Tax bracket interpolation 2021.ana|Tax bracket interpolation 2021.ana]]
+|[[Media:Tax bracket interpolation 2021.ana|Tax bracket interpolation 2021.ana]]
 |
-|<div style="text-align: left;">Computes amount of tax due from taxable income for a 2017 US Federal tax return. To match the IRS's numbers exactly, it is necessary to process tax brackets correctly as well as implementation a complex mix of rounding rules that reproduce the 12 pages of table lookups from the Form 1040 instructions. This model is showcased in a blog article, [http://Lumina.com/blog/how-to-simplify-the-irs-tax-tables How to simplify the IRS Tax Tables].</div>
 |
+|<div style="text-align: left;">Computes amount of tax due from taxable income for a 2017 US Federal tax return.</div>
 |[[Tax bracket interpolation]]
 |-
 | Model
-|[[Media:Feasible_Sampler.ana|Feasible Sampler.ana]]
+|[[Media:Feasible Sampler.ana|Feasible Sampler.ana]]
 |feasibility
-|<div style="text-align: left;">You have a bunch of chance variables, each with a probability distribution. Their joint sample, however, contains some combinations of points that are (for one reason or another) physically impossible. We'll call those infeasible points. You'd like to eliminate those points from the sample and keep only the feasible points. <br /><br />
+|
-This module implements a button that will sample a collection of chance variables, then reset the sample size and keep only those sample points that are "feasible". <br /><br />
+|<div style="text-align: left;">Implements a button that will sample a collection of chance variables, then reset the sample size and keep only those sample points that are "feasible".</div>
-Obviously, this approach will work best when most of your samples are feasible. If you can handle the "infeasible" points in your model directly, by conditioning certain chance variables on others, that is far preferable. But there are some cases where this solution (although a bit of a kludge) is more convenient. <br /><br />
-The instructions for how to use this are in the module description field.</div>
-|statistics, sampling, importance sampling, Monte Carlo simulation
-|[[Sampling from only feasible points]]
 |-
 | Model
-|[[media:Cross-validation example.ana|Cross-validation example.ana]]
+|[[Media:Cross-validation example.ana|Cross-validation example.ana]]
 |
-|<div style="text-align: left;">When fitting a function to data, if you have too many free parameters relative to the number of points in your data set, you may "overfit" the data.  When this happens, the fit to your training data may be very good, but the fit to new data points (beyond those used for training) may be very poor.<br /><br />
-Cross-validation is a common technique to deal with this problem: We set aside a fraction of the available data as a cross-validation set.  Then we begin by fitting very simple functions to the data (with few free parameters), successively increasing the number of free parameters, and seeing how the predictive performance changes on the cross-validation set.  It is typical to see improvement on the cross-validation set for a while, followed by a deterioration of predictive performance on the cross-validation set once overfitting starts occurring.  <br /><br />
-This example model successively fits a non-linear kernel function to the residual error, and uses cross-validation to determine how many kernel functions should be used.<br /><br />
-Requires Analytica Optimizer: The kernel fitting function (Kern_Fit) uses [[NlpDefine]].</div>
 |cross-validation, overfitting, non-linear kernel functions
+|<div style="text-align: left;">Fits a non-linear kernel function to the residual error, and uses cross-validation to determine how many kernel functions should be used.</div>
 |[[Cross-Validation / Fitting Kernel Functions to Data]]
 |-
 | Model
-|[[media:Bootstrapping.ana|Bootstrapping.ana]]
+|[[Media:Bootstrapping.ana|Bootstrapping.ana]]
 |
-|<div style="text-align: left;">Bootstrapping is a technique from statistics for estimating the sampling error present in a statistical estimator.  The simplest version estimates sampling error by resampling the original data.  This model demonstrates how to do this in Analytica.</div>
 |bootstrapping, sampling error, re-sampling
+|<div style="text-align: left;">Bootstrapping; estimates sampling error by resampling the original data.</div>
 |[[Statistical Bootstrapping]]
 |-
 | Model
-|[[media:Kernel_Density_Estimation.ana|Kernel Density Estimation.ana]]
+|[[Media:Kernel Density Estimation.ana|Kernel Density Estimation.ana]]
 |
-|<div style="text-align: left;">This example demonstrates a very simple fixed-width kernel density estimator to estimate a "smooth" probability density.   The built-in PDF function in Analytica often has a choppy appearance due to the nature of histogramming -- it sets up a set of bins and counts how many points land in each bin.  A kernel density estimator smooths this out, producing a less choppy PDF plot.<br /><br />
-This smoothing is built into [[Analytica 4.4]].  You can select [[Kernel Density Smoothing|smoothing]] from the [[Uncertainty Setup dialog]].</div>
 |kernel density estimation, kernel density smoothing
+|<div style="text-align: left;">Demonstrates a very simple fixed-width kernel density estimator to estimate a "smooth" probability density.</div>
 |[[Smooth PDF plots using Kernel Density Estimation]]
 |-
 | Model
-|[[media:Output and input columns.ana|Output and input columns.ana]]
+|[[Media:Output and input columns.ana|Output and input columns.ana]]
 |
-|<div style="text-align: left;">Presents an input table to a user, where one column is populated with computed output data, the other column with checkboxes for the user to select.  Although the '''Output Data''' column isn't read only, as would be desired, a [[Check Attribute]] has been configured to complain if he does try to change values in that column.  The model that uses these inputs would ignore any changes he makes to data in the '''Output Data''' column.<br /><br />
-Populating the '''Output Data''' column requires the user to press a button, which runs a button script to populate that column.  This button is presented on the top-level panel.  If you change the input value, the output data will change, and then the button needs to be pressed to refresh the output data column.</div>
 |data analysis
+|<div style="text-align: left;">Presents an input table to a user, where one column is populated with computed output data, the other column with checkboxes for the user to select.</div>
 |[[Output and Input Columns in Same Table]]
 |-
 | Model
-|[[media:Platform 2018b.ana|Platform2018b.ana]]
+|[[Media:Platform 2018b.ana|Platform2018b.ana]]
 |offshore platforms, oil and gas, stakeholders, rigs to reefs, decision support
-|<div style="text-align: left;">Too many environmental issues cause bitter public controversy. The question of how to decommission California's 27 offshore oil platforms started out as a typical example. But remarkably, after careful analysis a single option, "[http://lumina.com/case-studies/energy-and-power/a-win-win-solution-for-californias-offshore-oil-rigs rigs to reefs]", obtained the support of almost all stakeholders, including oil companies and environmentalists. A law to enable this option was passed by the California State house almost unanimously, and signed by Governor Arnold Schwarzenegger.</div>
 |decision analysis, multi-attribute, sensitivity analysis
+|<div style="text-align: left;">Determined how to decommission California's 27 offshore oil platforms.</div>
 |[[From Controversy to Consensus: California's offshore oil platforms]]
 |-
 | Model
-|[[media:Comparing retirement account types.ana|Comparing retirement account types.ana]] or [[media:Comparing retirement account types without sensitivity.ana|Free 101 Compatible Version]]
+|[[Media:Comparing retirement account types.ana|Comparing retirement account types.ana]] or [[Media:Comparing retirement account types without sensitivity.ana|Free 101 Compatible Version]]
 |401(k), IRA, retirement account, decision analysis, uncertainty
-|<div style="text-align: left;">Will you end up with a bigger nest egg at retirement with a 401(k), traditional IRA, Roth IRA or a normal non-tax-advantaged brokerage account? For example, comparing a Roth IRA to a normal brokerage, intermediate capital gains compound in the Roth, but eventually you pay taxes on those gains at your income tax rate at retirement, whereas in the brokerage you pay capital gains taxes on the gains, which is likely a lower tax rate. So does the compounding outweigh the tax rate difference? What effect do the higher account maintenance fees in a 401(k) account have? How sensitive are these conclusions to the various input estimates? The answers to all these questions depend on your own situation, and may different for someone else. Explore these questions with this model.</div>
 |[[MultiTable]]s, sensitivity analysis
+|<div style="text-align: left;">Explores tradeoffs between different retirement account types.</div>
 |[[Retirement plan type comparison]]
 |-
 | Model
-|[[media:Plane catching with UI 2020.ANA|Plane catching with UI 2020.ANA]]
+|[[Media:Plane catching with UI 2020.ANA|Plane catching with UI 2020.ANA]]
 |
-|<div style="text-align: left;">A simple decision analysis model of a familiar decision: What time I should leave my home to catch an early morning plane departure?  I am uncertain about the time to drive to the airport, walk from parking to gate (including security), and time needed at the departure gate. It also illustrates the Expected Value of Including Uncertainty (EVIU) -- the value of considering uncertainty explicitly in your decision making compared to ignoring it and assuming that all uncertain quantities are fixed at the median estimate.<br /><br />
-Details at [[Catching a plane example and EVIU]]. Includes downloadable model, slides, and video.</div>
 |decision theory, decision analysis, uncertainty, Monte Carlo simulation, value of information, EVPI, EVIU
+|<div style="text-align: left;">Determines when you should leave home to catch an early morning plane departure.</div>
 |[[Plane Catching Decision with Expected Value of Including Uncertainty]]
 |-
 | Model
-|[[media:Marginal Analysis for Control of SO2 Emissions.ana|Marginal Analysis for Control of SO2 Emissions.ana]]
+|[[Media:Marginal Analysis for Control of SO2 Emissions.ana|Marginal Analysis for Control of SO2 Emissions.ana]]
 |environmental engineering
-|<div style="text-align: left;">Acid rain in eastern US and Canada caused by sulfur dioxide is emitted primarily by coal-burning electric-generating plants in the Midwestern U.S.  This model demonstrates a marginal analysis a.k.a. benefit/cost analysis to determine the policy alternative that leads us to the most economically efficient level of cleanup.</div>
 |cost-benefit analysis, marginal analysis
+|<div style="text-align: left;">Marginal analysis a.k.a. benefit/cost analysis to determine the policy alternative that leads us to the most economically efficient level of cleanup of acid rain from coal-burning electric-generating plants.</div>
 |[[Marginal Analysis for Control of SO2 emissions]]
 |-
 | Model
-|[[Media:Donor_Presenter_Dashboard_II.ANA|Donor-Presenter Dashboard.ana]]
+|[[Media:Donor Presenter Dashboard II.ANA|Donor-Presenter Dashboard.ana]]
 |
-|<div style="text-align: left;">This model implements a continuous-time Markov chain in Analytica's discrete-time dynamic simulation environment.  It supports immigration to, and emigration from, every node.<br /><br />
-It can be used by an arts organization to probabilistically forecast future audience evolution, in both the short and the long (steady state) term.  It also allows for uncertainty in the input parameters.</div>
 |dynamic models, Markov processes
+|<div style="text-align: left;">Implements a continuous-time Markov chain in Analytica's discrete-time dynamic simulation environment.  It supports immigration to, and emigration from, every node.</div>
 |[[Donor/Presenter Dashboard]]
 |-
 | Model
-|[[media:Photosynthesis regulation.ANA|Photosynthesis Regulation.ana]] - main regulation pathways<br />[[media:Photosystem.ana | Photosystem.ana]] - rough sketch of genetic regulation
+|[[Media:Photosynthesis regulation.ANA|Photosynthesis Regulation.ana]] - main regulation pathways<br />[[Media:Photosystem.ana| Photosystem.ana]] - rough sketch of genetic regulation
 |photosynthesis
-|<div style="text-align: left;">A model of how photosynthesis is regulated inside a cyanobacteria.  As light exposure varies over time (and you can experiment with various light intensity waveforms), it simulates the concentration levels of key transport molecules along the chain, through the PSII complex, plasto-quinone pool, PSI complex, down to metabolic oxidation.  The dynamic response to light levels, or changes in light levels, over time becomes evident, and the impact of changes to metabolic demand can also be observed.  In the graph of fluorescence above, we can see an indicator of how much energy is being absorbed, in three different cases (different light intensities).  In the two higher intensity cases, photoinhibition is observed -- a protective mechanism of the cell that engages when more energy is coming in than can be utilized by the cell.  Excess incoming energy, in the absence of photoinhibition, causes damage, particularly to the PSII complex.<br /><br />
-This model uses node shapes for a different purpose than is normally seen in decision analysis models.  In this model, ovals, instead of depicting chance variables, depict chemical reactions, where the value depicts the reaction rate, and rounded rectangles depict chemical concentrations.<br /><br />
-Two models are attached.  The first is a bit cleaner, and focused on the core transport chain, as described above.  The second is less developed, but is focused more on genetic regulation processes.</div>
 |dynamic models
+|<div style="text-align: left;">A model of how photosynthesis is regulated inside a cyanobacteria. Simulates the concentration levels of key transport molecules along the chain.</div>
 |[[Regulation of Photosynthesis]]
 |-
 | Model
-|[[media:Time-series-reindexing.ana|Time-series-reindexing.ana]]
+|[[Media:Time-series-reindexing.ana|Time-series-reindexing.ana]]
 |
-|<div style="text-align: left;">This model contains some examples of time-series re-indexing.  It is intended to demonstrate some of these basic techniques.
-In this example, actual measurements were collected at non-uniform time increments.  Before analyzing these, we map these to a uniformly spaced time index (<code>Week</code>), occurring on Monday of each week.  The mapping is done using an interpolation.  The evenly-spaced data is then used to forecast future behavior.  We first forecast over an index containing only future time points (<code>Future_weeks</code>), using a log-normal process model based on the historical weekly change.  We then combine the historical data with the forecast on a common index (<code>Week</code>).  A prob-bands graph of the weekly_data result shows the range of uncertainty projected by the process model (you'll notice the uncertainty exists only for future forecasted values, not historical ones).</div>
 |dynamic models, forecasting, time-series re-indexing
+|<div style="text-align: left;">Examples of time-series re-indexing.</div>
 |[[Time-series re-indexing]]
 |-
@@ Line 183: / Line 168: @@
 |[[Media:PostCompression.ana|Post Compression Model]]
 |
-|<div style="text-align: left;">Here is a calculator for computing the maximum load that can be handled by a Douglas Fir - Larch post of a given size, grade, and composition in a construction setting.</div>
 |
+|<div style="text-align: left;">Calculator for computing the maximum load that can be handled by a Douglas Fir - Larch post of a given size, grade, and composition in a construction setting.</div>
 |[[Timber Post Compression Load Capacity]]
 |-
 | Model
-|[[media:Compression_Post_Load_Capacity.ana|Compression Post Load Capacity.ana]]
+|[[Media:Compression Post Load Capacity.ana|Compression Post Load Capacity.ana]]
 |
+|compression analysis
 |<div style="text-align: left;">Computes the load that a Douglas-Fir Larch post can support in compression.  Works for different timber types and grades and post sizes.</div>
-|compression analysis
 |[[Compression Post Load Calculator]]
 |-
 | Model
-|[[media:Daylighting analyzer.ana|Daylighting analyzer.ana]]
+|[[Media:Daylighting analyzer.ana|Daylighting analyzer.ana]]
 |engineering
-|<div style="text-align: left;">A demonstration showing how to analyze lifecycle costs and savings from daylighting options in building design.<br /><br />
-Analysis based on Nomograph Cost/Benefit Tool for Daylighting. adapted from S.E. Selkowitz and M. Gabel. 1984. "LBL Daylighting Nomographs," LBL-13534, Lawrence Berkeley Laboratory, Berkeley CA, 94704. (510) 486-6845.</div>
 |cost-benefits analysis
+|<div style="text-align: left;">How to analyze lifecycle costs and savings from daylighting options in building design.</div>
 |[[Daylighting Options in Building Design]]
 |-
 | Model
-|[[Media:California_Power_Plants.ANA|California Power Plants.ana ]]
+|[[Media:California Power Plants.ANA|California Power Plants.ana]]
 |power plants
-|<div style="text-align: left;">An example showing how to use Choice menus and Checkbox inside an Edit table. It also shows how to use the Cell default attribute to specify default values (including Choice menu and Checkbox with default selections) specified in "Default Plant Data" to be used when user creates a new row in the Edit table.  This model shows how to demonstrates the use of [[Choice|choice pulldowns]] in edit tables.  The model is created during a mini-tutorial on [[Inserting Choice Controls in Edit Table Cells]] elsewhere on this Wiki.</div>
 |edit table, choice menu, pulldown menu, checkbox
+|<div style="text-align: left;">Example showing how to use Choice menus and Checkbox inside an Edit table.</div>
 |[[California Power Plants]]
 |-
 | Model
-|Requires Analytica Optimizer<br />[[media:Electrical Transmission.ana|Electrical Transmission.ana]]
+|Requires Analytica Optimizer<br />[[Media:Electrical Transmission.ana|Electrical Transmission.ana]]
 |electrical engineering, power generation and transmission
-|<div style="text-align: left;">This model of an electrical network minimizes total cost of generation and transmission.  Each node in the network has power generators and consumers (demand).  Nodes are connected by transmission links. Each link has a maximum capacity in Watts and an admittance (the real part of impedance is assumed to be zero).  Each generator has a min and max power and a marginal cost in $/KWh.  The model uses a linear program to determine how much power each generator should produce so as to minimize total cost of generation and transmission, while satisfying demand and remaining within link constraints.</div>
 |
+|<div style="text-align: left;">Electrical network model that minimizes total cost of generation and transmission.</div>
 |[[Electrical Generation and Transmission]]
 |-
 | Model
-|[[media:Time of use pricing.ana|Time of use pricing.ana]] & [[media:MECOLS0620.xlsx|MECOLS0620.xlsx]]<br />(both files needed)
+|[[Media:Time of use pricing.ana|Time of use pricing.ana]] & [[Media:MECOLS0620.xlsx|MECOLS0620.xlsx]]<br />(both files needed)
 |reading from spreadsheets, time-of-use pricing, electricity pricing
-|<div style="text-align: left;">Electricity demand and generation is not constant, varying by time of day and season. For example, solar panels generate only when the sun is out, and demand drops in the wee morning hours when most people are sleeping. Time-of-use pricing is a rate tariff model used by utility companies that changes more during times when demand tends to exceed supply. This model import actual usage data from a spreadsheet obtained from [https://www9.nationalgridus.com/energysupply/load_estimate.asp NationalGridUS.com] of historic average customer usage, uses that to project average future demand, and then calculates the time-of-use component of PG&E's [https://www.pge.com/tariffs/assets/pdf/tariffbook/ELEC_SCHEDS_E-TOU-C.pdf TOU-C] and [https://www.pge.com/tariffs/assets/pdf/tariffbook/ELEC_SCHEDS_E-TOU-D.pdf TOU-D] tariffs. (Note: The historical data came from Massachussets, the rate plan is from California, but these are used as examples).  Developed during a User Group Webinar on 30-Sep-2020, which you can watch as well to see it built.<br />'''Video''': [http://webinararchive.analytica.com/2020-Sep-30%20Time%20of%20use%20pricing.mp4 Time of use pricing.mp4]</div>
 |
+|<div style="text-align: left;">Time-of-use pricing is a rate tariff model used by utility companies that changes more during times when demand tends to exceed supply.</div>
 |[[Time of Use pricing]]
 |-
 | Model
-|[[media:color_map.ana|Color map.ana]]
+|[[Media:Color map.ana|Color map.ana]]
 |
-|<div style="text-align: left;">A model which highlights [[Cell Format Expression|Cell Formatting]] and [[Computed cell formats|Computed Cell Formats]]. Model result is a 'color map' wherein the cell fill color is computed based on three input variables (R, G, and B), the computed color is displayed in hexadecimal, and the font color of the hexadecimal color is determined by the cell fill color.</div>
 |computed cell formatting
+|<div style="text-align: left;">A model which highlights [[Cell Format Expression|Cell Formatting]] and [[Computed cell formats|Computed Cell Formats]].</div>
 |[[Color Map]]
 |-
@@ Line 240: / Line 224: @@
 |[[Media:World cup.ana|World cup.ana]]
 |
-|<div style="text-align: left;">On July 15, 2018, France beat Croatia 4-2 in the final game of the World Cup to become world champions. But how much of that is can be attributed to France being the better team versus to the random chance? This model accompanies my blog article, [http://lumina.com/blog/world-cup-soccer.-how-much-does-randomness-determine-the-winner World Cup Soccer. How much does randomness determine the winner?], where I explore this question and use the example to demonstrate the [[Poisson|Poisson distribution]].</div>
 |
+|<div style="text-align: left;">Demonstrates the [[Poisson|Poisson distribution]] to determine why France beat Croatia in 2018.</div>
 |[[2018 World Cup Soccer final]]
 |-
@@ Line 248: / Line 232: @@
 |[http://AnalyticaOnline.com/Lonnie/resnet18.zip resnet18.zip]
 |residual network, deep residual learning, image recognition
-|<div style="text-align: left;">Show it an image, and it tries to recognize what it is an image of, classifying it among 1000 possible categories. It uses an 18-layer residual network. This model is described and demonstrated in a video in the blog article [http://lumina.com/blog/an-analytica-model-that-recognizes-images An Analytica model that recognizes images].</div>
 |
+|<div style="text-align: left;">Show it an image, and it tries to recognize what it is an image of, classifying it among 1000 possible categories. It uses an 18-layer residual network.</div>
 |[[Image recognition]]
 |-
@@ Line 256: / Line 240: @@
 |[[Media:Month to quarter.ana|Month to quarter.ana]]
 |
-|<div style="text-align: left;">The model shows how to transform an array from a finer-grain index (e.g., Month) onto a coarser index (e.g., Quarter).  We generally refer to this as [[Aggregate|aggregation]].   The model illustrates the direct use of [[Aggregate]], as well as a method to do this used before Aggregate was added to Analytica in release 4.2.</div>
 |aggregation, level of detail, days, weeks, months, quarters, years
+|<div style="text-align: left;">Shows how to transform an array from a finer-grain index (e.g., Month) onto a coarser index (e.g., Quarter).  We generally refer to this as [[Aggregate|aggregation]].</div>
 |[[Transforming Dimensions by transform matrix, month to quarter]]
 |-
@@ Line 264: / Line 248: @@
 |[[Media:Convolution.ana|Convolution.ana]]
 |
-|<div style="text-align: left;">Convolution is used mostly for signal and systems analysis. It is a way to combine two time series.  This model contains function Convolve(Y, Z, T, I), that computes the convolution of two time series.  The model contains several examples of convolved functions.<br /><br />
-A time series is a set of points, <code>(Y, T)</code>, where <code>T</code> is the ascending X-axis, and the set of points is indexed by <code>I</code>. The values of <code>T</code> do not have to be equally spaced. The function treats <code>Y</code> and <code>Z</code> as being equal to 0 outside the range of <code>T</code>. The two time series here are the set of points <code>(Y, T)</code> and the set of points <code>(Z, T)</code>, where both sets of points are indexed by <code>I</code>.<br /><br />
-The mathematical definition of the convolution of two time series is the function given by:
-:<math>h(t) = \int y(u) z(t-u) dt</math></div>
 |signal analysis, systems analysis
+|<div style="text-align: left;">Contains several examples of convolved functions.</div>
 |[[Convolution]]
 |-
 | Model
-|[[media:Dependency_Tracker.ANA | Dependency Tracker.ana]]
+|[[Media:Dependency Tracker.ANA| Dependency Tracker.ana]]
 |
-|<div style="text-align: left;">This module tracks dependencies through your model, updating the visual appearance of nodes so that you can quickly visualize the paths by which one variable influences another.  You can also use it to provide a visual indication of which nodes are downstream (or upstream) from an indicated variable.<br /><br />
-The module contains button scripts that change the bevel appearance of nodes in your model.  To see how Variable <code>X</code> influences Variable <code>Y</code>, the script will bevel the nodes for all variables that are influenced by <code>X</code> and influence <code>Y</code>.  Alternatively, you can bevel all nodes that are influenced by <code>X</code>, or you can bevel all nodes that influence <code>Y</code>.<br /><br />
-In the image above, the path from <code>dp_ex_2</code> through <code>dp_ex_4</code> has been highlighted using the bevel style of the nodes.  (The result of pressing the "Bevel all from Ancestor to Descendant" button).</div>
 |dependency analysis
+|<div style="text-align: left;">This module tracks dependencies through your model, updating the visual appearance of nodes so that you can quickly visualize the paths by which one variable influences another.</div>
 |[[Dependency Tracker Module]]
 |-
 | Model
-|[[media:French-English.ana|French-English.ana]]
+|[[Media:French-English.ana|French-English.ana]]
 |multi-lingual models
-|<div style="text-align: left;">Maintains a single influence diagram with Title and Description attributes in both English and French.  With the change of a pull-down, the influence diagram and all object descriptions are instantly reflected in the language of choice.<br /><br />
-If you change a title or description while viewing English, and then change to French, the change you made will become the English-language version of the description.  Similarly if you make a change while viewing French.</div>
 |
+|<div style="text-align: left;">Maintains a single influence diagram with Title and Description attributes in both English and French.</div>
 |[[Multi-lingual Influence Diagram]]
 |-
 | Model
-|[[media:Parsing XML example.ana|Parsing XML example.ana]]
+|[[Media:Parsing XML example.ana|Parsing XML example.ana]]
 |data extraction, xml, DOM parsing
-|<div style="text-align: left;">Suppose you receive data in an XML format that you want to read into your model. This example demonstrates two methods for extracting data: Using a full XML DOM parser, or using regular expressions. The first method fully parses the XML structure, the second simply finds the data of interest by matching patterns, which can be easier for very simple data structures (as is often the case).</div>
 |
+|<div style="text-align: left;">Demonstrates two methods for extracting data: Using a full XML DOM parser, or using regular expressions.</div>
 |[[Extracting Data from an XML file]]
 |-
 | Model
-|[[media:Vector Math.ana|Vector Math.ana]]
+|[[Media:Vector Math.ana|Vector Math.ana]]
 |
-|<div style="text-align: left;">Functions used for computing geospatial coordinates and distances. Includes:<br />
-* A cross product of vectors function
-* Functions to conversion between spherical and Cartesian coordinates in 3-D
-* Functions to compute bearings from one latitude-longitude point to another
-* Functions for finding distance between two latitude-longitude points along the great circle.
-* Functions for finding the intersection of two great circles</div>
 |geospatial analysis, GIS, vector analysis
+|<div style="text-align: left;">Functions used for computing geospatial coordinates and distances.</div>
 |[[Vector Math]]
 |-
 | Model
-|[[media:Total Allowable Removal model with Optimizer.ana | Total Allowable w Optimizer.ana]] or<br />[[media:Total Allowable Removal model w StepInterp.ana|Total Allowable w StepInterp.ana]] for those without Optimizer
+|[[Media:Total Allowable Removal model with Optimizer.ana| Total Allowable w Optimizer.ana]] or<br />[[Media:Total Allowable Removal model w StepInterp.ana|Total Allowable w StepInterp.ana]] for those without Optimizer
 |
-|<div style="text-align: left;">The problem applies to any population of fish or animal whose dynamics are poorly known but can be summarized in a simple model:<br /><br />
-:<code>N_t + 1 = N_t*Lambda - landed catch*(1 + loss rate)</code>
-where «N_t» is the population size (number of individuals) at time ''t'', «N_t+1» is the population size at time ''t + 1'', «Lambda» is the intrinsic rate of increase and the «loss rate» is the percentage of fish or animals killed but not retrieved relative to the «landed catch», or catch secured.<br /><br />
-The question here is to determine how many fish or animals can be caught (landed) annually so that the probability of the population declining X%  in Y years (decline threshold) is less than Z% (risk tolerance).  <br /><br />
-Two models are available for download.  One uses the Optimizer ([[NlpDefine]]) to find the maximum landed catch at the risk tolerance level for the given decline threshold. The other (for those using a version of Analytica without Optimizer) uses [[StepInterp]] in an iterative way to get that maximum landed catch.</div>
 |population analysis, dynamic models, optimization analysis
+|<div style="text-align: left;">Determines how many fish or animals can be caught (landed) annually so that the probability of the population declining X%  in Y years (decline threshold) is less than Z% (risk tolerance).</div>
 |[[Total Allowable Harvest]]
 |-
 | Model
-|[[media:Cereal Formulation1.ana|Cereal Formulation1.ana]]
+|[[Media:Cereal Formulation1.ana|Cereal Formulation1.ana]]
 |product formulation, cereal formulation
-|<div style="text-align: left;">A cereal formulation model<br /><br />
-A discrete mixed integer model that chooses product formulations to minimize total ingredient costs.  This could be an NSP but it uses two methods to linearize:
-) Decision variable is constructed as a constrained Boolean array
-) Prices are defined as piecewise linear curves</div>
 |
+|<div style="text-align: left;">Cereal formulation/discrete mixed integer model that chooses product formulations to minimize total ingredient costs.</div>
 |[[Linearizing a discrete NSP]]
 |-
 | Model
-|[[media:Neural-Network.ana|Neural Network.ana]]
+|[[Media:Neural-Network.ana|Neural Network.ana]]
-|feed-forward neural networks
+|feed-forward neural networks, non-linear regression
-|<div style="text-align: left;">A feed-forward neural network can be trained (fit to training data) using the Analytica Optimizer.  This is essentially an example of non-linear regression.  This model contains four sample data sets, and is set up to train a 2-layer feedforward sigmoid network to "learn" the concept represented by the data set(s), and then test how well it does across examples not appearing in the training set.
-Developed during the Analytica User Group Webinar of 21-Apr-2011 -- see the [[Analytica_User_Group/Past_Topics#Neural_Networks|webinar recording]].</div>
 |optimization analysis
+|<div style="text-align: left;">Models set up to train a 2-layer feedforward sigmoid network to "learn" the concept represented by the data set(s), and then test how well it does across examples not appearing in the training set.</div>
 |[[Neural Network]]
 |-
 | Model
-|[[media:Earthquake expenses.ana|Earthquake expenses.ana]]
+|[[Media:Earthquake expenses.ana|Earthquake expenses.ana]]
 |
-|<div style="text-align: left;">An example of risk analysis with time-dependence and costs shifted over time.<br /><br />
-Certain organizations (insurance companies, large companies, governments) incur expenses following earthquakes.  This simplified demo model can be used to answer questions such as:<br />
-* What is the probability of more than one quake in a specific 10 year period.
-* What is the probability that in my time window my costs exceed $X?
-<br />Assumptions in this model:
-* Earthquakes are Poisson events with mean rate of once every 10 years.
-* Damage caused by such quake is lognormally distributed, with mean $10M adn stddev of $6M.
-* Cost of damage gets incurred over the period of a year from the date of the quake as equipment is replaced and buildings are repaired over time:  20% in 1st quarter after quake, 50% in 2nd quarter, 20% in 3rd quarter, 10% in 4th quarter.</div>
 |risk analysis, cost analysis
+|<div style="text-align: left;">An example of risk analysis with time-dependence and costs shifted over time.</div>
 |[[Earthquake Expenses]]
 |-
-|'''Best used with Analytica Optimizer'''<br />[[media:Loan policy selection.ANA|Loan policy selection.ana]]
+| Model
+|'''Best used with Analytica Optimizer'''<br />[[Media:Loan policy selection.ANA|Loan policy selection.ana]]
 |creditworthiness, credit rating, default risk
-|<div style="text-align: left;">A lender has a large pool of money to loan, but needs to decide what credit rating threshold to require and what interest rate (above prime) to charge.  The optimal value is determined by market forces (competing lenders) and by the probability that the borrower defaults on the loan, which is a function of the economy and borrower's credit rating.  The model can be used without the Analytica optimizer, in which case you can explore the decision space manually or use a parametric analysis to find the near optimal solution.  Those with Analytica Optimizer can find the optimal solution (more quickly) using an [[NlpDefine|NLP]] search.</div>
 |risk analysis
+|<div style="text-align: left;">Helps a lender decide optimal credit rating threshold to require and what interest rate (above prime) to charge.</div>
 |[[Loan Policy Selection]]
 |-
 | Model
-|[[media:Hubbard and Seiersen cyberrisk.ana|Hubbard_and_Seiersen_cyberrisk.ana]]
+|[[Media:Hubbard and Seiersen cyberrisk.ana|Hubbard_and_Seiersen_cyberrisk.ana]]
 |cybersecurity risk
-|<div style="text-align: left;">The model simulates loss exceedance curves for a set of cybersecurity events, the likelihood and probabilistic monetary impact of which have been characterized by system experts. The goal of the model is assess the impact of mitigation measures, by comparing the residual risk curve to the inherent risk curve (defined as risk without any mitigation measures) and to the risk tolerance curve. This is a translation of a model built by Douglas Hubbard and Richard Seiersen which they describe in their book [https://www.howtomeasureanything.com/cybersecurity/about-the-book/ How to Measure Anything in Cybersecurity Risk], and which they make available [https://www.howtomeasureanything.com/cybersecurity/ here].</div>
 |loss exceedance curve, simulation
+|<div style="text-align: left;">Simulates loss exceedance curves for a set of cybersecurity events, the likelihood and probabilistic monetary impact of which have been characterized by system experts.</div>
 |[[Inherent and Residual Risk Simulation]]
 |-
 | Model
-|[[media:Red State Blue State plot.ana]]
+|[[Media:Red_State_Blue_State_plot.ana]]
 |map, states
-|<div style="text-align: left;">This example contains the shape outlines for each of the 50 US states, along with a graph that uses color to depict something that varies by state (historical political party leaning). You may find the shape data useful for your own plots. In addition, it demonstrates the polygon fill feature that is new in [[Analytica 5.2]].</div>
 |graphing
+|<div style="text-align: left;">Example containing the shape outlines for each of the 50 US states, along with a graph that uses color to depict something that varies by state.</div>
 |[[Red or blue state]]
 |-
 | Model
-|[[media:COVID Model 2020--03-25.ana|COVID Model 2020--03-25.ana]]
+|[[Media:COVID Model 2020--03-25.ana|COVID Model 2020--03-25.ana]]
 |covid, covid-19, coronavirus, corona, epidemic
-|<div style="text-align: left;">A systems dynamics style SICR model of the COVID-19 outbreak within the state of Colorado. It simulates the progression of the outbreak into the future, examining the expected impact on ventilator (compared to levels available), forecasts number of sick and number of deaths, and also the risk reduction that a "lock down" has based on the date of the start of the lock down and the amount of reduction in social interaction. [https://lumidyne-test-site.webflow.io/the-energy-modeler/covid-19 A Lumidyne blog article] describes the model and conclusions ascertained from it.</div>
 |
+|<div style="text-align: left;">A systems dynamics style SICR model of the COVID-19 outbreak within the state of Colorado.</div>
 |[[COVID-19 State Simulator, a Systems Dynamics approach]]
 |-
@@ Line 396: / Line 351: @@
 | Model
 |[[Media:Corona Markov.ana|Corona Markov.ana]]
-|covid, covid-19, coronavirus, corona, epidemic
+|covid, covid-19, coronavirus, corona, epidemic, sensitivity analyses
-|<div style="text-align: left;">Used to explore the progression of the COVID-19 coronavirus epidemic in the US, and to explore the effects of different levels of social isolation. It also includes sensitivity analyses. [https://analytica.com/how-social-isolation-impacts-covid-19-spread-in-us-a-markov-model-approach/ A blog article] showcases this model.</div>
 |
+|<div style="text-align: left;">Explores the progression of the COVID-19 coronavirus epidemic in the US, and to explore the effects of different levels of social isolation.</div>
 |[[How social isolation impacts COVID-19 spread in the US - A Markov model approach]]
 |-
@@ Line 405: / Line 360: @@
 |[[Media:Modelo Epidemiologoco para el Covid-19 con cuarentena.ana|Modelo Epidemiológoco para el Covid-19 con cuarentena.ana]]
 |covid, covid-19, coronavirus, corona, epidemic
-|<div style="text-align: left;">Un modelo en cadena de Markov del impacto previsto de la enfermedad coronavirus COVID-19 en el Perú, y del impacto del aislamiento social.  Consulte el artículo [https://www.linkedin.com/posts/jorgemuroarbulu_este-estudio-est%C3%A1-adaptado-a-la-realidad-activity-6650119971621912576-yRbh/ Aislamiento Social y Propagación COVID-19]  para detalles.<br /><br />
-An adaptation and extension of Robert D. Brown's Markov Model (the previous example) to the country of Perú, translated into Spanish.</div>
 |
+|<div style="text-align: left;">Un modelo en cadena de Markov del impacto previsto de la enfermedad coronavirus COVID-19 en el Perú, y del impacto del aislamiento social.</div>
 |[[Epidemiological model of COVID-19 for Perú, en español]]
 |-
 | Model
-|[[media:COVID-19_Triangle_Suppression.ana|COVID-19 Triangle Suppression.ana]]
+|[[Media:COVID-19 Triangle Suppression.ana|COVID-19 Triangle Suppression.ana]]
 |covid, covid-19, coronavirus, corona, epidemic
-|<div style="text-align: left;">A novel approach to modeling the progression of the COVID-19 pandemic in the US, and understanding the amount of time that is required for lock down measures when a suppression strategy is adopted. This model is features in the blog article [https://lumina.com/forecast-update-us-deaths-from-covid-19-coronavirus-in-2020/ Suppression strategy and update forecast for US deaths from COVID-19 Coronavirus in 2020] on the Analytica blog.</div>
 |
+|<div style="text-align: left;">Models progression of the COVID-19 pandemic in the US, understanding the amount of time that is required for lock down measures when a suppression strategy is adopted.</div>
 |[[A Triangle Suppression model of COVID-19]]
 |-
@@ Line 422: / Line 376: @@
 |[[Media:Simple COVID-19.ana|Simple COVID-19.ana]]
 |covid, covid-19, coronavirus, corona, epidemic
-|<div style="text-align: left;">Used to explore possible COVID-19 Coronavirus scenarios from the beginning of March, 2020 through the end of 2020 in the US. The US is modeled as a closed system, which people classified as being in one of the progressive stages: Susceptible, Incubating, Contagious or Recovered. Deaths occur only from the Contagious stage. There is no compartimentalization such as by age or geography.</div>
 |
+|<div style="text-align: left;">Explores possible COVID-19 Coronavirus scenarios from the beginning of March, 2020 through the end of 2020 in the US.</div>
 |[[COVID-19 Coronavirus SICR progression for 2020]]
 |-
@@ Line 430: / Line 384: @@
 |[[Media:US COVID-19 Data.ana|US COVID-19 Data.ana]]
 |covid, covid-19, coronavirus, corona, epidemic, death, infection
-|<div style="text-align: left;">The [https://github.com/nytimes/covid-19-data New York Times has made data available] to researchers on the number of reported cases and deaths in each US county, and state-wide, on each day the pandemic. This model reads in these files and transforms them into a form that is convenient to work with in Analytica. <br /><br />
-'''Requires''': You'll need to install GIT and then clone the NYT repository. The Description of the model gives instructions for getting set up.  You'll also need to have the Analytica Enterprise or Optimizer edition.</div>
 |
+|<div style="text-align: left;">Reads in [https://github.com/nytimes/covid-19-data COVID-19 data from the New York Times] and transforms it into a form that is convenient to work with in Analytica.</div>
 |[[COVID-19 Case and Death data for US states and counties]]
 |-
@@ Line 439: / Line 392: @@
 |[[Media:Voluntary vs mandatory testing.ana|Voluntary vs mandatory testing.ana]]
 |covid, covid-19, coronavirus, corona, epidemic
-|<div style="text-align: left;">A Navy wants to compare two COVID-19 testing policies. In the first, all crew members must take a COVID-19 test before boarding a ship, and those with a positive test cannot board. In the second policy, testing is encouraged but voluntary -- each sailor has an option of being tested before boarding. This model computes the rate of infection among those allowed to board under the two scenarios, based on prevalence rates, test accuracies and voluntary testing rates. It also examines the probability of achieving zero infections on board, and the sensitivity of the results to input parameter estimates.  [https://analytica.com/voluntary-vs-mandatory-testing-for-naval-crew-selection This model is described in a blog posting].</div>
 |
+|<div style="text-align: left;">Computes the rate of infection in scenarios when COVID-19 testing is required/optional,based on prevalence rates, test accuracies and voluntary testing rates.</div>
 |[[Mandatory vs Voluntary testing policies]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Takes typical bond purchase inputs (purchase price, par value, interest rate, and life to maturity) and calculates bond cash flows, current yield, and yield to maturity.</div>
+|[[Bond Model]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Example of a breakeven analysis of a set of revenue levels, when the fixed expenses are set at one amount and the variable expenses are a constant fraction of revenue.</div>
+|[[Breakeven Analysis]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Evaluates and compares the expected commercialization value of multiple proposed R&D projects.</div>
+|[[Expected R&D Project Value]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Financial Statement Templates]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Explores a market for a new product, and the pricing and advertising budget decisions involved.</div>
+|[[Market Model]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Takes input data for activity paths required to complete a project, and calculates various outputs describing the critical path, timing, and costs for project completion.</div>
+|[[Plan_Schedule_ Control]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Evaluates and prioritizes a portfolio of projects based on either the estimated net present value or a multi-attribute score, based on strategy fit, staff development, the generation of public goodwill, and estimated net revenue.</div>
+|[[Project Portfolio Planner]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Sales Effectiveness]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Subscriber Pricing]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Demonstrates the use of a waterfall chart to visualize the components of an earnings stream from an asset.</div>
+|[[Waterfall chart]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Demonstrates how a distribution for an estimated statistic can be obtained by resampling repeatedly.</div>
+|[[Bootstrapping]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Example of scatter plots in Analytica.</div>
+|[[Kmeans Clustering]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Demonstrates the use of Logistic and Probit regression to fit a generalized linear model to breast cancer data.</div>
+|[[Logistic regression prior selection]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Moving Average Example]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Multidimensional Scaling]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Principle Components]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Regression Examples]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Example of how to decide where to throw a party. Shows how to model a two-branch decision tree in Analytica.</div>
+|[[Two Branch Party Tree]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Uses the beta distribution for the Bayesian update of beliefs about the probability that a coin will come up heads.</div>
+|[[Beta Updating]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Multi-project R&D evaluation models a typical R&D decision problem that might be faced by a biogenetic company.</div>
+|[[Biotech R&D Portfolio]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Diversification Illustration]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Expected Value of Sampling Information (EVSI)]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Gibbs Sampling in Bayesian Network]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Models R&D decision analysis for investment strategy among several choices of powerplants for a low emissions vehicle (LEV).</div>
+|[[LEV R&D Strategy]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Demonstrates a marginal benefit/cost analysis to determine the policy alternative that leads to the most economically efficient level of cleanup.</div>
+|[[Marginal Analysis for Control of SO2 Emissions]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Example of a multi-attribute utility analysis for cars, showing how to analyze an array of cars across an array of attributes, where different drivers assign differing weights to the importance of each attribute.</div>
+|[[Multi-attribute Utility Analysis]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Newton-Raphson Method]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Uses decision tree terminology to provide an example asymmetric decision tree in Analytica.
+</div>
+|[[Nonsymmetric Tree]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Party With Forecast]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Plane catching decision with EVIU]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Probability of Gaussian Region (Importance Sampling)]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Calculates the required supply level to maximize profit when the profit function is asymmetric around the average demand value.</div>
+|[[Supply and Demand]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Tornado Diagrams]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Represents a decision often faced in today’s world: which technology to purchase now, in the face of uncertain future products and prices.</div>
+|[[Upgrade Decision]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Forecasts the population of fish and the establishment of a contagious viral disease within the population over time.</div>
+|[[Disease establishment]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Levels staff efforts over time according to staff available, computing both the work done over time and idle time.</div>
+|[[Leveling]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Demonstrates how to simulate a Markov process using dynamic time. The example estimates the number of hospital patients over time, modeled as a Markov process.</div>
+|[[Markov Chain]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Mass-Spring-Damper]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Models a mean-reverting price process, along with a trading strategy to "beat the market". It demonstrates the encoding of a Markov Decision Process.</div>
+|[[Mean-reversion trading]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Minimal edit distance]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Optimal Path Dynamic Programming]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Parking Space Selection]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Projectile Motion]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Tunnel through earth]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Example of a dynamic variable that calculates growth over time, where Time is defined with unequal time steps. It is an example of exponential or linear growth or decay.</div>
+|[[Unequal time steps]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Curve fits noisy time-sequence data using an adaptive filter.</div>
+|[[Adaptive Filter]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Calculates the expected gain of an antenna looking at two different satellites.</div>
+|[[Antenna Gain]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Computes the load that a Douglas-Fir Large compression post can support.</div>
+|[[Compression Post Load Capacity]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Demonstration showing how to analyze life cycle costs and savings from daylighting options in building design.</div>
+|[[Daylight Analyzer]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Failure Analysis]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Ideal Gas Law]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Power dispatch]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Regular polygon calculator]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Find Words Game]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Fractals everywhere]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Probability assessment]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Abstracted Subset]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Assignment from Button]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Calculates the auto-correlation coefficients of noisy time sequence data.</div>
+|[[Autocorrelation]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Choice and Determtables]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Correlated Distributions]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Correlated Normals]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[DBWrite Example]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Discrete Sampling]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Demonstrates how to extract a diagonal from a matrix.</div>
+|[[Extracting Diagonal]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Lookup Reindexing]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Map images from internet]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Sample Size Input Node]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Sorting People by Height]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Creates a subset array out of a larger array based on a decision criterion.</div>
+|[[Subset of Array]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Swaps a computed or one-dimensional table value with its index, thereby making the computed value an index.</div>
+|[[Swapping y and x-index]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Use of MDTable]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Airline NLP]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Asset allocation]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Automobile Production]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Beer Distribution LP]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Big Mac Attack]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Example of capital budgeting for four possible projects, where the objective is to decide which projects to choose in order to maximize the total return.</div>
+|[[Capital Investment]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Diagnosing an infeasible set of linear constraints.</div>
+|[[Infeasible]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Linear program to determine which product variants to produce and how labor should be allocated among the various production steps based on the workers’ skill sets.</div>
+|[[Labor production allocation]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Demonstration of a grouped-integer domain. Fill in a square with the digits 1 through n2 such that the rows and columns all sum to the same value.</div>
+|[[Magic Square]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[NLP with Jacobian]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Optimal can dimensions]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Optimal Production Allocation]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Allocate planes to origin/destination legs to maximize gross margin.</div>
+|[[Plane allocation LP]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">1-D example illustrating local the problem of local optima.</div>
+|[[Polynomial NLP]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Problems with Local Optima]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Production Planning LP]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Example of a quadratically constrained optimization problem with a quadratic objective function.</div>
+|[[Quadratic Constraints]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Demonstrates how an nonlinear programming formulation can be used to solve a non- linear equation.</div>
+|[[Solve using NLP]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Find a solution to a Sudoku puzzle.</div>
+|[[Sudoku with Optimizer]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Find a minimal length tour through a set of cites.</div>
+|[[Traveling salesman]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Two Mines Model]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Vacation plan with PWL tax]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Projects cost assessments of damage resulting from large earthquakes over time. It demonstrates risk analysis with time-dependence and costs of events shifted over time.</div>
+|[[Earthquake expense risk]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Compares the value of various policies for restraints on occupants of automobiles.</div>
+|[[Seat belt safety]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Demonstrates risk/benefit analysis, in this case regarding the benefits of reducing the emissions of fictitious air pollutant TXC.</div>
+|[[Txc]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Rent vs Buy Model]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Rent vs Buy Analysis]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Car cost]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Car cost model ch 4]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Car cost model ch 5]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Hares sub-module - act I]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Foxes and hares sub-modules - act II]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Foxes and hares act III]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Demonstrate the building blocks for creating and editing variable definitions — expressions, standard operators, and mathematical functions.</div>
+|[[Expression Examples]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Input and Output Nodes]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Examples in this model demonstrate the basics of working with multidimensional arrays.</div>
+|[[Array Examples]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Examples in this model demonstrate many more of Analytica’s built-in array functions.</div>
+|[[Array Function Examples]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Analyzing Unc & Sens]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Continuous Distributions]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Discrete Distributions]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Dynamic model that finds the downward velocity and position of a dropped object over a six second time period.</div>
+|[[Dynamic & Dependencies]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Dynamic & Uncertainty]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Simple dynamic model, with one variable that changes over time. This example finds the gasoline price for each of five years, assuming a 5% growth rate.</div>
+|[[Dynamic Example 1]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Slight increase in complexity over Dynamic Example 1. Instead of assuming a fixed inflation rate, this example, looks at the price with three different inflation rates for comparison.
+</div>
+|[[Dynamic Example 2]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;">Illustrates a dynamic loop involving simultaneous recurrences over two distinct indexes.</div>
+|[[Dynamic on multiple indexes]]
+|-
+| Model
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Dynamic on non-Time index]]
+|-
+| Library
+|Included with software
+|
+|
+|<div style="text-align: left;">Functions that convert between binary, octal, decimal integer and hexadecimal values.</div>
+|[[Base conversion library]]
+|-
+| Library
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Bayes Function]]
+|-
+| Library
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Complex Library]]
+|-
+| Library
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Concatenation]]
+|-
+| Library
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Data Statistics Library]]
+|-
+| Library
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Distribution Densities]]
+|-
+| Library
+|Included with software
+|
+|
+|<div style="text-align: left;">Contains various functions for defining standard distributions using different sets of parameters.</div>
+|[[Distribution Variations]]
+|-
+| Library
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Expand Index]]
+|-
+| Library
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Financial Library]]
+|-
+| Library
+|Included with software
+|
+|
+|<div style="text-align: left;">Provides functions for writing data to and from flat files, particularly between two- dimensional tables and comma-separated value (CSV) files.</div>
+|[[Flat File Library]]
+|-
+| Library
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Garbage Bin Library]]
+|-
+| Library
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Generalized Regression]]
+|-
+| Library
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Linked List Library]]
+|-
+| Library
+|Included with software
+|
+|
+|<div style="text-align: left;">Contains functions for creating several multivariate distributions.</div>
+|[[Multivariate Distributions Library]]
+|-
+| Library
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[ODBC-Library]]
+|-
+| Library
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Optimization functions]]
+|-
+| Library
+|Included with software
+|
+|
+|<div style="text-align: left;">See which variables and functions are taking most of the computation time when running your model.</div>
+|[[Performance Profiler]]
+|-
+| Library
+|Included with software
+|
+|
+|<div style="text-align: left;"></div>
+|[[Structured Optimization Tools]]
 |-
 |}
 </div>