yokesh sankar

yokesh sankar


Ethereum ERC-20 Token — A Standard Token for an Entire Ecosystem

Ethereum is a blockchain designed to have functionality beyond creating and recording transfers of its own cryptocurrencies. Among these features, the ability to create other tokens that will run on its blockchain stands out.

This curious functionality of Ethereum is possible thanks to the ERC-20 tokens . In this article, we will examine some elements that will allow us to know everything related to one of the most important technologies implemented by Ethereum.

The origin of the ERC-20 token

With this improvement, the Ethereum developers wanted to implement a standard system for tokens within smart contracts. Ethereum token development as well as allowing tokens to be approved so they can be spent by another third party on the blockchain.The idea was introduced by the developer, Fabian Vogelsteller in the official Ethereum GitHub repository in 2015.

What are ERC-20 tokens?

ERC-20 tokens are essentially a smart contract . These smarts contracts use a standard interface that runs on the Ethereum network. Thanks to this, they obtain great flexibility and ability to be used in different scenarios.

The purpose of these rules is to allow the coexistence of other tokens on Ethereum, without diminishing the evolution capabilities of the blockchain technology or the platforms that use them. In this way, the developers of the DApps have a wide freedom when it comes to programming their applications and effectively tokenizing their projects. Thanks to this, the ERC-20 tokens are capable of achieving the objective for which they were created; allow any Ethereum token to be reused by other applications: from wallets to any decentralized application.

How does an ERC-20 token work?

Without going too far into the technical aspect, the operation of an ERC-20 token is simple. The developers of Ethereum worked hard to make this standard easy to use in software development. This has a clear objective: to facilitate its adoption and use by other developers. Ethereum token development services To achieve this, the token interface design has a series of well-identified and well-defined elements. Each of these elements has a specific utility and function, and can be used by any DApp built to use this standard. To explain these elements, let’s take Golem ‘s ERC-20 token as an example :

Name (Name). With this element we can name the token created and managed by the DApp. It serves to identify and differentiate the token created from others that already exist. For example, the name Golem, is the token name of the Golem distributed computing DApp.

Symbol (Symbol). This element mentions the symbol or abbreviation of the token. Following the example above, this field would define the symbol of the Golem token, the GNT. Decimals (Decimals). This field is used to define the number of decimal places that the token will use. Total supply (TotalSupply). This indicates the total supply of tokens that will exist. This is a value that is decided by the developers of the new token, and may be associated with different economic rules depending on the consideration of its developers. Balance (Balance Of). With this element, you can find out the balance in a certain network account. In this case, it would be the number of GNT tokens a user has in a given wallet.

Transfer. This is one of the elements that allow us to control the assets that we have in our accounts. The ERC-20 token has two elements that allow this type of action.Approved. Thanks to this function we can make withdrawals from the accounts. This is a special element of control, as it allows us to know the amount of tokens that we can withdraw from a certain account. Together with the Approved function, it allows accounting control of the tokens within an account and enables automated management options for them.

ERC-20 tokens, in essence, are a smart contract It is thanks to each of these functions that the ERC-20 tokens have the great versatility that characterizes them. Well, it allows a quick implementation of the same for various uses without having to alter its structure. In addition, thanks to the fact that these are executed on the Ethereum blockchain, their traceability and auditability are guaranteed, since their actions can be traced on the Ethereum blockchain.

Among the advantages of the ERC-20 token is the saving of time and resources . ERC-20 token development tokens take advantage of the existing Ethereum infrastructure rather than creating an entirely new blockchain for them. In addition, they have greater security, since the creation of new tokens increases the demand for Ether, which makes the entire network even more secure, that is, less susceptible to a potential hacker attack.

If all tokens created on the Ethereum network use the same standard, these tokens will be easily interchangeable and can easily work with other applications in the same ecosystem. We cannot fail to mention among its advantages the great liquidity they provide, since the ERC-20 tokens are used as the basis of work for most projects on the blockchain.

What is GEEK

Buddha Community

Ethereum ERC-20 Token — A Standard Token for an Entire Ecosystem
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Jack  Shaw

Jack Shaw


A Curated List Of Awesome Ethereum Ressources

A curated list of awesome Ethereum Ressources. Inspired by awesome-go.


Please take a quick gander at the contribution guidelines first. Thanks to all contributors; you rock!

If you see a link or project here that is no longer maintained or is not a good fit, please submit a pull request to improve this file. Thank you!

Basic {#basic}

What is Ethereum? {#what-is-ethereum}

Bitcoin 2.0? a world computer? a smart contracts platform?

Papers {#papers}

If you feel like going to the source

Roadmap {#roadmap}

  • Timeline - Expected timeline - Post from Mars 2015.
    • Olympic - 0: Olympic.
    • Frontier - 1: Frontier.
    • Homestead - 2: Homestead <----- HERE WE ARE.
    • Metropolis - 3: Metropolis - "when we finally officially release a relatively full-featured user interface for non-technical users of Ethereum"
    • Serenity - 4: Serenity - Switching the network from Proof of Work to Proof of Stake ( Casper). end of 2016?.

Branding / Logo {#branding}

Crowfunding {#crowfunding}

Remembering a time where the price of Ether was 2000 ETH per BTC

Foundation {#foundation}

The Ethereum Foundation’s mission is to promote and support research, development and education to bring decentralized protocols and tools to the world that empower developers to produce next generation decentralized applications (DAPPs), and together build a more globally accessible, more free and more trustworthy Internet.

  • Website - The Ethereum foundation Page.

Clients {#clients}

Implementations of the Ethereum protocol.

The Ethereum network {#network}

Network Stat {#network-stats}

Need information about a block, a current difficulty, the network hashrate?

  • Ethstats - See latest data of the Ethereum Network.

Blockhain Explorer {#blockchain-explorer}



Ether {#ether}

Ether is the name of the currency used within Ethereum

Info {#ether-info}

SPOILER: There are about 77 million ethers in existence and every new block (an average of 15 seconds) creates 5 new ether.

Exchanges {#exchanges}

Where you can trade ethers - Remember: if you don't control the private you don't really control the ethers

Faucets {#faucets}

Free Ether? don't have big expectation :)



Wallets {#wallets}

To store your ethers

  • Mist - Mist - Official wallet with integrated full node.
  • Jaxx - By KryptoKit, Wallets that unify the Bitcoin and Ethereum experience accross Devices.
  • Myetherwallet - Open Source JavaScript Client-Side Ether Wallet.
  • Icebox - Lightwallet-powered cold storage solution..

Mining {#mining}

let's make the network work! and earn some ethers!

How to {#mining-hoe-to}

Mining pools {#mining-pools}

Fell alone? join a pool

Smart Contract languages {#smart-contracts-languages}


Solidity, the JavaScript-like language


Serpent, the Python-like language


LLL, the Lisp-like languagee

DAPP {#dapp}

Tutorials {#tutorials}

IDE {#ide}

Others awesome things & concepts {#others}

Casper {#casper}

  • Casper - Casper - Proof of Work (PoW) for Serenity.
  • Research - ethereum/research

Whisper {#whisper}

an upcoming P2P messaging protocol that will be integrated into the EtherBrowser.

  • Whisper Wiki Wiki article about Whisper ( December 2014)-
  • Whisper ? - What is Whisper and what is it used for?.

Swarm {#swarm}

  • Swarm - Swarm for Storage .

web3-j {#web3-j}

Ethereum compatible JavaScript API which implements the Generic JSON RPC spec.

Gas {#gas}

Gas is the fundamental network cost unit and is paid for exclusively in ether.

  • Gas Doc - Gas and transaction costs from the Ethereum Documentation.
  • What is Gas? - What is the “Gas” in Ethereum? -Post from CryptoCompare.
  • Cost calculator - Calculate the cost of conducting a transaction or executing a contract on Ethereum.

Projects using Ethereum {#projects}

Big ones

  • Augur - Prediction Market.
  • Slock.it - Rent, sell or share anything - without middlemen.
  • Digix - Transparent asset tracking of LBMA GOLD with blockchain technology 2.0.

Lists of projects

Companies {#companies}

Community {#community}

Social {#social}

Skype {#skype}

Main Skype Channels

  • Ethereum - Ethereum: the main channel, bridged to IRC #ethereum.
  • Ethereum-dev - Ethereum-dev: the developer's channel, bridged to IRC #ethereum-dev.

Speciality Skype Channels

Regional Skype Channels

  • London - London General: London-based Etherians.
  • Italia - Italia: Italian Etherians.
  • Romania - Romania: Romanian Etherians.
  • Russia - Russia - Russian Etherians (Russian language).

Gitter channels

IRC channels (Freenode)

  • Go-Ethereum -
    • #ethereum: for general discussion
    • #ethereum-dev: for development specific questions and discussions
    • ##ethereum: for offtopic and banter
    • #ethereum-mining: for mining only conversations
    • #ethereum-markets: for discussions about markets

Meetups {#meetups}

Events {#events}


Stay up to date! {#up-to-date}

Newsletter {#newsletter}

Podcast {#podcast}


Your contributions are always welcome! Please take a look at the contribution guidelines first.

I would keep some pull requests open if I'm not sure whether the content are awesome, you could vote for them by leaving a comment that contains +1.

To be added

  • Jobs
  • Courses

Download details:

Author: lampGit
Source code: https://github.com/lampGit/awesome-ethereum


RobustStats.jl: A Collection Of Robust Statistical Tests in Julia


This package contains a variety of functions from the field robust statistical methods. Many are estimators of location or dispersion; others estimate the standard error or the confidence intervals for the location or dispresion estimators, generally computed by the bootstrap method.

Many functions in this package are based on the R package WRS (an R-Forge repository) by Rand Wilcox. Others were contributed by users as needed. References to the statistics literature can be found below.

This package requires Compat, Rmath, Dataframes, and Distributions. They can be installed automatically, or by invoking Pkg.add("packagename").


Location estimators:

  • tmean(x, tr=0.2) - Trimmed mean: mean of data with the lowest and highest fraction tr of values omitted.
  • winmean(x, tr=0.2)- Winsorized mean: mean of data with the lowest and highest fraction tr of values squashed to the 20%ile or 80%ile value, respectively.
  • tauloc(x) - Tau measure of location by Yohai and Zamar.
  • onestep(x) - One-step M-estimator of location using Huber's ψ
  • mom(x) - Modified one-step M-estimator of location (MOM)
  • bisquareWM(x) - Mean with weights given by the bisquare rho function.
  • huberWM(x) - Mean with weights given by Huber's rho function.
  • trimean(x) - Tukey's trimean, the average of the median and the midhinge.

Dispersion estimators:

  • winvar(x, tr=0.2) - Winsorized variance.
  • wincov(x, y, tr=0.2) - Winsorized covariance.
  • pbvar(x) - Percentage bend midvariance.
  • bivar(x) - Biweight midvariance.
  • tauvar(x) - Tau measure of scale by Yohai and Zamar.
  • iqrn(x) - Normalized inter-quartile range (normalized to equal σ for Gaussians).
  • shorthrange(x) - Length of the shortest closed interval containing at least half the data.
  • scaleQ(x) - Normalized Rousseeuw & Croux Q statistic, from the 25%ile of all 2-point distances.
  • scaleS(x) - Normalized Rousseeuw & Croux S statistic, from the median of the median of all 2-point distances.
  • shorthrange!(x), scaleQ!(x), and scaleS!(x) are non-copying (that is, x-modifying) forms of the above.

Confidence interval or standard error estimates:

  • trimse(x) - Standard error of the trimmed mean.
  • trimci(x) - Confidence interval for the trimmed mean.
  • msmedse(x) - Standard error of the median.
  • binomci(s,n) - Binomial confidence interval (Pratt's method).
  • acbinomci(s,n) - Binomial confidence interval (Agresti-Coull method).
  • sint(x) - Confidence interval for the median (with optional p-value).
  • momci(x) - Confidence interval of the modified one-step M-estimator of location (MOM).
  • trimpb(x) - Confidence interval for trimmed mean.
  • pcorb(x) - Confidence intervale for Pearson's correlation coefficient.
  • yuend - Compare the trimmed means of two dependent random variables.
  • bootstrapci(x, est=f) - Compute a confidence interval for estimator f(x) by bootstrap methods.
  • bootstrapse(x, est=f) - Compute a standard error of estimator f(x) by bootstrap methods.

Utility functions:

  • winval(x, tr=0.2) - Return a Winsorized copy of the data.
  • idealf(x) - Ideal fourths, interpolated 1st and 3rd quartiles.
  • outbox(x) - Outlier detection.
  • hpsi(x) - Huber's ψ function.
  • contam_randn - Contaminated normal distribution (generates random deviates).
  • _weightedhighmedian(x) - Weighted median (breaks ties by rounding up). Used in scaleQ.


For location, consider the bisquareWM with k=3.9σ, if you can make any reasonable guess as to the "Gaussian-like width" σ (see dispersion estimators for this). If not, trimean is a good second choice, though less efficient. Also, though the author personally has no experience with them, tauloc, onestep, and mom might be useful.

For dispersion, the scaleS is a good general choice, though scaleQ is very efficient for nearly Gaussian data. The MAD is the most robust though less efficient. If scaleS doesn't work, then shorthrange is a good second choice.

The first reference on scaleQ and scaleS (below) is a lengthy discussion of the tradeoffs among scaleQ, scaleS, shortest half, and median absolute deviation (MAD, see BaseStats.mad for Julia implementation). All four have the virtue of having the maximum possible breakdown point, 50%. This means that replacing up to 50% of the data with unbounded bad values leaves the statistic still bounded. The efficiency of Q is better than S and S is better than MAD (for Gaussian distributions), and the influence of a single bad point and the bias due to a fraction of bad points is only slightly larger on Q or S than on MAD. Unlike MAD, the other three do not implicitly assume a symmetric distribution.

To choose between Q and S, the authors note that Q has higher statistical efficiency, but S is typically twice as fast to compute and has lower gross-error sensitivity. An interesting advantage of Q over the others is that its influence function is continuous. For a rough idea about the efficiency, the large-N limit of the standardized variance of each quantity is 2.722 for MAD, 1.714 for S, and 1.216 for Q, relative to 1.000 for the standard deviation (given Gaussian data). The paper gives the ratios for Cauchy and exponential distributions, too; the efficiency advantages of Q are less for Cauchy than for the other distributions.


#Set up a sample dataset:
x=[1.672064, 0.7876588, 0.317322, 0.9721646, 0.4004206, 1.665123, 3.059971, 0.09459603, 1.27424, 3.522148,
   0.8211308, 1.328767, 2.825956, 0.1102891, 0.06314285, 2.59152, 8.624108, 0.6516885, 5.770285, 0.5154299]

julia> mean(x)     #the mean of this dataset

tmean: trimmed mean

julia> tmean(x)            #20% trimming by default

julia> tmean(x, tr=0)      #no trimming; the same as the output of mean()

julia> tmean(x, tr=0.3)    #30% trimming

julia> tmean(x, tr=0.5)    #50% trimming, which gives you the median of the dataset.

winval: winsorize data

That is, return a copy of the input array, with the extreme low or high values replaced by the lowest or highest non-extreme value, repectively. The fraction considered extreme can be between 0 and 0.5, with 0.2 as the default.

julia> winval(x)           #20% winsorization; can be changed via the named argument `tr`.
20-element Any Array:

winmean, winvar, wincov: winsorized mean, variance, and covariance

julia> winmean(x)          #20% winsorization; can be changed via the named argument `tr`.
julia> winvar(x)
julia> wincov(x, x)
julia> wincov(x, x.^2)

trimse: estimated standard error of the trimmed mean

julia> trimse(x)           #20% winsorization; can be changed via the named argument `tr`.

trimci: (1-α) confidence interval for the trimmed mean

Can be used for paired groups if x consists of the difference scores of two paired groups.

julia> trimci(x)                 #20% winsorization; can be changed via the named argument `tr`.
(1-α) confidence interval for the trimmed mean

Degrees of freedom:   11
Estimate:             1.292180
Statistic:            3.469611
Confidence interval:  0.472472       2.111889
p value:              0.005244

idealf: the ideal fourths:

Returns (q1,q3), the 1st and 3rd quartiles. These will be a weighted sum of the values that bracket the exact quartiles, analogous to how we handle the median of an even-length array.

julia> idealf(x)

pbvar: percentage bend midvariance

A robust estimator of scale (dispersion). See NIST ITL webpage for more.

julia> pbvar(x)

bivar: biweight midvariance

A robust estimator of scale (dispersion). See NIST ITL webpage for more.

julia> bivar(x)

tauloc, tauvar: tau measure of location and scale

Robust estimators of location and scale, with breakdown points of 50%.

See Yohai and Zamar JASA, vol 83 (1988), pp 406-413 and Maronna and Zamar Technometrics, vol 44 (2002), pp. 307-317.

julia> tauloc(x)       #the named argument `cval` is 4.5 by default.
julia> tauvar(x)

outbox: outlier detection

Use a modified boxplot rule based on the ideal fourths; when the named argument mbox is set to true, a modification of the boxplot rule suggested by Carling (2000) is used.

julia> outbox(x)
Outlier detection method using
the ideal-fourths based boxplot rule

Outlier ID:         17
Outlier value:      8.62411
Number of outliers: 1
Non-outlier ID:     1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20

msmedse: Standard error of the median

Return the standard error of the median, computed through the method recommended by McKean and Schrader (1984).

julia> msmedse(x)

binomci(), acbinomci(): Binomial confidence interval

Compute the (1-α) confidence interval for p, the binomial probability of success, given s successes in n trials. Instead of s and n, can use a vector x whose values are all 0 and 1, recording failure/success one trial at a time. Returns an object.

binomci uses Pratt's method; acbinomci uses a generalization of the Agresti-Coull method that was studied by Brown, Cai, & DasGupta.

julia> binomci(2, 10)           # # of success and # of total trials are provided. By default alpha=.05
p_hat:               0.2000
confidence interval: 0.0274   0.5562
Sample size          10

julia> trials=[1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0]
julia> binomci(trials, alpha=0.01)    #trial results are provided in array form consisting of 1's and 0's.
 p_hat:               0.5000
 confidence interval: 0.1768   0.8495
 Sample size          12

julia> acbinomci(2, 10)           # # of success and # of total trials are provided. By default alpha=.05
p_hat:               0.2000
confidence interval: 0.0459   0.5206
Sample size          10


Compute the confidence interval for the median. Optionally, uses the Hettmansperger-Sheather interpolation method to also estimate a p-value.

julia> sint(x)
Confidence interval for the median

 Confidence interval:  0.547483       2.375232

julia> sint(x, 0.6)
Confidence interval for the median with p-val

 Confidence interval:  0.547483       2.375232
 p value:              0.071861


Compute Huber's ψ. The default bending constant is 1.28.

julia> hpsi(x)
20-element Array{Float64,1}:


Compute one-step M-estimator of location using Huber's ψ. The default bending constant is 1.28.

julia> onestep(x)

bootstrapci, bootstrapse

Compute a bootstrap, (1-α) confidence interval (bootstrapci) or a standard error (bootstrapse) for the measure of location corresponding to the argument est. By default, the median is used. Default α=0.05.

julia> ci = bootstrapci(x, est=onestep, nullvalue=0.6)
 Estimate:             1.305811
 Confidence interval:  0.687723       2.259071
 p value:              0.026000

julia> se = bootstrapse(x, est=onestep)

mom and mom!

Returns a modified one-step M-estimator of location (MOM), which is the unweighted mean of all values not more than (bend times the mad(x)) away from the data median.

julia> mom(x)


Compute the bootstrap (1-α) confidence interval for the MOM-estimator of location based on Huber's ψ. Default α=0.05.

julia> momci(x, seed=2, nboot=2000, nullvalue=0.6)
Estimate:             1.259646
Confidence interval:  0.504223       2.120979
p value:              0.131000


Create contaminated normal distributions. Most values will by from a N(0,1) zero-mean unit-variance normal distribution. A fraction epsilon of all values will have k times the standard devation of the others. Default: epsilon=0.1 and k=10.

julia> srand(1);
julia> std(contam_randn(2000))


Compute a (1-α) confidence interval for a trimmed mean by bootstrap methods.

julia> trimpb(x, nullvalue=0.75)
 Estimate:             1.292180
 Confidence interval:  0.690539       2.196381
 p value:              0.086000


Compute a .95 confidence interval for Pearson's correlation coefficient. This function uses an adjusted percentile bootstrap method that gives good results when the error term is heteroscedastic.

julia> pcorb(x, x.^5)
 Estimate:             0.802639
 Confidence interval:  0.683700       0.963478


Compare the trimmed means of two dependent random variables using the data in x and y. The default amount of trimming is 20%.

julia> srand(3)
julia> y2 = randn(20)+3;
julia> yuend(x, y2)

Comparing the trimmed means of two dependent variables.

Sample size:          20
Degrees of freedom:   11
Estimate:            -1.547776
Standard error:       0.460304
Statistic:           -3.362507
Confidence interval: -2.560898      -0.534653
p value:              0.006336

Unmaintained functions

See UNMAINTAINED.md for information about functions that the maintainers have not yet understood but also not yet deleted entirely.


Percentage bend and related estimators come from L.H. Shoemaker and T.P. Hettmansperger "Robust estimates and tests for the one- and two-sample scale models" in Biometrika Vol 69 (1982) pp. 47-53.

Tau measures of location and scale are from V.J. Yohai and R.H. Zamar "High Breakdown-Point Estimates of Regression by Means of the Minimization of an Efficient Scale" in J. American Statistical Assoc. vol 83 (1988) pp. 406-413.

The outbox(..., mbox=true) modification was suggested in K. Carling, "Resistant outlier rules and the non-Gaussian case" in Computational Statistics and Data Analysis vol 33 (2000), pp. 249-258. doi:10.1016/S0167-9473(99)00057-2

The estimate of the standard error of the median, msmedse(x), is computed by the method of J.W. McKean and R.M. Schrader, "A comparison of methods for studentizing the sample median" in Communications in Statistics: Simulation and Computation vol 13 (1984) pp. 751-773. doi:10.1080/03610918408812413

For Pratt's method of computing binomial confidence intervals, see J.W. Pratt (1968) "A normal approximation for binomial, F, Beta, and other common, related tail probabilities, II" J. American Statistical Assoc., vol 63, pp. 1457- 1483, doi:10.1080/01621459.1968.10480939. Also R.G. Newcombe "Confidence Intervals for a binomial proportion" Stat. in Medicine vol 13 (1994) pp 1283-1285, doi:10.1002/sim.4780131209.

For the Agresti-Coull method of computing binomial confidence intervals, see L.D. Brown, T.T. Cai, & A. DasGupta "Confidence Intervals for a Binomial Proportion and Asymptotic Expansions" in Annals of Statistics, vol 30 (2002), pp. 160-201.

Shortest Half-range comes from P.J. Rousseeuw and A.M. Leroy, "A Robust Scale Estimator Based on the Shortest Half" in Statistica Neerlandica Vol 42 (1988), pp. 103-116. doi:10.1111/j.1467-9574.1988.tb01224.x . See also R.D. Martin and R. H. Zamar, "Bias-Robust Estimation of Scale" in Annals of Statistics Vol 21 (1993) pp. 991-1017. doi:10.1214/aoe/1176349161

Scale-Q and Scale-S statistics are described in P.J. Rousseeuw and C. Croux "Alternatives to the Median Absolute Deviation" in J. American Statistical Assoc. Vo 88 (1993) pp 1273-1283. The time-efficient algorithms for computing them appear in C. Croux and P.J. Rousseeuw, "Time-Efficient Algorithms for Two Highly Robust Estimators of Scale" in Computational Statistics, Vol I (1992), Y. Dodge and J. Whittaker editors, Heidelberg, Physica-Verlag, pp 411-428. If link fails, see ftp://ftp.win.ua.ac.be/pub/preprints/92/Timeff92.pdf

Download Details:

Author: Mrxiaohe
Source Code: https://github.com/mrxiaohe/RobustStats.jl 
License: MIT license

#julia #statistical #tests 

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