1645420669

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.

1616568076

The Blockchain App Factory is a leading industry with **Ethereum Token development platform** that offers services like Token creation, Token migration, Token Listing, Secured Storage, ICO development, ERC token wallet. They also build and generate Ethereum tokens such as ERC 20, ERC 721, ERC 777.

#erc token development #ethereum token development company #ethereum token development services #ethereum(erc) token development services #erc 20 development #erc 721 development

1672986240

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}

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

- Ethereum: the World Computer - Youtube video : Ethereum: the World Computer .
- What is ethereum ? - Description form the official documentation.
- Ethereum Infographic - A great infographic.
- At Quora - What is Ethereum, in layman's term?

*If you feel like going to the source*

- Ethereum Whitepaper - Ethereum Whitepaper.
- Ethereum Yellow Paper - Ethereum Yellow Paper.

- 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?.

- Logo Assets - All current Ethereum logos (under Creative Commons attribution 3.0).
- Ethereum Visual Identity - Ethereum Visual Identity Guide.

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

- Launching the ether sale - Blog post of the July 22nd 2014 explaining the crowfunding.
- Purchase Agreement - Crowfunding purchase agreement.
- Intended use if revenue - Intended use of revenue.

*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.*

- C++ Client - C++ Client.
- Go Client - Go Client.
- Java Client - Java Client.
- NodeJS Client - NodeJS Client.
- Python Client - Python Client.
- Parity - Next Generation Ethereum Client - Rust language.

The Ethereum network {#network}

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

- Ethstats - See latest data of the Ethereum Network.

- Etherchain - Etherchain.
- Ether camp - Ether camp.
- Etherscan - Etherscan .

- Etherscan - Etherscan for the testnet.

Ether {#ether}

*Ether is the name of the currency used within Ethereum*

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

- What is ether ? - .
- Ether Stat - Stat about Ether.
- Supply of Ether - Good post about the total supply of Ether.

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

- Poloniex - Poloniex.
- Kraken - Kraken.
- GateCoin - Gatecoin.
- Bittrex - Bitrrex.
- Exmo - Exmo.
- Bitfinex - Bitfinex.
- The whole list - All exchanges accepting ETH from coinmarketcap.com.

*Free Ether? don't have big expectation :)*

- Ether faucet - Ether faucet.

- Wei faucet - Wei faucet for the tesnet.

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!*

- Mining FAQ - Mining FAQ.
- How to mine Ethereum on a Windows PC? - How to mine Ethereum on a Windows PC?.

*Fell alone? join a pool*

- Coinmine - Coinmine Pool.
- Coinotron - Coinotron Pool.
- Dwarfpool - Dwarfpool .
- Ethpool - Ethpool.
- Nanopool - Nanopool.

Smart Contract languages {#smart-contracts-languages}

*Solidity, the JavaScript-like language*

- Solidity Guide - Learn the Solidity Language.

*Serpent, the Python-like language*

*LLL, the Lisp-like languagee*

DAPP {#dapp}

- Mix - MIX.

- Mix - MIX.

Others awesome things & concepts {#others}

*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 for Storage .

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

- web3-j GitHub - GitHub Repo.
- web3-j documentation - web3-j documentation.

*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}

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

- State of the Dapps - State of the Dapps - an impressive list of projects.

Companies {#companies}

- Consensys - Consensys.

Community {#community}

- Facebook - Facebook.
- Twitter - Twitter.
- Reddit - Reddit.
- Youtube - .
- IRC - IRC.
- Blog - The official blog.
- Stack Exchange - Stack Exchange.
- Forum - Forum.

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

- Solidity and web3 - Solidity and web3.
- Pi Ethereum - Pi Ethereum.
- Serpent - Serpent.
- Miner community Support -Miner community Support.
- ÐΞVgrants -ÐΞVgrants.
- Ethereum: Reputation - Ethereum: Reputation - discussion, education and delivery.
- Netstats - Netstats: chat regarding stats.ethdev.com.

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

- Go-Ethereum - Go-Ethereum.

- 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

- London, UK - London, UK.
- Paris, France - Paris, France.
- New York, USA - New York, USA.
- Toronto, Canada - Toronto, Canada.
- San Francisco, USA - San Francisco, USA.
- Sao Paulo, Brazil - Sao Paulo, Brazil.
- Portland, USA - Portland, USA.
- Tel Aviv-Yafo, Israel - Tel Aviv-Yafo, Israel.
- Tokyo, Japan - Tokyo, Japan.
- Chicago, USA - Chicago, USA.
- Melbourne, Australia - Melbourne, Australia.
- Sao Paulo, Brazil - Sao Paulo, Brazil.
- Vancouvert, Canada - Vancouvert, Canada.
- Tehran, Iran - Tehran, Iran.

- Devcon1 (2015) playlist - Devcon1 (2015) playlist.
- Devcon0 (2014) playlist - Devcon0 (2014) playlist.

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

- The Ether Review - The Ether Review.

Contributing

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

Author: lampGit

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

1677864120

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")`

.

`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.

`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.

`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.

`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
1.853401259
```

`tmean`

: trimmed mean```
julia> tmean(x) #20% trimming by default
1.2921802666666669
julia> tmean(x, tr=0) #no trimming; the same as the output of mean()
1.853401259
julia> tmean(x, tr=0.3) #30% trimming
1.1466045875000002
julia> tmean(x, tr=0.5) #50% trimming, which gives you the median of the dataset.
1.1232023
```

`winval`

: winsorize dataThat 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:
1.67206
0.787659
0.400421
0.972165
...
0.651689
2.82596
0.51543
```

`winmean`

, `winvar`

, `wincov`

: winsorized mean, variance, and covariance```
julia> winmean(x) #20% winsorization; can be changed via the named argument `tr`.
1.4205834800000001
julia> winvar(x)
0.998659015947531
julia> wincov(x, x)
0.998659015947531
julia> wincov(x, x.^2)
3.2819238397424004
```

`trimse`

: estimated standard error of the trimmed mean```
julia> trimse(x) #20% winsorization; can be changed via the named argument `tr`.
0.3724280347984342
```

`trimci`

: (1-α) confidence interval for the trimmed meanCan 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)
(0.4483411416666667,2.7282743333333332)
```

`pbvar`

: percentage bend midvarianceA robust estimator of scale (dispersion). See NIST ITL webpage for more.

```
julia> pbvar(x)
2.0009575278957623
```

`bivar`

: biweight midvarianceA robust estimator of scale (dispersion). See NIST ITL webpage for more.

```
julia> bivar(x)
1.5885279811329132
```

`tauloc`

, `tauvar`

: tau measure of location and scaleRobust 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.
1.2696652567510853
julia> tauvar(x)
1.53008203090696
```

`outbox`

: outlier detectionUse 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 medianReturn the standard error of the median, computed through the method recommended by McKean and Schrader (1984).

```
julia> msmedse(x)
0.4708261134886094
```

`binomci()`

, `acbinomci()`

: Binomial confidence intervalCompute 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
```

`sint()`

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
```

`hpsi`

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

```
julia> hpsi(x)
20-element Array{Float64,1}:
1.28
0.787659
0.317322
0.972165
0.400421
...
```

`onestep`

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

```
julia> onestep(x)
1.3058109021286803
```

`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)
0.41956761772722817
```

`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)
1.2596462322222222
```

`momci`

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
```

`contam_randn`

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))
3.516722458797104
```

`trimpb`

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
```

`pcorb`

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
```

`yuend`

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
```

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

Author: Mrxiaohe

Source Code: https://github.com/mrxiaohe/RobustStats.jl

License: MIT license

1625808587

Our dedicated Ethereum developers can create blockchain wallet apps, smart contracts, distributed applications (dApp), cryptocurrency (bitcoin) apps and more. Our experts work as your extended team and are capable to deliver quality Ethereum solutions meeting your business challenges.

Why Go For Hiring Ethereum Developers?

Hiring Blockchain Ethereum developers and programmers from India can help you save your time and cost. You get optimum quality Ethereum software solutions at highly affordable prices.

#ethereum developers #hire offshore ethereum developer #hire blockchain ethereum developers #dedicated ethereum developers #hire ethereum developers #hire ethereum developers in india

1621339959

We have delivered excellent software development services for the last 15 years as an IT service provider. ValueCoders provides IT outsourcing services across the world. With their agile approach and proven methodologies, they enable digital transformation in existing businesses. Having expertise of 450+ professionals, ValueCoders have yielded optimal results. An ISO 9001:2008 certified company that has successfully delivered 4,200+ projects with 2,500 of happy customers from across the world. Being a blockchain development company, we have expertise in Ethereum wallet development and much more. Hire Ethereum developers from us and build customized wallets today!

Read Complete blog here:https://www.valuecoders.com/blog/technology-and-apps/top-10-ethereum-wallets-to-watch-out-for-in-2019/?utm_source=ethereum&utm_medium=av189&utm_campaign=web

#hire ethereum developers #hire ethereum developer #ethereum development team #ethereum developers #ethereum app development company #ethereum app development